System

Abstract

A system is provided.SOLUTION: A system comprising: a user terminal for inputting an accident situation; a server for receiving accident situation data input from the user terminal; an artificial intelligence module for analyzing the data received by the server and asking a user about necessary additional information; means for searching past similar precedents based on information obtained by the artificial intelligence module; means for calculating a fault ratio based on the similar precedents and presenting it to the user; and means for guiding the user to a relevant legal support service.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] When a traffic accident occurs, it is difficult for the parties involved to quickly determine a reasonable degree of fault. Without specialized knowledge, it is difficult to understand the factors and legal precedents that affect fault, putting them at risk of receiving inappropriate compensation. Furthermore, differences in knowledge can put parties at a disadvantage when negotiating with insurance companies. A method is needed to resolve these issues and reach a fair and appropriate agreement. [Means for solving the problem]

[0005] The present invention provides a system that includes a user terminal for inputting accident details, a server that receives the data, an artificial intelligence module that performs analysis, a means for generating questions to obtain additional information, a means for searching similar past cases, a means for calculating the degree of fault, and a means for directing users to legal support services. This system allows users to understand a reasonable degree of fault without specialized knowledge and prevents unfair conditions. It also makes it easy to direct users to legal support services to obtain expert help if necessary.

[0006] "User terminal" refers to an electronic device used by a user to input information about a traffic accident, such as a mobile device or computer.

[0007] "Server" refers to a central computer system that receives and processes data sent from user terminals.

[0008] The "artificial intelligence module" is a program that analyzes accident situation data and generates questions to obtain the necessary information.

[0009] The "database" is an information storage system that accumulates past traffic accident precedents and keeps them in a searchable format.

[0010] "Fault ratio" is a number that indicates the proportion of responsibility that each party should bear in a traffic accident.

[0011] "Similar precedents" refer to cases of traffic accidents that occurred in the past that are similar to the current accident situation.

[0012] "Question generation means" refers to a function that uses an artificial intelligence module to create additional questions for the user to fill in missing information.

[0013] "Legal aid services" refers to professional agencies and law firms that provide legal advice and assistance in relation to road traffic accidents.

[0014] "Search means" refers to algorithms or programs for searching for similar past cases in the database.

[0015] A "user" is a party who uses the system to find out their share of fault in a traffic accident. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] MODE FOR CARRYING OUT THE INVENTION

[0038] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means. The specific operation of this system and the processing of its programs are described below.

[0039] System Overview

[0040] 1. Enter the accident details

[0041] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[0042] The device displays a dedicated form for entering accident details, and the user can enter the location, date and time of the accident, traffic light conditions, vehicle direction, speed, etc. in text or by drawing.

[0043] 2. Receipt and storage of data

[0044] The server receives the accident situation data sent from the user terminal, and this data is temporarily stored in the server's storage.

[0045] 3. Analysis of the accident situation

[0046] The server passes the received data to the artificial intelligence module.

[0047] The AI ​​module analyzes the input data and understands the circumstances of the accident, specifically extracting key factors such as collisions when turning right at intersections, traffic light conditions, and the speed of each vehicle.

[0048] 4. Missing Information Questions

[0049] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault.

[0050] If there is missing information, the artificial intelligence module generates additional questions (e.g., "What is the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0051] The server then sends this question back to the user terminal.

[0052] The user answers the additional questions presented and sends them to the server via the terminal.

[0053] 5. Search for similar cases

[0054] After the server has all the necessary data, it sends it to the artificial intelligence module.

[0055] The artificial intelligence module searches the database for past traffic accident cases and selects the case that best matches the entered accident circumstances.

[0056] 6. Calculation of Fault Percentage

[0057] The artificial intelligence module calculates a reasonable degree of fault based on selected legal precedents.

[0058] The calculation results and details of the legal precedents on which they are based are sent to the server.

[0059] 7. Presentation of the percentage of fault

[0060] The server generates a screen to present the calculated fault ratio and its basis to the user.

[0061] The server distributes this information to the user's terminal so that the user can view it.

[0062] 8. Guidance for legal aid services

[0063] If necessary, the server will display a link directing the user to legal aid services, where they can obtain further professional advice.

[0064] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[0065] Specific examples

[0066] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[0067] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[0068] 2. The server receives this and passes it to the artificial intelligence module.

[0069] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[0070] 4. Determine whether all necessary information is available and generate additional questions if missing, such as "What direction is the other vehicle heading?"

[0071] 5. The user answers the follow-up question with "north direction."

[0072] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[0073] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[0074] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[0075] 9. Users can click on a link to access legal aid services.

[0076] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[0077] The processing flow will be explained below.

[0078] Step 1:

[0079] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user then enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[0080] Step 2:

[0081] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[0082] Step 3:

[0083] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[0084] Step 4:

[0085] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0086] Step 5:

[0087] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[0088] Step 6:

[0089] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[0090] Step 7:

[0091] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[0092] Step 8:

[0093] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[0094] Step 9:

[0095] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[0096] Step 10:

[0097] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[0098] This process allows users to determine a reasonable share of fault, obtain a fair settlement, and take steps to obtain professional legal assistance if necessary.

[0099] Example 1

[0100] 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."

[0101] When a traffic accident occurs, those involved are required to quickly and accurately determine the degree of fault, but doing so efficiently is not easy. Conventional methods require the manual collection and analysis of relevant information, which is time-consuming and labor-intensive. Furthermore, without thorough knowledge of the law and past legal precedents, it is difficult to respond appropriately. As a result, it often takes a long time for victims to receive appropriate legal support. The present invention aims to solve these problems and realize the rapid and accurate calculation of the degree of fault and the provision of legal support when a traffic accident occurs.

[0102] 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.

[0103] In this invention, the server includes a user terminal for inputting the accident situation, an information processing device that receives the accident situation data input from the user terminal, an artificial intelligence module that analyzes the data received by the information processing device and asks the user for any additional information needed, a means for searching for similar past cases based on the information obtained by the artificial intelligence module, a means for calculating the fault ratio based on the similar cases and presenting it to the user, and a means for directing the user to relevant legal support services. This enables the system to not only quickly and accurately calculate the fault ratio when a traffic accident occurs, but also enables the user to quickly receive the necessary legal support.

[0104] "Accident situation" refers to a series of information that constitutes the details of the accident, such as the location of the traffic accident, the date and time, the traffic light conditions, and the direction and speed of each vehicle.

[0105] A "user terminal" is an information processing device used by a user to input information about traffic accidents, and specifically includes devices such as smartphones, tablets, and personal computers.

[0106] The term "information processing device" refers to a computer system that receives, analyzes, and saves data acquired from a user terminal.

[0107] An "artificial intelligence module" is a software or hardware component that has the ability to analyze input accident situation data, identify missing information, and generate additional questions as necessary.

[0108] "Similar cases" are data extracted from legal precedents and examples of traffic accidents that have occurred in the past that most closely resemble the current accident situation.

[0109] "Fault ratio" indicates the percentage of fault of each party involved in a traffic accident, and is an important indicator for determining liability for compensation.

[0110] "Legal aid services" are professional organisations or services that provide expert advice and support on legal issues relating to road traffic accidents.

[0111] The present invention relates to a system for quickly and accurately calculating the degree of fault in a traffic accident and providing legal support to users. Specific embodiments of the system are described below.

[0112] System configuration

[0113] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means.

[0114] The user terminal is an information processing device such as a smartphone, tablet, or PC, and displays a dedicated form for the user to enter information about the traffic accident.

[0115] Specific operation of the system

[0116] Enter the accident details

[0117] When a traffic accident occurs, the user accesses the system from their own terminal.

[0118] The terminal displays a dedicated form for entering accident details, and the user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form.

[0119] Receiving and storing data

[0120] The user completes the input and presses the send button.

[0121] The terminal transmits the transmitted data to the server.

[0122] The server temporarily stores the received data in storage.

[0123] Analysis of the accident situation

[0124] The server passes the temporarily stored data to an artificial intelligence (AI) module.

[0125] The AI ​​module analyzes the received data and extracts important information related to the accident, such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light status, and the speed of each vehicle.

[0126] Missing Information Question

[0127] Based on the analysis results, the artificial intelligence module determines whether additional information is needed to accurately calculate the percentage of fault.

[0128] If there is missing information, the AI ​​module automatically generates additional questions, such as "What direction is the other vehicle heading?"

[0129] The server transmits the generated follow-up question to the user terminal.

[0130] The user answers the additional questions and sends them to the server via the terminal.

[0131] Search for similar cases

[0132] The server confirms that all necessary data is available and sends the data back to the artificial intelligence module.

[0133] The artificial intelligence module searches a database of past traffic accident cases and extracts the case that best matches the input accident situation.

[0134] Calculation of fault ratio

[0135] The AI ​​module calculates the percentage of fault based on selected legal precedents, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight."

[0136] The artificial intelligence module sends the results of this calculation and details of the legal precedents on which it is based to the server.

[0137] Presentation of the percentage of fault

[0138] The server generates a screen to present the received calculation results to the user.

[0139] The screen generated by the server is sent to the user's terminal, allowing the user to check the calculated fault ratio and its basis.

[0140] Guidance for legal aid services

[0141] The server displays a link on the screen that directs the user to legal assistance services if necessary.

[0142] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[0143] Specific examples

[0144] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[0145] 1. The user enters information such as the location of the accident, the traffic light conditions, and the speed of the other vehicle into a dedicated form on the device ("Collision occurred while turning right at an intersection," "The traffic light was green," "The other vehicle's speed was 60 km / h").

[0146] 2. The terminal sends the entered data to the server.

[0147] 3. The server receives this data and stores it temporarily in storage.

[0148] 4. The server passes the data to an artificial intelligence module, which analyzes the circumstances of the accident and extracts important keywords such as "turn right," "go straight," and "green light."

[0149] 5. Based on the analysis results, the artificial intelligence module determines what information is missing and automatically generates additional questions, such as, "What direction is the other vehicle heading?"

[0150] 6. The server sends an additional question to the user's terminal, and the user answers "north."

[0151] 7. The server receives the answer and sends it back to the artificial intelligence module, which searches for similar precedents.

[0152] 8. The artificial intelligence module calculates the percentage of fault based on precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[0153] 9. The server generates a screen that presents the calculation results in an easy-to-view format for the user and sends it to the user's terminal.

[0154] 10. If necessary, the server will display a link to a legal assistance service on the screen, allowing the user to receive professional assistance through that link.

[0155] Example prompt sentence:

[0156] "You collide with a vehicle going straight while turning right at an intersection. The light is green and the other vehicle is traveling at 60km / h. Please calculate the percentage of fault in this situation."

[0157] In this way, the invention helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[0158] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0159] Step 1:

[0160] When a traffic accident occurs, the user accesses the system from their own terminal. The terminal displays a dedicated form for entering the accident details. The user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form and submits the input data. This data is then sent to the server.

[0161] input:

[0162] Accident details (location, date and time, traffic signal conditions, vehicle direction, speed, etc.)

[0163] output:

[0164] Data sent to the server

[0165] Specific behavior:

[0166] The user fills out the form and presses the submit button.

[0167] Step 2:

[0168] The server receives the accident situation data sent from the user terminal and temporarily stores it in storage, which is used for subsequent processing.

[0169] input:

[0170] Accident situation data sent from the user device

[0171] output:

[0172] Data stored in storage

[0173] Specific behavior:

[0174] The server receives the data and temporarily stores it in a database.

[0175] Step 3:

[0176] The server passes the temporarily stored data to an artificial intelligence (AI) module, which analyzes the received data and extracts important information about the accident (such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light conditions, and the speed of each vehicle).

[0177] input:

[0178] Temporarily saved accident situation data

[0179] output:

[0180] Analysis results (important information extracted)

[0181] Specific behavior:

[0182] The server sends the data to the AI ​​module, which analyzes the information and extracts important keywords.

[0183] Step 4:

[0184] Based on the analysis results, the AI ​​module determines whether additional information necessary to accurately calculate the percentage of fault is missing. If so, the AI ​​module automatically generates additional questions and passes them to the server.

[0185] input:

[0186] Analysis results

[0187] output:

[0188] Additional questions (if necessary)

[0189] Specific behavior:

[0190] The AI ​​module identifies missing information and generates follow-up questions if necessary.

[0191] Step 5:

[0192] The server transmits the generated follow-up question to the user terminal, and the user answers the follow-up question and transmits the answer to the server via the terminal.

[0193] input:

[0194] Additional questions

[0195] output:

[0196] User Answers

[0197] Specific behavior:

[0198] The server sends a question, and the user enters and sends the answer.

[0199] Step 6:

[0200] The server receives additional responses from the user and sends them back to the AI ​​module, which searches a database of past traffic accident cases and extracts the case that best matches the entered accident situation.

[0201] input:

[0202] Additional user answers

[0203] output:

[0204] Similar precedents

[0205] Specific behavior:

[0206] The server sends the additional answers to the AI ​​module, which then searches the database to extract similar cases.

[0207] Step 7:

[0208] The AI ​​module calculates the percentage of fault based on the selected legal precedents, and sends the calculation results and details of the legal precedents on which they are based to the server.

[0209] input:

[0210] Similar precedents

[0211] output:

[0212] Calculation results and basis of fault ratio

[0213] Specific behavior:

[0214] The AI ​​module calculates the degree of fault based on legal precedent and sends the results to the server.

[0215] Step 8:

[0216] The server generates a screen to display the received calculation results to the user, and sends it to the user's terminal. The user can check the calculated fault ratio and its basis.

[0217] input:

[0218] Calculation results and basis of fault ratio

[0219] output:

[0220] User presentation screen

[0221] Specific behavior:

[0222] The server generates a screen and sends it to the user's terminal to present the information.

[0223] Step 9:

[0224] The server displays a link on the screen to direct the user to the legal aid service as needed, and when the user clicks the link, the user can access the reservation page or inquiry page of the legal aid service.

[0225] input:

[0226] User presentation screen

[0227] output:

[0228] Links to legal aid services

[0229] Specific behavior:

[0230] The server displays a link that the user clicks to access the legal assistance service.

[0231] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[0232] (Application example 1)

[0233] 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."

[0234] Traffic accidents involving autonomous vehicles generate unique data (camera footage, LiDAR data, etc.) that differs from those generated by human drivers, making them difficult to handle with conventional accident analysis systems. Furthermore, there is a need for rapid and accurate calculations of fault and legal support after accidents, but current systems require a lot of manual input and analysis, which is time-consuming and can lack accuracy. Therefore, there is a need for a system that can effectively incorporate data specific to autonomous vehicles, quickly and accurately calculate fault, and provide appropriate legal support to users.

[0235] 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.

[0236] In this invention, the server includes means for automatically acquiring sensor data from the autonomous vehicle as accident situation data, means for presenting accident analysis results on a head-mounted display, a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for any additional information required, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the degree of fault based on the similar legal precedents and presenting it to the user, and means for directing the user to relevant legal support services. This enables rapid and accurate analysis of autonomous vehicle accidents, calculation of the degree of fault, and provision of legal support services.

[0237] "Accident situation data" refers to information including the location, date and time, traffic light conditions, vehicle direction and speed, and related sensor data when a traffic accident occurs.

[0238] A "user terminal" is an electronic device, such as a smartphone, tablet, or PC, through which a user inputs data.

[0239] The "server" is a computer system that receives, analyzes, and stores accident situation data sent from user terminals.

[0240] An "artificial intelligence module" is software that analyzes received data, identifies any additional information needed, and performs advanced processing such as calculating fault percentages.

[0241] A "head-mounted display" is a device that a user wears on their head and displays information on a display.

[0242] "Sensor data" is digital information acquired by sensors (e.g., cameras, LiDAR, etc.) installed in autonomous vehicles.

[0243] "Similar precedents" refer to precedents of traffic accidents that occurred in the past and are similar to the current accident situation.

[0244] "Additional questions" are questions generated by the artificial intelligence module to supplement missing parts of the accident situation data.

[0245] "Legal aid service" means a professional organisation or service that provides legal advice and assistance in relation to road traffic accidents.

[0246] This invention is a system for analyzing traffic accident situations involving autonomous vehicles and accurately calculating the degree of fault. The system works by having the user input the accident situation using a smartphone or head-mounted display (HMD), and the server receives the data and analyzes it using an artificial intelligence module.

[0247] First, the user uses a device to input accident situation data. The input data includes sensor data acquired from the autonomous vehicle, such as camera footage and LiDAR data. This makes it possible to grasp the details of the accident situation, which was difficult to do using conventional methods.

[0248] The server then receives and temporarily stores the accident situation data sent from the user's device. The received data is then passed to an AI module for analysis. The AI ​​module extracts key elements from the data analysis process (e.g., collisions during right turns, traffic light conditions, and the speed of each vehicle).

[0249] If additional information is needed based on the analysis results, the AI ​​module will generate additional questions and send them to the server, which will then display them on the user's device and prompt the user to answer them. This process will allow for a complete understanding of the accident situation.

[0250] Once all the necessary data is collected, the server uses an artificial intelligence module to access the data in the database to search for similar past cases. Once a match is found, the fault ratio is calculated based on that case and the result is presented to the user by the server.

[0251] Finally, the system will display links to direct users to relevant legal aid services, allowing them to quickly receive professional legal assistance when needed.

[0252] During this process, the user can visually check the accident analysis results in real time using an HMD, which allows them to more intuitively understand the accident situation and the degree of fault.

[0253] Specific examples

[0254] For example, consider the case where an autonomous vehicle collides with a vehicle traveling straight while turning right at an intersection. The user inputs and transmits the accident details, such as "Intersection A," "2023-10-01 14:30," "Light is green," "Right-turn speed 15 km / h," and "Straight-moving vehicle speed 60 km / h." The server receives this data and passes it to an artificial intelligence module. After analysis, the missing information, "the direction of the other vehicle," is presented, and the user answers "northbound." The server then searches for similar cases and calculates the fault ratio: "70% for the right-turning vehicle, 30% for the straight-moving vehicle," which is presented to the user. After viewing the results, the user can easily access legal support services by clicking the displayed link.

[0255] Prompt Sentence Examples

[0256] "My self-driving vehicle collided with another vehicle at intersection A. Please analyze the following accident information and tell me the percentage of fault.

[0257] Location: Intersection A

[0258] Time: 2023-10-01 14:30

[0259] Signal status: Green

[0260] Vehicle direction: Turn right, go straight

[0261] Vehicle speed: Right-turning vehicles 15km / h, straight-going vehicles 60km / h

[0262] Sensor Data:

[0263] Camera footage: Image data

[0264] LiDAR: LiDAR data

[0265] In this way, it becomes possible to quickly and accurately analyze accidents involving autonomous vehicles and calculate the degree of fault.

[0266] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0267] Step 1:

[0268] The user inputs accident situation data using a terminal. The input data includes the location of the accident, date and time, traffic light conditions, vehicle direction, speed, and sensor data from the autonomous vehicle. The input data is then sent from the terminal to the server.

[0269] Step 2:

[0270] The server receives the accident situation data sent from the user terminal and temporarily stores it. The server then passes the received data to the artificial intelligence module. At this point, the input is the accident situation data entered by the user, and the output is the temporarily stored data.

[0271] Step 3:

[0272] The AI ​​module analyzes the received accident situation data and extracts important elements (e.g., "collision while turning right," "green light," "right turn speed 15 km / h," "straight vehicle speed 60 km / h," etc.). This extraction process organizes the data and identifies important information. The input is the received data, and the output is the analysis results, which are important elements.

[0273] Step 4:

[0274] Based on the analysis results, the AI ​​module determines whether additional information is needed to accurately calculate the degree of fault. If any information is missing, it generates additional questions. The generated questions are sent to the server. The input is the analysis results, and the output is the additional questions.

[0275] Step 5:

[0276] The server sends the generated follow-up question to the user terminal. The user terminal displays the question to the user, and the user inputs an answer to the follow-up question. The input is the follow-up question, and the output is the user's follow-up answer.

[0277] Step 6:

[0278] The server receives the user's additional answer and resends it to the AI ​​module. The AI ​​module analyzes the additional answer and searches the database for similar past cases. The input is the user's additional answer, and the output is the searched similar cases.

[0279] Step 7:

[0280] The AI ​​module calculates a reasonable percentage of fault based on the searched similar cases. The calculation results are sent to the server. The input is the similar cases, and the output is the calculated percentage of fault.

[0281] Step 8:

[0282] The server generates a screen to present the calculated fault ratio and its basis to the user. The generated screen is sent to the user's terminal so that the user can check it. The input is the calculated fault ratio, and the output is the generated presentation screen.

[0283] Step 9:

[0284] The server displays a link to direct the user to a legal aid service as needed. When the user clicks on this link, they can access the legal aid service's reservation page or inquiry page. The input is the legal aid service URL, and the output is a screen displaying the link.

[0285] In this way, by linking the server, terminal, and user, it is possible to analyze the accident situation and calculate the degree of fault quickly and accurately, and provide appropriate legal support.

[0286] 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.

[0287] MODE FOR CARRYING OUT THE INVENTION

[0288] The present invention is composed of a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, a legal support service guidance means, and an emotion engine. The specific operation form of this system and the processing of its programs are described below.

[0289] System Overview

[0290] 1. Enter the accident details

[0291] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[0292] The device displays a dedicated form for entering accident details, and the user can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[0293] 2. Receipt and storage of data

[0294] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[0295] 3. Analysis of the accident situation

[0296] The server passes the stored data to the artificial intelligence module.

[0297] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light is green."

[0298] 4. Missing Information Questions

[0299] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0300] The server displays the generated question on the user's device. The user answers the question and sends the information to the server via the device. For example, the user might answer, "The speed of the other vehicle is 60 km / h."

[0301] 5. Operation of the Emotion Engine

[0302] The emotion engine analyzes the user's emotional state from their responses and voice input, assessing, for example, their stress or anxiety levels and providing information on relaxation techniques or psychological support as needed.

[0303] The server displays additional support information to the user based on instructions from the emotion engine.

[0304] 6. Search for similar cases

[0305] After the server has all the necessary data, it sends it to the artificial intelligence module.

[0306] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[0307] 7. Calculation of Fault Percentage

[0308] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[0309] 8. Presentation of the percentage of fault

[0310] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The server then distributes this information to the user's device.

[0311] 9. Guidance for legal aid services

[0312] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[0313] Specific examples

[0314] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[0315] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[0316] 2. The server receives this and passes it to the artificial intelligence module.

[0317] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[0318] 4. Determine whether all necessary information is available and generate follow-up questions if missing (e.g., "What direction is the other vehicle heading?").

[0319] 5. The user answers the follow-up question with "north direction."

[0320] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[0321] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[0322] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[0323] 9. Users can click on a link to access legal aid services.

[0324] Through this process, users can determine the reasonable degree of fault and, if necessary, receive support from the emotion engine. Furthermore, if further professional assistance is required, users will be efficiently directed to the appropriate service, ensuring a fair and appropriate solution.

[0325] The processing flow will be explained below.

[0326] Step 1:

[0327] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[0328] Step 2:

[0329] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[0330] Step 3:

[0331] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[0332] Step 4:

[0333] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0334] Step 5:

[0335] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[0336] Step 6:

[0337] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[0338] Step 7:

[0339] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[0340] Step 8:

[0341] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[0342] Step 9:

[0343] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[0344] Step 10:

[0345] The emotion engine analyzes the user's input data and voice to identify their emotional state, for example determining their stress or anxiety level through voice analysis.

[0346] Step 11:

[0347] Based on the emotional state obtained from the emotion engine, the server provides additional information on relaxation techniques and psychological support if the user is experiencing high levels of stress or anxiety.

[0348] Step 12:

[0349] If necessary, the server may display a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. When the user clicks on the link, they are taken to a booking or enquiry page for the legal aid service.

[0350] Example 2

[0351] 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."

[0352] Conventional traffic accident processing systems require complicated input and analysis of accident circumstances, making it difficult for users to calculate the degree of fault and access the legal support they need. Furthermore, they do not adequately consider the emotional state of the parties involved in the accident, and psychological support is rarely provided. This makes it difficult for users to understand the appropriate degree of fault and quickly obtain the necessary legal support, and also creates a significant mental burden after an accident.

[0353] 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.

[0354] In this invention, the server includes a means for analyzing the user's emotional state and providing information on psychological support as needed, a means for directing the user to relevant legal support services, and a means for passing the input accident situation data to an artificial intelligence module for analysis, thereby enabling the user to accurately determine the degree of fault in the accident and receive professional legal support, as well as psychological support according to the user's emotional state.

[0355] "User terminal" refers to an electronic device used by a user to input the details of a traffic accident, including devices such as smartphones, tablets, and personal computers.

[0356] "Server" refers to a central processing unit that receives, stores, and analyzes accident situation data sent from user terminals.

[0357] "Artificial intelligence module" refers to a software component that analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault.

[0358] "Means for searching for similar cases" refers to the function in which the artificial intelligence module searches for past traffic accident cases in the database based on the input accident situation data and identifies similar cases.

[0359] "Means for calculating the degree of fault" refers to the function in which the artificial intelligence module calculates a reasonable degree of fault based on similar precedents and returns the results to the server.

[0360] "Legal aid link" refers to a feature that provides links or information to help users access appropriate legal aid services.

[0361] "Means for analyzing emotional state" refers to a function that analyzes emotions such as stress and anxiety from the user's responses and voice input, and provides information on psychological support based on the results.

[0362] MODE FOR CARRYING OUT THE INVENTION

[0363] The present invention relates to a system for understanding the circumstances of a traffic accident and calculating the degree of fault, and is a system that includes a user terminal, a server, an artificial intelligence module, a database, an emotion analysis engine, a search means, a means for calculating the degree of fault, and a means for guiding legal support services.

[0364] System configuration

[0365] This system is configured as follows:

[0366] 1. User Device:

[0367] An electronic device used by users to input the circumstances of a traffic accident. This includes devices such as smartphones, tablets, and PCs. Users use these devices to access the system and enter details of the accident.

[0368] 2. Server:

[0369] This is a central processing unit that receives, stores, and analyzes accident situation data sent from user devices. The server temporarily stores the received data and passes it to an artificial intelligence module or emotion analysis engine as needed.

[0370] 3. Artificial Intelligence Module:

[0371] This software component analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault. Specifically, it extracts accident keywords, searches for related information, and generates additional questions if any information is missing.

[0372] 4. Database:

[0373] This is a database for storing past traffic accident precedents and related information, and is used by the artificial intelligence module to calculate the degree of fault.

[0374] 5. Sentiment Analysis Engine:

[0375] The engine analyzes the user's emotional state based on their answers and voice input, and provides information on psychological support based on the results. For example, it assesses stress and anxiety levels and provides relaxation techniques and psychological support.

[0376] 6. Legal Aid Service Guidance:

[0377] It is a means of providing links and information to help users access appropriate legal aid services, so that they can quickly get the legal advice they need.

[0378] Operational Overview

[0379] When a user is involved in a traffic accident, they use their device to input details of the accident (such as the location, date and time of the accident, and traffic light conditions). This data is sent to the server and temporarily stored. The server then passes this data to an artificial intelligence module, which analyzes the data and extracts key elements. Based on the analysis results, if any information is missing, additional questions are generated and sent to the user.

[0380] Furthermore, an emotion analysis engine analyzes the user's emotional state and provides psychological support information as needed. After all data is collected, an artificial intelligence module searches the database for past traffic accident cases and selects the most similar case that best matches. It then calculates the reasonable degree of fault based on the similar case and presents the results to the user. Links to legal support services are also provided as needed.

[0381] Specific examples

[0382] For example, if a user collides with a vehicle going straight while turning right at an intersection, the user inputs the accident details as follows: "Collision occurred while turning right at the intersection," "The light was green," "The other vehicle's speed was 60 km / h," etc. This data is received by the server and passed to an artificial intelligence module. The module analyzes the data, extracts keywords such as "right turn," "going straight," and "green light," and determines whether the necessary information is available.

[0383] If there is insufficient information, the system generates an additional question, such as "What direction was the other vehicle traveling?" If the user answers "north," the information is sent back to the server, and the AI ​​module searches the database for similar cases and calculates the percentage of fault, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The results are then presented to the user, along with a link to legal support services.

[0384] Prompt Sentence Examples

[0385] You can check the system's processing by entering the following prompt sentence into the generative AI model:

[0386] example:

[0387] Please calculate the percentage of fault in an accident where a vehicle collided with a vehicle going straight while turning right at an intersection. The accident circumstances were as follows: the vehicle collided while turning right at an intersection, the traffic light was green, the other vehicle was traveling at 60 km / h, and the other vehicle was heading north.

[0388] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0389] Step 1: Enter the accident details

[0390] When a traffic accident occurs, the user accesses the system using their own device (smartphone, tablet, PC, etc.). The device displays a dedicated form for entering the details of the accident. The user enters the location of the accident, date and time, traffic light conditions, vehicle direction of travel, speed, etc. in text or diagrams. Input data such as "XX intersection, Chiyoda-ku, Tokyo," "March 15, 2023, 2:30 p.m.," "traffic light is green," "vehicle direction of travel is west," and "speed is 30 km / h" is sent to the server. Specific operations of the device include displaying the input form and sending the data.

[0391] Step 2: Receiving and storing data

[0392] The server receives the accident situation data sent from the terminal. The received data is temporarily saved in text or image format, and is then prepared to proceed to the next step. Examples of saved data include "collision while turning right at an intersection," "the traffic light is green," and "the other vehicle's speed is 60 km / h." The specific operations of the server include data reception processing and data storage processing in a database.

[0393] Step 3: Analysis of the accident situation

[0394] The server passes the stored data to an artificial intelligence module. The artificial intelligence module analyzes the input data and understands the accident situation. Specifically, it extracts key elements related to the accident. For example, it extracts keywords such as "intersection," "right turn," "straight ahead," "green light," and "60 km / h." The input data in this process is the stored accident situation data, and the output is the extracted keywords. Specific operations of the server include data transfer processing.

[0395] Step 4: Missing information question generation

[0396] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault. If any information is missing, it generates an additional question (e.g., "What direction is the other vehicle traveling?") and passes it to the server. The server displays the generated question on the user's device. The user answers this and sends the information back to the server via the device. For example, the user answers, "The other vehicle is traveling north." The input data is the analysis results, and a question about the missing information is output. The specific operations of the device include displaying the question and sending the answer.

[0397] Step 5: Emotion Engine in Action

[0398] The emotion analysis engine analyzes the user's emotional state from their responses and voice input. For example, it evaluates their state as "stressed," "anxious," or "relaxed." If necessary, it provides information on relaxation techniques or psychological support. The server follows the instructions of the emotion analysis engine and displays additional support information on the user's device. The specific operations of the emotion engine include analyzing the emotional state and generating support information. The specific operations of the server include displaying support information based on the results of the emotion analysis.

[0399] Step 6: Search for similar cases

[0400] After the server has gathered all the necessary data, it sends it to the AI ​​module, which searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data. The input data is the complete accident situation data, and the output is a list of similar cases. The specific operations of the server include sending data and receiving results.

[0401] Step 7: Calculate the percentage of fault

[0402] The AI ​​module analyzes the details of similar cases and calculates the most reasonable fault ratio from them. For example, a result such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight" is sent to the server. The input data is the details of similar cases, and the output is the calculated fault ratio. The specific operations of the server include receiving the calculation results and saving the data.

[0403] Step 8: State the percentage of fault

[0404] The server generates a screen to present the calculated fault percentage and detailed reasons to the user. The presented information will be information such as "70% fault for vehicles turning right, 30% for vehicles going straight" and will be distributed to the user's terminal. The input data is the fault percentage calculation result, and the output is the screen displayed to the user. The specific operations of the server include generating the screen and distributing the data.

[0405] Step 9: Referral to legal aid services

[0406] If necessary, the server also displays a link on the page that directs the user to a legal aid service, allowing the user to obtain further professional advice. When the user clicks on the link, they can access the legal aid service's reservation or inquiry page. The input data is the user's request for assistance, and the output is the link information for the legal aid service. The specific operations of the server include generating the link and directing the user to the page.

[0407] (Application example 2)

[0408] 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."

[0409] When a traffic accident occurs, especially in an autonomous vehicle, it is necessary to analyze the accident situation quickly and accurately and calculate the degree of fault. However, conventional systems rely on human input, which can lead to delays in inputting the accident situation or analysis being based on incomplete information, making it difficult to respond quickly. Another issue is the lack of information and advice necessary to receive legal support.

[0410] 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 a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for necessary additional information, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the fault ratio based on the similar legal precedents and presenting it to the user, means for directing the user to relevant legal support services, means for automatically acquiring accident data of autonomous vehicles and calculating the fault ratio based on the data, and means for providing legal advice related to the autonomous vehicle accident. This enables quick and accurate analysis of the accident situation and calculation of the fault ratio, and enables necessary legal support to be provided promptly.

[0411] A "user terminal for inputting accident details" is a device that allows a user to input details of a traffic accident when one occurs.

[0412] "Server" refers to a central computer system that receives, analyzes, and processes accident situation data sent from user terminals.

[0413] The "artificial intelligence module" is a program built into the server that analyzes the received data, identifies any additional information needed, and presents it to the user.

[0414] The "means of searching for similar past cases" is a function that searches a database of past traffic accident cases to find cases similar to the current accident situation.

[0415] The "means for calculating the degree of fault" is a module that calculates the degree of responsibility in the current accident based on similar precedents that have been searched.

[0416] "Means for presenting to the user" refers to an interface that displays the calculated fault ratio and necessary information to the user.

[0417] "Legal assistance means" means a feature that provides links or information that directs users to professional services for legal advice or assistance.

[0418] "Means for automatically acquiring accident data from autonomous vehicles" refers to a device that automatically collects accident information from sensors and logging systems built into autonomous vehicles.

[0419] "Means for providing legal advice related to accidents involving autonomous vehicles" refers to a function that provides users with appropriate legal advice depending on the circumstances of an accident involving an autonomous vehicle.

[0420] The present invention is a system that includes a user terminal for inputting accident details, a server, an artificial intelligence module, a means for searching for similar past cases, a means for calculating the degree of fault, a means for directing to legal support services, a means for automatically acquiring accident data of autonomous vehicles, and a means for providing legal advice related to accidents involving autonomous vehicles.

[0421] System Overview

[0422] User Device

[0423] The user device provides an interface that allows users to manually input accident details. The device can be a smartphone, tablet, or PC, and a dedicated form for entering detailed accident information is displayed. Users can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[0424] server

[0425] The server receives and temporarily stores the accident situation data sent from the user terminal, and then passes the received data to the artificial intelligence module for analysis.

[0426] Artificial Intelligence Module

[0427] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key elements related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "green light." It also determines whether additional information is needed, and generates additional questions if there is insufficient information. Examples of missing information include "What was the speed of the other vehicle?" and "What is the damage to the vehicle?"

[0428] Search for similar cases and calculate the percentage of fault

[0429] Based on the information obtained by the AI ​​module, the server searches the database for past traffic accident cases. It selects the most similar cases and calculates the percentage of fault based on them. This percentage of fault and the case data showing the basis for it are presented to the user.

[0430] Legal Aid Services

[0431] The user is presented with a percentage of fault and detailed reasons for it, and if necessary, a link to a legal aid service is also provided, allowing the user to seek further professional advice.

[0432] Data acquisition from autonomous vehicles

[0433] Autonomous vehicles automatically collect accident situation data using internal sensors and logging systems and send it to a server, allowing detailed accident information to be transmitted to the server quickly and accurately.

[0434] Examples of prompt statements

[0435] Example of what the user enters as a prompt:

[0436] I collided with a vehicle going straight while turning right at an intersection. The light was green, and my vehicle was turning right while the other vehicle was going straight. The accident occurred at an intersection in Shibuya Ward, Tokyo. The other vehicle was traveling at a speed of approximately 60 km / h, damaging the front of my vehicle. I would like your help calculating the degree of fault so that I can receive appropriate legal assistance.

[0437] By combining these elements, users can quickly and accurately input accident details, and based on the results, appropriate fault allocation and legal support information can be provided. In addition, data acquisition from autonomous vehicles can speed up accident analysis and legal support for autonomous driving technology.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] The user terminal inputs the accident situation data.

[0441] Input: location of accident, date and time, traffic light status, vehicle direction, speed, etc.

[0442] Data manipulation: converting data into text or graphical formats

[0443] Output: Accident situation data is entered into the user's terminal.

[0444] Specific operation: The user inputs the necessary information into a dedicated form using a smartphone or tablet. Example: "While turning right at an intersection in Shibuya Ward, Tokyo, a collision occurred with a vehicle going straight. The traffic light was green and the speed of the other vehicle was approximately 60 km / h."

[0445] Step 2:

[0446] The user terminal transmits the entered accident situation data to the server.

[0447] Input: Accident situation data entered into the user's terminal

[0448] Data processing: Convert data into JSON format

[0449] Output: Accident status data sent to the server

[0450] What it does: When a user presses a button on their device, data is sent to the server via an HTTP request, containing detailed information about the accident.

[0451] Step 3:

[0452] The server analyzes the received data and passes it to the artificial intelligence module.

[0453] Input: Accident situation data sent to the server

[0454] Data processing: Analyze the data and extract the main factors of the accident (e.g., "turning right at an intersection," "collision with a vehicle going straight," "green light")

[0455] Output: Analysis results passed to the AI ​​module

[0456] Specific operation: The data received by the server is analyzed using an internal analysis program (such as a Python script) to sort out the factors that caused the accident.

[0457] Step 4:

[0458] The artificial intelligence module determines what additional information is needed and generates follow-up questions for the user.

[0459] Input: Analysis results passed from the server

[0460] Data manipulation: Identifying missing information and generating follow-up questions

[0461] Output: Data with additional questions

[0462] How it works: The AI ​​module reviews the accident situation data, determines missing information, and generates additional questions, such as "What direction is the other vehicle heading?"

[0463] Step 5:

[0464] The server distributes the generated follow-up questions to the user terminal, and the user inputs the answers.

[0465] Input: Additional question data

[0466] Data processing: Display additional questions on the user's device interface

[0467] Output: Data on user answers to follow-up questions

[0468] Specific operation: The server sends a follow-up question to the user's device, and the user answers it. Example: "What direction is the other vehicle heading?" "Northbound"

[0469] Step 6:

[0470] The user terminal sends the answer to the additional question to the server.

[0471] Input: User's answer to follow-up question

[0472] Data processing: Converting answers to questions into JSON format

[0473] Output: The answer to the follow-up question has been sent to the server

[0474] What happens: The user answers additional questions and sends the data to the server.

[0475] Step 7:

[0476] The server gathers all the necessary data and passes it back to the artificial intelligence module.

[0477] Input: All accident situation data and answers to additional questions

[0478] Data processing: Re-analyzing data to understand the complete accident situation

[0479] Output: The AI ​​module has all the data.

[0480] Specific operation: The server collects all the data and passes it back to the AI ​​module, which performs the final analysis.

[0481] Step 8:

[0482] An artificial intelligence module searches for similar past cases and calculates the degree of fault.

[0483] Input: Complete accident situation data

[0484] Data processing: Search for similar cases in the database and calculate the percentage of fault

[0485] Output: Calculated fault percentage data

[0486] Specific operation: The artificial intelligence module uses a database search engine to identify similar cases and calculate the fault ratio, for example, "70% for the vehicle turning right, 30% for the vehicle going straight."

[0487] Step 9:

[0488] The server displays the calculated fault percentage and detailed reasons for it on the user's device.

[0489] Input: Calculated fault percentage data

[0490] Data processing: Converting data into a user-friendly format

[0491] Output: Fault percentage displayed on user device

[0492] Specific operation: The server sends the calculation results to the user's terminal and presents them to the user through the interface.

[0493] Step 10:

[0494] The server directs the user to legal assistance services if necessary.

[0495] Input: Calculated percentage of fault and legal assistance requirement

[0496] Data processing: generating links and details for legal aid services

[0497] Output: User device displays a link to a legal aid service

[0498] What it does: The server creates information about legal aid services and displays a link for the user to access them quickly.

[0499] 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.

[0500] 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.

[0501] 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.

[0502] [Second embodiment]

[0503] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0504] 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.

[0505] 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).

[0506] 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.

[0507] 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.

[0508] 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).

[0509] 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.

[0510] 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.

[0511] 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.

[0512] 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.

[0513] 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.

[0514] 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."

[0515] MODE FOR CARRYING OUT THE INVENTION

[0516] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means. The specific operation of this system and the processing of its programs are described below.

[0517] System Overview

[0518] 1. Enter the accident details

[0519] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[0520] The device displays a dedicated form for entering accident details, and the user can enter the location, date and time of the accident, traffic light conditions, vehicle direction, speed, etc. in text or by drawing.

[0521] 2. Receipt and storage of data

[0522] The server receives the accident situation data sent from the user terminal, and this data is temporarily stored in the server's storage.

[0523] 3. Analysis of the accident situation

[0524] The server passes the received data to the artificial intelligence module.

[0525] The AI ​​module analyzes the input data and understands the circumstances of the accident, specifically extracting key factors such as collisions when turning right at intersections, traffic light conditions, and the speed of each vehicle.

[0526] 4. Missing Information Questions

[0527] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault.

[0528] If there is missing information, the artificial intelligence module generates additional questions (e.g., "What is the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0529] The server then sends this question back to the user terminal.

[0530] The user answers the additional questions presented and sends them to the server via the terminal.

[0531] 5. Search for similar cases

[0532] After the server has all the necessary data, it sends it to the artificial intelligence module.

[0533] The artificial intelligence module searches the database for past traffic accident cases and selects the case that best matches the entered accident circumstances.

[0534] 6. Calculation of Fault Percentage

[0535] The artificial intelligence module calculates a reasonable degree of fault based on selected legal precedents.

[0536] The calculation results and details of the legal precedents on which they are based are sent to the server.

[0537] 7. Presentation of the percentage of fault

[0538] The server generates a screen to present the calculated fault ratio and its basis to the user.

[0539] The server distributes this information to the user's terminal so that the user can view it.

[0540] 8. Guidance for legal aid services

[0541] If necessary, the server will display a link directing the user to legal aid services, where they can obtain further professional advice.

[0542] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[0543] Specific examples

[0544] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[0545] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[0546] 2. The server receives this and passes it to the artificial intelligence module.

[0547] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[0548] 4. Determine whether all necessary information is available and generate additional questions if missing, such as "What direction is the other vehicle heading?"

[0549] 5. The user answers the follow-up question with "north direction."

[0550] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[0551] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[0552] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[0553] 9. Users can click on a link to access legal aid services.

[0554] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[0555] The processing flow will be explained below.

[0556] Step 1:

[0557] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user then enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[0558] Step 2:

[0559] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[0560] Step 3:

[0561] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[0562] Step 4:

[0563] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0564] Step 5:

[0565] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[0566] Step 6:

[0567] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[0568] Step 7:

[0569] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[0570] Step 8:

[0571] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[0572] Step 9:

[0573] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[0574] Step 10:

[0575] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[0576] This process allows users to determine a reasonable share of fault, obtain a fair settlement, and take steps to obtain professional legal assistance if necessary.

[0577] Example 1

[0578] 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."

[0579] When a traffic accident occurs, those involved are required to quickly and accurately determine the degree of fault, but doing so efficiently is not easy. Conventional methods require the manual collection and analysis of relevant information, which is time-consuming and labor-intensive. Furthermore, without thorough knowledge of the law and past legal precedents, it is difficult to respond appropriately. As a result, it often takes a long time for victims to receive appropriate legal support. The present invention aims to solve these problems and realize the rapid and accurate calculation of the degree of fault and the provision of legal support when a traffic accident occurs.

[0580] 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.

[0581] In this invention, the server includes a user terminal for inputting the accident situation, an information processing device that receives the accident situation data input from the user terminal, an artificial intelligence module that analyzes the data received by the information processing device and asks the user for any additional information needed, a means for searching for similar past cases based on the information obtained by the artificial intelligence module, a means for calculating the fault ratio based on the similar cases and presenting it to the user, and a means for directing the user to relevant legal support services. This enables the system to not only quickly and accurately calculate the fault ratio when a traffic accident occurs, but also enables the user to quickly receive the necessary legal support.

[0582] "Accident situation" refers to a series of information that constitutes the details of the accident, such as the location of the traffic accident, the date and time, the traffic light conditions, and the direction and speed of each vehicle.

[0583] A "user terminal" is an information processing device used by a user to input information about traffic accidents, and specifically includes devices such as smartphones, tablets, and personal computers.

[0584] The term "information processing device" refers to a computer system that receives, analyzes, and saves data acquired from a user terminal.

[0585] An "artificial intelligence module" is a software or hardware component that has the ability to analyze input accident situation data, identify missing information, and generate additional questions as necessary.

[0586] "Similar cases" are data extracted from legal precedents and examples of traffic accidents that have occurred in the past that most closely resemble the current accident situation.

[0587] "Fault ratio" indicates the percentage of fault of each party involved in a traffic accident, and is an important indicator for determining liability for compensation.

[0588] "Legal aid services" are professional organisations or services that provide expert advice and support on legal issues relating to road traffic accidents.

[0589] The present invention relates to a system for quickly and accurately calculating the degree of fault in a traffic accident and providing legal support to users. Specific embodiments of the system are described below.

[0590] System configuration

[0591] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means.

[0592] The user terminal is an information processing device such as a smartphone, tablet, or PC, and displays a dedicated form for the user to enter information about the traffic accident.

[0593] Specific operation of the system

[0594] Enter the accident details

[0595] When a traffic accident occurs, the user accesses the system from their own terminal.

[0596] The terminal displays a dedicated form for entering accident details, and the user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form.

[0597] Receiving and storing data

[0598] The user completes the input and presses the send button.

[0599] The terminal transmits the transmitted data to the server.

[0600] The server temporarily stores the received data in storage.

[0601] Analysis of the accident situation

[0602] The server passes the temporarily stored data to an artificial intelligence (AI) module.

[0603] The AI ​​module analyzes the received data and extracts important information related to the accident, such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light status, and the speed of each vehicle.

[0604] Missing Information Question

[0605] Based on the analysis results, the artificial intelligence module determines whether additional information is needed to accurately calculate the percentage of fault.

[0606] If there is missing information, the AI ​​module automatically generates additional questions, such as "What direction is the other vehicle heading?"

[0607] The server transmits the generated follow-up question to the user terminal.

[0608] The user answers the additional questions and sends them to the server via the terminal.

[0609] Search for similar cases

[0610] The server confirms that all necessary data is available and sends the data back to the artificial intelligence module.

[0611] The artificial intelligence module searches a database of past traffic accident cases and extracts the case that best matches the input accident situation.

[0612] Calculation of fault ratio

[0613] The AI ​​module calculates the percentage of fault based on selected legal precedents, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight."

[0614] The artificial intelligence module sends the results of this calculation and details of the legal precedents on which it is based to the server.

[0615] Presentation of the percentage of fault

[0616] The server generates a screen to present the received calculation results to the user.

[0617] The screen generated by the server is sent to the user's terminal, allowing the user to check the calculated fault ratio and its basis.

[0618] Guidance for legal aid services

[0619] The server displays a link on the screen that directs the user to legal assistance services if necessary.

[0620] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[0621] Specific examples

[0622] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[0623] 1. The user enters information such as the location of the accident, the traffic light conditions, and the speed of the other vehicle into a dedicated form on the device ("Collision occurred while turning right at an intersection," "The traffic light was green," "The other vehicle's speed was 60 km / h").

[0624] 2. The terminal sends the entered data to the server.

[0625] 3. The server receives this data and stores it temporarily in storage.

[0626] 4. The server passes the data to an artificial intelligence module, which analyzes the circumstances of the accident and extracts important keywords such as "turn right," "go straight," and "green light."

[0627] 5. Based on the analysis results, the artificial intelligence module determines what information is missing and automatically generates additional questions, such as, "What direction is the other vehicle heading?"

[0628] 6. The server sends an additional question to the user's terminal, and the user answers "north."

[0629] 7. The server receives the answer and sends it back to the artificial intelligence module, which searches for similar precedents.

[0630] 8. The artificial intelligence module calculates the percentage of fault based on precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[0631] 9. The server generates a screen that presents the calculation results in an easy-to-view format for the user and sends it to the user's terminal.

[0632] 10. If necessary, the server will display a link to a legal assistance service on the screen, allowing the user to receive professional assistance through that link.

[0633] Example prompt sentence:

[0634] "You collide with a vehicle going straight while turning right at an intersection. The light is green and the other vehicle is traveling at 60km / h. Please calculate the percentage of fault in this situation."

[0635] In this way, the invention helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[0636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] When a traffic accident occurs, the user accesses the system from their own terminal. The terminal displays a dedicated form for entering the accident details. The user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form and submits the input data. This data is then sent to the server.

[0639] input:

[0640] Accident details (location, date and time, traffic signal conditions, vehicle direction, speed, etc.)

[0641] output:

[0642] Data sent to the server

[0643] Specific behavior:

[0644] The user fills out the form and presses the submit button.

[0645] Step 2:

[0646] The server receives the accident situation data sent from the user terminal and temporarily stores it in storage, which is used for subsequent processing.

[0647] input:

[0648] Accident situation data sent from the user device

[0649] output:

[0650] Data stored in storage

[0651] Specific behavior:

[0652] The server receives the data and temporarily stores it in a database.

[0653] Step 3:

[0654] The server passes the temporarily stored data to an artificial intelligence (AI) module, which analyzes the received data and extracts important information about the accident (such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light conditions, and the speed of each vehicle).

[0655] input:

[0656] Temporarily saved accident situation data

[0657] output:

[0658] Analysis results (important information extracted)

[0659] Specific behavior:

[0660] The server sends the data to the AI ​​module, which analyzes the information and extracts important keywords.

[0661] Step 4:

[0662] Based on the analysis results, the AI ​​module determines whether additional information necessary to accurately calculate the percentage of fault is missing. If so, the AI ​​module automatically generates additional questions and passes them to the server.

[0663] input:

[0664] Analysis results

[0665] output:

[0666] Additional questions (if necessary)

[0667] Specific behavior:

[0668] The AI ​​module identifies missing information and generates follow-up questions if necessary.

[0669] Step 5:

[0670] The server transmits the generated follow-up question to the user terminal, and the user answers the follow-up question and transmits the answer to the server via the terminal.

[0671] input:

[0672] Additional questions

[0673] output:

[0674] User Answers

[0675] Specific behavior:

[0676] The server sends a question, and the user enters and sends the answer.

[0677] Step 6:

[0678] The server receives additional responses from the user and sends them back to the AI ​​module, which searches a database of past traffic accident cases and extracts the case that best matches the entered accident situation.

[0679] input:

[0680] Additional user answers

[0681] output:

[0682] Similar precedents

[0683] Specific behavior:

[0684] The server sends the additional answers to the AI ​​module, which then searches the database to extract similar cases.

[0685] Step 7:

[0686] The AI ​​module calculates the percentage of fault based on the selected legal precedents, and sends the calculation results and details of the legal precedents on which they are based to the server.

[0687] input:

[0688] Similar precedents

[0689] output:

[0690] Calculation results and basis of fault ratio

[0691] Specific behavior:

[0692] The AI ​​module calculates the degree of fault based on legal precedent and sends the results to the server.

[0693] Step 8:

[0694] The server generates a screen to display the received calculation results to the user, and sends it to the user's terminal. The user can check the calculated fault ratio and its basis.

[0695] input:

[0696] Calculation results and basis of fault ratio

[0697] output:

[0698] User presentation screen

[0699] Specific behavior:

[0700] The server generates a screen and sends it to the user's terminal to present the information.

[0701] Step 9:

[0702] The server displays a link on the screen to direct the user to the legal aid service as needed, and when the user clicks the link, the user can access the reservation page or inquiry page of the legal aid service.

[0703] input:

[0704] User presentation screen

[0705] output:

[0706] Links to legal aid services

[0707] Specific behavior:

[0708] The server displays a link that the user clicks to access the legal assistance service.

[0709] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[0710] (Application example 1)

[0711] 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."

[0712] Traffic accidents involving autonomous vehicles generate unique data (camera footage, LiDAR data, etc.) that differs from those generated by human drivers, making them difficult to handle with conventional accident analysis systems. Furthermore, there is a need for rapid and accurate calculations of fault and legal support after accidents, but current systems require a lot of manual input and analysis, which is time-consuming and can lack accuracy. Therefore, there is a need for a system that can effectively incorporate data specific to autonomous vehicles, quickly and accurately calculate fault, and provide appropriate legal support to users.

[0713] 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.

[0714] In this invention, the server includes means for automatically acquiring sensor data from the autonomous vehicle as accident situation data, means for presenting accident analysis results on a head-mounted display, a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for any additional information required, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the degree of fault based on the similar legal precedents and presenting it to the user, and means for directing the user to relevant legal support services. This enables rapid and accurate analysis of autonomous vehicle accidents, calculation of the degree of fault, and provision of legal support services.

[0715] "Accident situation data" refers to information including the location, date and time, traffic light conditions, vehicle direction and speed, and related sensor data when a traffic accident occurs.

[0716] A "user terminal" is an electronic device, such as a smartphone, tablet, or PC, through which a user inputs data.

[0717] The "server" is a computer system that receives, analyzes, and stores accident situation data sent from user terminals.

[0718] An "artificial intelligence module" is software that analyzes received data, identifies any additional information needed, and performs advanced processing such as calculating fault percentages.

[0719] A "head-mounted display" is a device that a user wears on their head and displays information on a display.

[0720] "Sensor data" is digital information acquired by sensors (e.g., cameras, LiDAR, etc.) installed in autonomous vehicles.

[0721] "Similar precedents" refer to precedents of traffic accidents that occurred in the past and are similar to the current accident situation.

[0722] "Additional questions" are questions generated by the artificial intelligence module to supplement missing parts of the accident situation data.

[0723] "Legal aid service" means a professional organisation or service that provides legal advice and assistance in relation to road traffic accidents.

[0724] This invention is a system for analyzing traffic accident situations involving autonomous vehicles and accurately calculating the degree of fault. The system works by having the user input the accident situation using a smartphone or head-mounted display (HMD), and the server receives the data and analyzes it using an artificial intelligence module.

[0725] First, the user uses a device to input accident situation data. The input data includes sensor data acquired from the autonomous vehicle, such as camera footage and LiDAR data. This makes it possible to grasp the details of the accident situation, which was difficult to do using conventional methods.

[0726] The server then receives and temporarily stores the accident situation data sent from the user's device. The received data is then passed to an AI module for analysis. The AI ​​module extracts key elements from the data analysis process (e.g., collisions during right turns, traffic light conditions, and the speed of each vehicle).

[0727] If additional information is needed based on the analysis results, the AI ​​module will generate additional questions and send them to the server, which will then display them on the user's device and prompt the user to answer them. This process will allow for a complete understanding of the accident situation.

[0728] Once all the necessary data is collected, the server uses an artificial intelligence module to access the data in the database to search for similar past cases. Once a match is found, the fault ratio is calculated based on that case and the result is presented to the user by the server.

[0729] Finally, the system will display links to direct users to relevant legal aid services, allowing them to quickly receive professional legal assistance when needed.

[0730] During this process, the user can visually check the accident analysis results in real time using an HMD, which allows them to more intuitively understand the accident situation and the degree of fault.

[0731] Specific examples

[0732] For example, consider the case where an autonomous vehicle collides with a vehicle traveling straight while turning right at an intersection. The user inputs and transmits the accident details, such as "Intersection A," "2023-10-01 14:30," "Light is green," "Right-turn speed 15 km / h," and "Straight-moving vehicle speed 60 km / h." The server receives this data and passes it to an artificial intelligence module. After analysis, the missing information, "the direction of the other vehicle," is presented, and the user answers "northbound." The server then searches for similar cases and calculates the fault ratio: "70% for the right-turning vehicle, 30% for the straight-moving vehicle," which is presented to the user. After viewing the results, the user can easily access legal support services by clicking the displayed link.

[0733] Prompt Sentence Examples

[0734] "My self-driving vehicle collided with another vehicle at intersection A. Please analyze the following accident information and tell me the percentage of fault.

[0735] Location: Intersection A

[0736] Time: 2023-10-01 14:30

[0737] Signal status: Green

[0738] Vehicle direction: Turn right, go straight

[0739] Vehicle speed: Right-turning vehicles 15km / h, straight-going vehicles 60km / h

[0740] Sensor Data:

[0741] Camera footage: Image data

[0742] LiDAR: LiDAR data

[0743] In this way, it becomes possible to quickly and accurately analyze accidents involving autonomous vehicles and calculate the degree of fault.

[0744] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0745] Step 1:

[0746] The user inputs accident situation data using a terminal. The input data includes the location of the accident, date and time, traffic light conditions, vehicle direction, speed, and sensor data from the autonomous vehicle. The input data is then sent from the terminal to the server.

[0747] Step 2:

[0748] The server receives the accident situation data sent from the user terminal and temporarily stores it. The server then passes the received data to the artificial intelligence module. At this point, the input is the accident situation data entered by the user, and the output is the temporarily stored data.

[0749] Step 3:

[0750] The AI ​​module analyzes the received accident situation data and extracts important elements (e.g., "collision while turning right," "green light," "right turn speed 15 km / h," "straight vehicle speed 60 km / h," etc.). This extraction process organizes the data and identifies important information. The input is the received data, and the output is the analysis results, which are important elements.

[0751] Step 4:

[0752] Based on the analysis results, the AI ​​module determines whether additional information is needed to accurately calculate the degree of fault. If any information is missing, it generates additional questions. The generated questions are sent to the server. The input is the analysis results, and the output is the additional questions.

[0753] Step 5:

[0754] The server sends the generated follow-up question to the user terminal. The user terminal displays the question to the user, and the user inputs an answer to the follow-up question. The input is the follow-up question, and the output is the user's follow-up answer.

[0755] Step 6:

[0756] The server receives the user's additional answer and resends it to the AI ​​module. The AI ​​module analyzes the additional answer and searches the database for similar past cases. The input is the user's additional answer, and the output is the searched similar cases.

[0757] Step 7:

[0758] The AI ​​module calculates a reasonable percentage of fault based on the searched similar cases. The calculation results are sent to the server. The input is the similar cases, and the output is the calculated percentage of fault.

[0759] Step 8:

[0760] The server generates a screen to present the calculated fault ratio and its basis to the user. The generated screen is sent to the user's terminal so that the user can check it. The input is the calculated fault ratio, and the output is the generated presentation screen.

[0761] Step 9:

[0762] The server displays a link to direct the user to a legal aid service as needed. When the user clicks on this link, they can access the legal aid service's reservation page or inquiry page. The input is the legal aid service URL, and the output is a screen displaying the link.

[0763] In this way, by linking the server, terminal, and user, it is possible to analyze the accident situation and calculate the degree of fault quickly and accurately, and provide appropriate legal support.

[0764] 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.

[0765] MODE FOR CARRYING OUT THE INVENTION

[0766] The present invention is composed of a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, a legal support service guidance means, and an emotion engine. The specific operation form of this system and the processing of its programs are described below.

[0767] System Overview

[0768] 1. Enter the accident details

[0769] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[0770] The device displays a dedicated form for entering accident details, and the user can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[0771] 2. Receipt and storage of data

[0772] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[0773] 3. Analysis of the accident situation

[0774] The server passes the stored data to the artificial intelligence module.

[0775] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light is green."

[0776] 4. Missing Information Questions

[0777] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0778] The server displays the generated question on the user's device. The user answers the question and sends the information to the server via the device. For example, the user might answer, "The speed of the other vehicle is 60 km / h."

[0779] 5. Operation of the Emotion Engine

[0780] The emotion engine analyzes the user's emotional state from their responses and voice input, assessing, for example, their stress or anxiety levels and providing information on relaxation techniques or psychological support as needed.

[0781] The server displays additional support information to the user based on instructions from the emotion engine.

[0782] 6. Search for similar cases

[0783] After the server has all the necessary data, it sends it to the artificial intelligence module.

[0784] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[0785] 7. Calculation of Fault Percentage

[0786] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[0787] 8. Presentation of the percentage of fault

[0788] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The server then distributes this information to the user's device.

[0789] 9. Guidance for legal aid services

[0790] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[0791] Specific examples

[0792] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[0793] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[0794] 2. The server receives this and passes it to the artificial intelligence module.

[0795] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[0796] 4. Determine whether all necessary information is available and generate follow-up questions if missing (e.g., "What direction is the other vehicle heading?").

[0797] 5. The user answers the follow-up question with "north direction."

[0798] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[0799] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[0800] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[0801] 9. Users can click on a link to access legal aid services.

[0802] Through this process, users can determine the reasonable degree of fault and, if necessary, receive support from the emotion engine. Furthermore, if further professional assistance is required, users will be efficiently directed to the appropriate service, ensuring a fair and appropriate solution.

[0803] The processing flow will be explained below.

[0804] Step 1:

[0805] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[0806] Step 2:

[0807] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[0808] Step 3:

[0809] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[0810] Step 4:

[0811] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[0812] Step 5:

[0813] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[0814] Step 6:

[0815] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[0816] Step 7:

[0817] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[0818] Step 8:

[0819] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[0820] Step 9:

[0821] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[0822] Step 10:

[0823] The emotion engine analyzes the user's input data and voice to identify their emotional state, for example determining their stress or anxiety level through voice analysis.

[0824] Step 11:

[0825] Based on the emotional state obtained from the emotion engine, the server provides additional information on relaxation techniques and psychological support if the user is experiencing high levels of stress or anxiety.

[0826] Step 12:

[0827] If necessary, the server may display a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. When the user clicks on the link, they are taken to a booking or enquiry page for the legal aid service.

[0828] Example 2

[0829] 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."

[0830] Conventional traffic accident processing systems require complicated input and analysis of accident circumstances, making it difficult for users to calculate the degree of fault and access the legal support they need. Furthermore, they do not adequately consider the emotional state of the parties involved in the accident, and psychological support is rarely provided. This makes it difficult for users to understand the appropriate degree of fault and quickly obtain the necessary legal support, and also creates a significant mental burden after an accident.

[0831] 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.

[0832] In this invention, the server includes a means for analyzing the user's emotional state and providing information on psychological support as needed, a means for directing the user to relevant legal support services, and a means for passing the input accident situation data to an artificial intelligence module for analysis, thereby enabling the user to accurately determine the degree of fault in the accident and receive professional legal support, as well as psychological support according to the user's emotional state.

[0833] "User terminal" refers to an electronic device used by a user to input the details of a traffic accident, including devices such as smartphones, tablets, and personal computers.

[0834] "Server" refers to a central processing unit that receives, stores, and analyzes accident situation data sent from user terminals.

[0835] "Artificial intelligence module" refers to a software component that analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault.

[0836] "Means for searching for similar cases" refers to the function in which the artificial intelligence module searches for past traffic accident cases in the database based on the input accident situation data and identifies similar cases.

[0837] "Means for calculating the degree of fault" refers to the function in which the artificial intelligence module calculates a reasonable degree of fault based on similar precedents and returns the results to the server.

[0838] "Legal aid link" refers to a feature that provides links or information to help users access appropriate legal aid services.

[0839] "Means for analyzing emotional state" refers to a function that analyzes emotions such as stress and anxiety from the user's responses and voice input, and provides information on psychological support based on the results.

[0840] MODE FOR CARRYING OUT THE INVENTION

[0841] The present invention relates to a system for understanding the circumstances of a traffic accident and calculating the degree of fault, and is a system that includes a user terminal, a server, an artificial intelligence module, a database, an emotion analysis engine, a search means, a means for calculating the degree of fault, and a means for guiding legal support services.

[0842] System configuration

[0843] This system is configured as follows:

[0844] 1. User Device:

[0845] An electronic device used by users to input the circumstances of a traffic accident. This includes devices such as smartphones, tablets, and PCs. Users use these devices to access the system and enter details of the accident.

[0846] 2. Server:

[0847] This is a central processing unit that receives, stores, and analyzes accident situation data sent from user devices. The server temporarily stores the received data and passes it to an artificial intelligence module or emotion analysis engine as needed.

[0848] 3. Artificial Intelligence Module:

[0849] This software component analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault. Specifically, it extracts accident keywords, searches for related information, and generates additional questions if any information is missing.

[0850] 4. Database:

[0851] This is a database for storing past traffic accident precedents and related information, and is used by the artificial intelligence module to calculate the degree of fault.

[0852] 5. Sentiment Analysis Engine:

[0853] The engine analyzes the user's emotional state based on their answers and voice input, and provides information on psychological support based on the results. For example, it assesses stress and anxiety levels and provides relaxation techniques and psychological support.

[0854] 6. Legal Aid Service Guidance:

[0855] It is a means of providing links and information to help users access appropriate legal aid services, so that they can quickly get the legal advice they need.

[0856] Operational Overview

[0857] When a user is involved in a traffic accident, they use their device to input details of the accident (such as the location, date and time of the accident, and traffic light conditions). This data is sent to the server and temporarily stored. The server then passes this data to an artificial intelligence module, which analyzes the data and extracts key elements. Based on the analysis results, if any information is missing, additional questions are generated and sent to the user.

[0858] Furthermore, an emotion analysis engine analyzes the user's emotional state and provides psychological support information as needed. After all data is collected, an artificial intelligence module searches the database for past traffic accident cases and selects the most similar case that best matches. It then calculates the reasonable degree of fault based on the similar case and presents the results to the user. Links to legal support services are also provided as needed.

[0859] Specific examples

[0860] For example, if a user collides with a vehicle going straight while turning right at an intersection, the user inputs the accident details as follows: "Collision occurred while turning right at the intersection," "The light was green," "The other vehicle's speed was 60 km / h," etc. This data is received by the server and passed to an artificial intelligence module. The module analyzes the data, extracts keywords such as "right turn," "going straight," and "green light," and determines whether the necessary information is available.

[0861] If there is insufficient information, the system generates an additional question, such as "What direction was the other vehicle traveling?" If the user answers "north," the information is sent back to the server, and the AI ​​module searches the database for similar cases and calculates the percentage of fault, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The results are then presented to the user, along with a link to legal support services.

[0862] Prompt Sentence Examples

[0863] You can check the system's processing by entering the following prompt sentence into the generative AI model:

[0864] example:

[0865] Please calculate the percentage of fault in an accident where a vehicle collided with a vehicle going straight while turning right at an intersection. The accident circumstances were as follows: the vehicle collided while turning right at an intersection, the traffic light was green, the other vehicle was traveling at 60 km / h, and the other vehicle was heading north.

[0866] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0867] Step 1: Enter the accident details

[0868] When a traffic accident occurs, the user accesses the system using their own device (smartphone, tablet, PC, etc.). The device displays a dedicated form for entering the details of the accident. The user enters the location of the accident, date and time, traffic light conditions, vehicle direction of travel, speed, etc. in text or diagrams. Input data such as "XX intersection, Chiyoda-ku, Tokyo," "March 15, 2023, 2:30 p.m.," "traffic light is green," "vehicle direction of travel is west," and "speed is 30 km / h" is sent to the server. Specific operations of the device include displaying the input form and sending the data.

[0869] Step 2: Receiving and storing data

[0870] The server receives the accident situation data sent from the terminal. The received data is temporarily saved in text or image format, and is then prepared to proceed to the next step. Examples of saved data include "collision while turning right at an intersection," "the traffic light is green," and "the other vehicle's speed is 60 km / h." The specific operations of the server include data reception processing and data storage processing in a database.

[0871] Step 3: Analysis of the accident situation

[0872] The server passes the stored data to an artificial intelligence module. The artificial intelligence module analyzes the input data and understands the accident situation. Specifically, it extracts key elements related to the accident. For example, it extracts keywords such as "intersection," "right turn," "straight ahead," "green light," and "60 km / h." The input data in this process is the stored accident situation data, and the output is the extracted keywords. Specific operations of the server include data transfer processing.

[0873] Step 4: Missing information question generation

[0874] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault. If any information is missing, it generates an additional question (e.g., "What direction is the other vehicle traveling?") and passes it to the server. The server displays the generated question on the user's device. The user answers this and sends the information back to the server via the device. For example, the user answers, "The other vehicle is traveling north." The input data is the analysis results, and a question about the missing information is output. The specific operations of the device include displaying the question and sending the answer.

[0875] Step 5: Emotion Engine in Action

[0876] The emotion analysis engine analyzes the user's emotional state from their responses and voice input. For example, it evaluates their state as "stressed," "anxious," or "relaxed." If necessary, it provides information on relaxation techniques or psychological support. The server follows the instructions of the emotion analysis engine and displays additional support information on the user's device. The specific operations of the emotion engine include analyzing the emotional state and generating support information. The specific operations of the server include displaying support information based on the results of the emotion analysis.

[0877] Step 6: Search for similar cases

[0878] After the server has gathered all the necessary data, it sends it to the AI ​​module, which searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data. The input data is the complete accident situation data, and the output is a list of similar cases. The specific operations of the server include sending data and receiving results.

[0879] Step 7: Calculate the percentage of fault

[0880] The AI ​​module analyzes the details of similar cases and calculates the most reasonable fault ratio from them. For example, a result such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight" is sent to the server. The input data is the details of similar cases, and the output is the calculated fault ratio. The specific operations of the server include receiving the calculation results and saving the data.

[0881] Step 8: State the percentage of fault

[0882] The server generates a screen to present the calculated fault percentage and detailed reasons to the user. The presented information will be information such as "70% fault for vehicles turning right, 30% for vehicles going straight" and will be distributed to the user's terminal. The input data is the fault percentage calculation result, and the output is the screen displayed to the user. The specific operations of the server include generating the screen and distributing the data.

[0883] Step 9: Referral to legal aid services

[0884] If necessary, the server also displays a link on the page that directs the user to a legal aid service, allowing the user to obtain further professional advice. When the user clicks on the link, they can access the legal aid service's reservation or inquiry page. The input data is the user's request for assistance, and the output is the link information for the legal aid service. The specific operations of the server include generating the link and directing the user to the page.

[0885] (Application example 2)

[0886] 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."

[0887] When a traffic accident occurs, especially in an autonomous vehicle, it is necessary to analyze the accident situation quickly and accurately and calculate the degree of fault. However, conventional systems rely on human input, which can lead to delays in inputting the accident situation or analysis being based on incomplete information, making it difficult to respond quickly. Another issue is the lack of information and advice necessary to receive legal support.

[0888] 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 a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for necessary additional information, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the fault ratio based on the similar legal precedents and presenting it to the user, means for directing the user to relevant legal support services, means for automatically acquiring accident data of autonomous vehicles and calculating the fault ratio based on the data, and means for providing legal advice related to the autonomous vehicle accident. This enables quick and accurate analysis of the accident situation and calculation of the fault ratio, and enables necessary legal support to be provided promptly.

[0889] A "user terminal for inputting accident details" is a device that allows a user to input details of a traffic accident when one occurs.

[0890] "Server" refers to a central computer system that receives, analyzes, and processes accident situation data sent from user terminals.

[0891] The "artificial intelligence module" is a program built into the server that analyzes the received data, identifies any additional information needed, and presents it to the user.

[0892] The "means of searching for similar past cases" is a function that searches a database of past traffic accident cases to find cases similar to the current accident situation.

[0893] The "means for calculating the degree of fault" is a module that calculates the degree of responsibility in the current accident based on similar precedents that have been searched.

[0894] "Means for presenting to the user" refers to an interface that displays the calculated fault ratio and necessary information to the user.

[0895] "Legal assistance means" means a feature that provides links or information that directs users to professional services for legal advice or assistance.

[0896] "Means for automatically acquiring accident data from autonomous vehicles" refers to a device that automatically collects accident information from sensors and logging systems built into autonomous vehicles.

[0897] "Means for providing legal advice related to accidents involving autonomous vehicles" refers to a function that provides users with appropriate legal advice depending on the circumstances of an accident involving an autonomous vehicle.

[0898] The present invention is a system that includes a user terminal for inputting accident details, a server, an artificial intelligence module, a means for searching for similar past cases, a means for calculating the degree of fault, a means for directing to legal support services, a means for automatically acquiring accident data of autonomous vehicles, and a means for providing legal advice related to accidents involving autonomous vehicles.

[0899] System Overview

[0900] User Device

[0901] The user device provides an interface that allows users to manually input accident details. The device can be a smartphone, tablet, or PC, and a dedicated form for entering detailed accident information is displayed. Users can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[0902] server

[0903] The server receives and temporarily stores the accident situation data sent from the user terminal, and then passes the received data to the artificial intelligence module for analysis.

[0904] Artificial Intelligence Module

[0905] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key elements related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "green light." It also determines whether additional information is needed, and generates additional questions if there is insufficient information. Examples of missing information include "What was the speed of the other vehicle?" and "What is the damage to the vehicle?"

[0906] Search for similar cases and calculate the percentage of fault

[0907] Based on the information obtained by the AI ​​module, the server searches the database for past traffic accident cases. It selects the most similar cases and calculates the percentage of fault based on them. This percentage of fault and the case data showing the basis for it are presented to the user.

[0908] Legal Aid Services

[0909] The user is presented with a percentage of fault and detailed reasons for it, and if necessary, a link to a legal aid service is also provided, allowing the user to seek further professional advice.

[0910] Data acquisition from autonomous vehicles

[0911] Autonomous vehicles automatically collect accident situation data using internal sensors and logging systems and send it to a server, allowing detailed accident information to be transmitted to the server quickly and accurately.

[0912] Examples of prompt statements

[0913] Example of what the user enters as a prompt:

[0914] I collided with a vehicle going straight while turning right at an intersection. The light was green, and my vehicle was turning right while the other vehicle was going straight. The accident occurred at an intersection in Shibuya Ward, Tokyo. The other vehicle was traveling at a speed of approximately 60 km / h, damaging the front of my vehicle. I would like your help calculating the degree of fault so that I can receive appropriate legal assistance.

[0915] By combining these elements, users can quickly and accurately input accident details, and based on the results, appropriate fault allocation and legal support information can be provided. In addition, data acquisition from autonomous vehicles can speed up accident analysis and legal support for autonomous driving technology.

[0916] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0917] Step 1:

[0918] The user terminal inputs the accident situation data.

[0919] Input: location of accident, date and time, traffic light status, vehicle direction, speed, etc.

[0920] Data manipulation: converting data into text or graphical formats

[0921] Output: Accident situation data is entered into the user's terminal.

[0922] Specific operation: The user inputs the necessary information into a dedicated form using a smartphone or tablet. Example: "While turning right at an intersection in Shibuya Ward, Tokyo, a collision occurred with a vehicle going straight. The traffic light was green and the speed of the other vehicle was approximately 60 km / h."

[0923] Step 2:

[0924] The user terminal transmits the entered accident situation data to the server.

[0925] Input: Accident situation data entered into the user's terminal

[0926] Data processing: Convert data into JSON format

[0927] Output: Accident status data sent to the server

[0928] What it does: When a user presses a button on their device, data is sent to the server via an HTTP request, containing detailed information about the accident.

[0929] Step 3:

[0930] The server analyzes the received data and passes it to the artificial intelligence module.

[0931] Input: Accident situation data sent to the server

[0932] Data processing: Analyze the data and extract the main factors of the accident (e.g., "turning right at an intersection," "collision with a vehicle going straight," "green light")

[0933] Output: Analysis results passed to the AI ​​module

[0934] Specific operation: The data received by the server is analyzed using an internal analysis program (such as a Python script) to sort out the factors that caused the accident.

[0935] Step 4:

[0936] The artificial intelligence module determines what additional information is needed and generates follow-up questions for the user.

[0937] Input: Analysis results passed from the server

[0938] Data manipulation: Identifying missing information and generating follow-up questions

[0939] Output: Data with additional questions

[0940] How it works: The AI ​​module reviews the accident situation data, determines missing information, and generates additional questions, such as "What direction is the other vehicle heading?"

[0941] Step 5:

[0942] The server distributes the generated follow-up questions to the user terminal, and the user inputs the answers.

[0943] Input: Additional question data

[0944] Data processing: Display additional questions on the user's device interface

[0945] Output: Data on user answers to follow-up questions

[0946] Specific operation: The server sends a follow-up question to the user's device, and the user answers it. Example: "What direction is the other vehicle heading?" "Northbound"

[0947] Step 6:

[0948] The user terminal sends the answer to the additional question to the server.

[0949] Input: User's answer to follow-up question

[0950] Data processing: Converting answers to questions into JSON format

[0951] Output: The answer to the follow-up question has been sent to the server

[0952] What happens: The user answers additional questions and sends the data to the server.

[0953] Step 7:

[0954] The server gathers all the necessary data and passes it back to the artificial intelligence module.

[0955] Input: All accident situation data and answers to additional questions

[0956] Data processing: Re-analyzing data to understand the complete accident situation

[0957] Output: The AI ​​module has all the data.

[0958] Specific operation: The server collects all the data and passes it back to the AI ​​module, which performs the final analysis.

[0959] Step 8:

[0960] An artificial intelligence module searches for similar past cases and calculates the degree of fault.

[0961] Input: Complete accident situation data

[0962] Data processing: Search for similar cases in the database and calculate the percentage of fault

[0963] Output: Calculated fault percentage data

[0964] Specific operation: The artificial intelligence module uses a database search engine to identify similar cases and calculate the fault ratio, for example, "70% for the vehicle turning right, 30% for the vehicle going straight."

[0965] Step 9:

[0966] The server displays the calculated fault percentage and detailed reasons for it on the user's device.

[0967] Input: Calculated fault percentage data

[0968] Data processing: Converting data into a user-friendly format

[0969] Output: Fault percentage displayed on user device

[0970] Specific operation: The server sends the calculation results to the user's terminal and presents them to the user through the interface.

[0971] Step 10:

[0972] The server directs the user to legal assistance services if necessary.

[0973] Input: Calculated percentage of fault and legal assistance requirement

[0974] Data processing: generating links and details for legal aid services

[0975] Output: User device displays a link to a legal aid service

[0976] What it does: The server creates information about legal aid services and displays a link for the user to access them quickly.

[0977] 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.

[0978] 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.

[0979] 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.

[0980] [Third embodiment]

[0981] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0982] 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.

[0983] 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).

[0984] 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.

[0985] 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.

[0986] 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).

[0987] 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.

[0988] 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.

[0989] 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.

[0990] 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.

[0991] 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.

[0992] 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."

[0993] MODE FOR CARRYING OUT THE INVENTION

[0994] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means. The specific operation of this system and the processing of its programs are described below.

[0995] System Overview

[0996] 1. Enter the accident details

[0997] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[0998] The device displays a dedicated form for entering accident details, and the user can enter the location, date and time of the accident, traffic light conditions, vehicle direction, speed, etc. in text or by drawing.

[0999] 2. Receipt and storage of data

[1000] The server receives the accident situation data sent from the user terminal, and this data is temporarily stored in the server's storage.

[1001] 3. Analysis of the accident situation

[1002] The server passes the received data to the artificial intelligence module.

[1003] The AI ​​module analyzes the input data and understands the circumstances of the accident, specifically extracting key factors such as collisions when turning right at intersections, traffic light conditions, and the speed of each vehicle.

[1004] 4. Missing Information Questions

[1005] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault.

[1006] If there is missing information, the artificial intelligence module generates additional questions (e.g., "What is the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1007] The server then sends this question back to the user terminal.

[1008] The user answers the additional questions presented and sends them to the server via the terminal.

[1009] 5. Search for similar cases

[1010] After the server has all the necessary data, it sends it to the artificial intelligence module.

[1011] The artificial intelligence module searches the database for past traffic accident cases and selects the case that best matches the entered accident circumstances.

[1012] 6. Calculation of Fault Percentage

[1013] The artificial intelligence module calculates a reasonable degree of fault based on selected legal precedents.

[1014] The calculation results and details of the legal precedents on which they are based are sent to the server.

[1015] 7. Presentation of the percentage of fault

[1016] The server generates a screen to present the calculated fault ratio and its basis to the user.

[1017] The server distributes this information to the user's terminal so that the user can view it.

[1018] 8. Guidance for legal aid services

[1019] If necessary, the server will display a link directing the user to legal aid services, where they can obtain further professional advice.

[1020] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[1021] Specific examples

[1022] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[1023] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[1024] 2. The server receives this and passes it to the artificial intelligence module.

[1025] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[1026] 4. Determine whether all necessary information is available and generate additional questions if missing, such as "What direction is the other vehicle heading?"

[1027] 5. The user answers the follow-up question with "north direction."

[1028] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[1029] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[1030] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[1031] 9. Users can click on a link to access legal aid services.

[1032] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user then enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[1036] Step 2:

[1037] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[1038] Step 3:

[1039] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[1040] Step 4:

[1041] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1042] Step 5:

[1043] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[1044] Step 6:

[1045] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[1046] Step 7:

[1047] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[1048] Step 8:

[1049] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[1050] Step 9:

[1051] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[1052] Step 10:

[1053] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[1054] This process allows users to determine a reasonable share of fault, obtain a fair settlement, and take steps to obtain professional legal assistance if necessary.

[1055] Example 1

[1056] 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."

[1057] When a traffic accident occurs, those involved are required to quickly and accurately determine the degree of fault, but doing so efficiently is not easy. Conventional methods require the manual collection and analysis of relevant information, which is time-consuming and labor-intensive. Furthermore, without thorough knowledge of the law and past legal precedents, it is difficult to respond appropriately. As a result, it often takes a long time for victims to receive appropriate legal support. The present invention aims to solve these problems and realize the rapid and accurate calculation of the degree of fault and the provision of legal support when a traffic accident occurs.

[1058] 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.

[1059] In this invention, the server includes a user terminal for inputting the accident situation, an information processing device that receives the accident situation data input from the user terminal, an artificial intelligence module that analyzes the data received by the information processing device and asks the user for any additional information needed, a means for searching for similar past cases based on the information obtained by the artificial intelligence module, a means for calculating the fault ratio based on the similar cases and presenting it to the user, and a means for directing the user to relevant legal support services. This enables the system to not only quickly and accurately calculate the fault ratio when a traffic accident occurs, but also enables the user to quickly receive the necessary legal support.

[1060] "Accident situation" refers to a series of information that constitutes the details of the accident, such as the location of the traffic accident, the date and time, the traffic light conditions, and the direction and speed of each vehicle.

[1061] A "user terminal" is an information processing device used by a user to input information about traffic accidents, and specifically includes devices such as smartphones, tablets, and personal computers.

[1062] The term "information processing device" refers to a computer system that receives, analyzes, and saves data acquired from a user terminal.

[1063] An "artificial intelligence module" is a software or hardware component that has the ability to analyze input accident situation data, identify missing information, and generate additional questions as necessary.

[1064] "Similar cases" are data extracted from legal precedents and examples of traffic accidents that have occurred in the past that most closely resemble the current accident situation.

[1065] "Fault ratio" indicates the percentage of fault of each party involved in a traffic accident, and is an important indicator for determining liability for compensation.

[1066] "Legal aid services" are professional organisations or services that provide expert advice and support on legal issues relating to road traffic accidents.

[1067] The present invention relates to a system for quickly and accurately calculating the degree of fault in a traffic accident and providing legal support to users. Specific embodiments of the system are described below.

[1068] System configuration

[1069] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means.

[1070] The user terminal is an information processing device such as a smartphone, tablet, or PC, and displays a dedicated form for the user to enter information about the traffic accident.

[1071] Specific operation of the system

[1072] Enter the accident details

[1073] When a traffic accident occurs, the user accesses the system from their own terminal.

[1074] The terminal displays a dedicated form for entering accident details, and the user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form.

[1075] Receiving and storing data

[1076] The user completes the input and presses the send button.

[1077] The terminal transmits the transmitted data to the server.

[1078] The server temporarily stores the received data in storage.

[1079] Analysis of the accident situation

[1080] The server passes the temporarily stored data to an artificial intelligence (AI) module.

[1081] The AI ​​module analyzes the received data and extracts important information related to the accident, such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light status, and the speed of each vehicle.

[1082] Missing Information Question

[1083] Based on the analysis results, the artificial intelligence module determines whether additional information is needed to accurately calculate the percentage of fault.

[1084] If there is missing information, the AI ​​module automatically generates additional questions, such as "What direction is the other vehicle heading?"

[1085] The server transmits the generated follow-up question to the user terminal.

[1086] The user answers the additional questions and sends them to the server via the terminal.

[1087] Search for similar cases

[1088] The server confirms that all necessary data is available and sends the data back to the artificial intelligence module.

[1089] The artificial intelligence module searches a database of past traffic accident cases and extracts the case that best matches the input accident situation.

[1090] Calculation of fault ratio

[1091] The AI ​​module calculates the percentage of fault based on selected legal precedents, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight."

[1092] The artificial intelligence module sends the results of this calculation and details of the legal precedents on which it is based to the server.

[1093] Presentation of the percentage of fault

[1094] The server generates a screen to present the received calculation results to the user.

[1095] The screen generated by the server is sent to the user's terminal, allowing the user to check the calculated fault ratio and its basis.

[1096] Guidance for legal aid services

[1097] The server displays a link on the screen that directs the user to legal assistance services if necessary.

[1098] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[1099] Specific examples

[1100] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[1101] 1. The user enters information such as the location of the accident, the traffic light conditions, and the speed of the other vehicle into a dedicated form on the device ("Collision occurred while turning right at an intersection," "The traffic light was green," "The other vehicle's speed was 60 km / h").

[1102] 2. The terminal sends the entered data to the server.

[1103] 3. The server receives this data and stores it temporarily in storage.

[1104] 4. The server passes the data to an artificial intelligence module, which analyzes the circumstances of the accident and extracts important keywords such as "turn right," "go straight," and "green light."

[1105] 5. Based on the analysis results, the artificial intelligence module determines what information is missing and automatically generates additional questions, such as, "What direction is the other vehicle heading?"

[1106] 6. The server sends an additional question to the user's terminal, and the user answers "north."

[1107] 7. The server receives the answer and sends it back to the artificial intelligence module, which searches for similar precedents.

[1108] 8. The artificial intelligence module calculates the percentage of fault based on precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[1109] 9. The server generates a screen that presents the calculation results in an easy-to-view format for the user and sends it to the user's terminal.

[1110] 10. If necessary, the server will display a link to a legal assistance service on the screen, allowing the user to receive professional assistance through that link.

[1111] Example prompt sentence:

[1112] "You collide with a vehicle going straight while turning right at an intersection. The light is green and the other vehicle is traveling at 60km / h. Please calculate the percentage of fault in this situation."

[1113] In this way, the invention helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[1114] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1115] Step 1:

[1116] When a traffic accident occurs, the user accesses the system from their own terminal. The terminal displays a dedicated form for entering the accident details. The user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form and submits the input data. This data is then sent to the server.

[1117] input:

[1118] Accident details (location, date and time, traffic signal conditions, vehicle direction, speed, etc.)

[1119] output:

[1120] Data sent to the server

[1121] Specific behavior:

[1122] The user fills out the form and presses the submit button.

[1123] Step 2:

[1124] The server receives the accident situation data sent from the user terminal and temporarily stores it in storage, which is used for subsequent processing.

[1125] input:

[1126] Accident situation data sent from the user device

[1127] output:

[1128] Data stored in storage

[1129] Specific behavior:

[1130] The server receives the data and temporarily stores it in a database.

[1131] Step 3:

[1132] The server passes the temporarily stored data to an artificial intelligence (AI) module, which analyzes the received data and extracts important information about the accident (such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light conditions, and the speed of each vehicle).

[1133] input:

[1134] Temporarily saved accident situation data

[1135] output:

[1136] Analysis results (important information extracted)

[1137] Specific behavior:

[1138] The server sends the data to the AI ​​module, which analyzes the information and extracts important keywords.

[1139] Step 4:

[1140] Based on the analysis results, the AI ​​module determines whether additional information necessary to accurately calculate the percentage of fault is missing. If so, the AI ​​module automatically generates additional questions and passes them to the server.

[1141] input:

[1142] Analysis results

[1143] output:

[1144] Additional questions (if necessary)

[1145] Specific behavior:

[1146] The AI ​​module identifies missing information and generates follow-up questions if necessary.

[1147] Step 5:

[1148] The server transmits the generated follow-up question to the user terminal, and the user answers the follow-up question and transmits the answer to the server via the terminal.

[1149] input:

[1150] Additional questions

[1151] output:

[1152] User Answers

[1153] Specific behavior:

[1154] The server sends a question, and the user enters and sends the answer.

[1155] Step 6:

[1156] The server receives additional responses from the user and sends them back to the AI ​​module, which searches a database of past traffic accident cases and extracts the case that best matches the entered accident situation.

[1157] input:

[1158] Additional user answers

[1159] output:

[1160] Similar precedents

[1161] Specific behavior:

[1162] The server sends the additional answers to the AI ​​module, which then searches the database to extract similar cases.

[1163] Step 7:

[1164] The AI ​​module calculates the percentage of fault based on the selected legal precedents, and sends the calculation results and details of the legal precedents on which they are based to the server.

[1165] input:

[1166] Similar precedents

[1167] output:

[1168] Calculation results and basis of fault ratio

[1169] Specific behavior:

[1170] The AI ​​module calculates the degree of fault based on legal precedent and sends the results to the server.

[1171] Step 8:

[1172] The server generates a screen to display the received calculation results to the user, and sends it to the user's terminal. The user can check the calculated fault ratio and its basis.

[1173] input:

[1174] Calculation results and basis of fault ratio

[1175] output:

[1176] User presentation screen

[1177] Specific behavior:

[1178] The server generates a screen and sends it to the user's terminal to present the information.

[1179] Step 9:

[1180] The server displays a link on the screen to direct the user to the legal aid service as needed, and when the user clicks the link, the user can access the reservation page or inquiry page of the legal aid service.

[1181] input:

[1182] User presentation screen

[1183] output:

[1184] Links to legal aid services

[1185] Specific behavior:

[1186] The server displays a link that the user clicks to access the legal assistance service.

[1187] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[1188] (Application example 1)

[1189] 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."

[1190] Traffic accidents involving autonomous vehicles generate unique data (camera footage, LiDAR data, etc.) that differs from those generated by human drivers, making them difficult to handle with conventional accident analysis systems. Furthermore, there is a need for rapid and accurate calculations of fault and legal support after accidents, but current systems require a lot of manual input and analysis, which is time-consuming and can lack accuracy. Therefore, there is a need for a system that can effectively incorporate data specific to autonomous vehicles, quickly and accurately calculate fault, and provide appropriate legal support to users.

[1191] 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.

[1192] In this invention, the server includes means for automatically acquiring sensor data from the autonomous vehicle as accident situation data, means for presenting accident analysis results on a head-mounted display, a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for any additional information required, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the degree of fault based on the similar legal precedents and presenting it to the user, and means for directing the user to relevant legal support services. This enables rapid and accurate analysis of autonomous vehicle accidents, calculation of the degree of fault, and provision of legal support services.

[1193] "Accident situation data" refers to information including the location, date and time, traffic light conditions, vehicle direction and speed, and related sensor data when a traffic accident occurs.

[1194] A "user terminal" is an electronic device, such as a smartphone, tablet, or PC, through which a user inputs data.

[1195] The "server" is a computer system that receives, analyzes, and stores accident situation data sent from user terminals.

[1196] An "artificial intelligence module" is software that analyzes received data, identifies any additional information needed, and performs advanced processing such as calculating fault percentages.

[1197] A "head-mounted display" is a device that a user wears on their head and displays information on a display.

[1198] "Sensor data" is digital information acquired by sensors (e.g., cameras, LiDAR, etc.) installed in autonomous vehicles.

[1199] "Similar precedents" refer to precedents of traffic accidents that occurred in the past and are similar to the current accident situation.

[1200] "Additional questions" are questions generated by the artificial intelligence module to supplement missing parts of the accident situation data.

[1201] "Legal aid service" means a professional organisation or service that provides legal advice and assistance in relation to road traffic accidents.

[1202] This invention is a system for analyzing traffic accident situations involving autonomous vehicles and accurately calculating the degree of fault. The system works by having the user input the accident situation using a smartphone or head-mounted display (HMD), and the server receives the data and analyzes it using an artificial intelligence module.

[1203] First, the user uses a device to input accident situation data. The input data includes sensor data acquired from the autonomous vehicle, such as camera footage and LiDAR data. This makes it possible to grasp the details of the accident situation, which was difficult to do using conventional methods.

[1204] The server then receives and temporarily stores the accident situation data sent from the user's device. The received data is then passed to an AI module for analysis. The AI ​​module extracts key elements from the data analysis process (e.g., collisions during right turns, traffic light conditions, and the speed of each vehicle).

[1205] If additional information is needed based on the analysis results, the AI ​​module will generate additional questions and send them to the server, which will then display them on the user's device and prompt the user to answer them. This process will allow for a complete understanding of the accident situation.

[1206] Once all the necessary data is collected, the server uses an artificial intelligence module to access the data in the database to search for similar past cases. Once a match is found, the fault ratio is calculated based on that case and the result is presented to the user by the server.

[1207] Finally, the system will display links to direct users to relevant legal aid services, allowing them to quickly receive professional legal assistance when needed.

[1208] During this process, the user can visually check the accident analysis results in real time using an HMD, which allows them to more intuitively understand the accident situation and the degree of fault.

[1209] Specific examples

[1210] For example, consider the case where an autonomous vehicle collides with a vehicle traveling straight while turning right at an intersection. The user inputs and transmits the accident details, such as "Intersection A," "2023-10-01 14:30," "Light is green," "Right-turn speed 15 km / h," and "Straight-moving vehicle speed 60 km / h." The server receives this data and passes it to an artificial intelligence module. After analysis, the missing information, "the direction of the other vehicle," is presented, and the user answers "northbound." The server then searches for similar cases and calculates the fault ratio: "70% for the right-turning vehicle, 30% for the straight-moving vehicle," which is presented to the user. After viewing the results, the user can easily access legal support services by clicking the displayed link.

[1211] Prompt Sentence Examples

[1212] "My self-driving vehicle collided with another vehicle at intersection A. Please analyze the following accident information and tell me the percentage of fault.

[1213] Location: Intersection A

[1214] Time: 2023-10-01 14:30

[1215] Signal status: Green

[1216] Vehicle direction: Turn right, go straight

[1217] Vehicle speed: Right-turning vehicles 15km / h, straight-going vehicles 60km / h

[1218] Sensor Data:

[1219] Camera footage: Image data

[1220] LiDAR: LiDAR data

[1221] In this way, it becomes possible to quickly and accurately analyze accidents involving autonomous vehicles and calculate the degree of fault.

[1222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1223] Step 1:

[1224] The user inputs accident situation data using a terminal. The input data includes the location of the accident, date and time, traffic light conditions, vehicle direction, speed, and sensor data from the autonomous vehicle. The input data is then sent from the terminal to the server.

[1225] Step 2:

[1226] The server receives the accident situation data sent from the user terminal and temporarily stores it. The server then passes the received data to the artificial intelligence module. At this point, the input is the accident situation data entered by the user, and the output is the temporarily stored data.

[1227] Step 3:

[1228] The AI ​​module analyzes the received accident situation data and extracts important elements (e.g., "collision while turning right," "green light," "right turn speed 15 km / h," "straight vehicle speed 60 km / h," etc.). This extraction process organizes the data and identifies important information. The input is the received data, and the output is the analysis results, which are important elements.

[1229] Step 4:

[1230] Based on the analysis results, the AI ​​module determines whether additional information is needed to accurately calculate the degree of fault. If any information is missing, it generates additional questions. The generated questions are sent to the server. The input is the analysis results, and the output is the additional questions.

[1231] Step 5:

[1232] The server sends the generated follow-up question to the user terminal. The user terminal displays the question to the user, and the user inputs an answer to the follow-up question. The input is the follow-up question, and the output is the user's follow-up answer.

[1233] Step 6:

[1234] The server receives the user's additional answer and resends it to the AI ​​module. The AI ​​module analyzes the additional answer and searches the database for similar past cases. The input is the user's additional answer, and the output is the searched similar cases.

[1235] Step 7:

[1236] The AI ​​module calculates a reasonable percentage of fault based on the searched similar cases. The calculation results are sent to the server. The input is the similar cases, and the output is the calculated percentage of fault.

[1237] Step 8:

[1238] The server generates a screen to present the calculated fault ratio and its basis to the user. The generated screen is sent to the user's terminal so that the user can check it. The input is the calculated fault ratio, and the output is the generated presentation screen.

[1239] Step 9:

[1240] The server displays a link to direct the user to a legal aid service as needed. When the user clicks on this link, they can access the legal aid service's reservation page or inquiry page. The input is the legal aid service URL, and the output is a screen displaying the link.

[1241] In this way, by linking the server, terminal, and user, it is possible to analyze the accident situation and calculate the degree of fault quickly and accurately, and provide appropriate legal support.

[1242] 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.

[1243] MODE FOR CARRYING OUT THE INVENTION

[1244] The present invention is composed of a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, a legal support service guidance means, and an emotion engine. The specific operation form of this system and the processing of its programs are described below.

[1245] System Overview

[1246] 1. Enter the accident details

[1247] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[1248] The device displays a dedicated form for entering accident details, and the user can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[1249] 2. Receipt and storage of data

[1250] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[1251] 3. Analysis of the accident situation

[1252] The server passes the stored data to the artificial intelligence module.

[1253] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light is green."

[1254] 4. Missing Information Questions

[1255] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1256] The server displays the generated question on the user's device. The user answers the question and sends the information to the server via the device. For example, the user might answer, "The speed of the other vehicle is 60 km / h."

[1257] 5. Operation of the Emotion Engine

[1258] The emotion engine analyzes the user's emotional state from their responses and voice input, assessing, for example, their stress or anxiety levels and providing information on relaxation techniques or psychological support as needed.

[1259] The server displays additional support information to the user based on instructions from the emotion engine.

[1260] 6. Search for similar cases

[1261] After the server has all the necessary data, it sends it to the artificial intelligence module.

[1262] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[1263] 7. Calculation of Fault Percentage

[1264] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[1265] 8. Presentation of the percentage of fault

[1266] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The server then distributes this information to the user's device.

[1267] 9. Guidance for legal aid services

[1268] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[1269] Specific examples

[1270] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[1271] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[1272] 2. The server receives this and passes it to the artificial intelligence module.

[1273] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[1274] 4. Determine whether all necessary information is available and generate follow-up questions if missing (e.g., "What direction is the other vehicle heading?").

[1275] 5. The user answers the follow-up question with "north direction."

[1276] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[1277] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[1278] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[1279] 9. Users can click on a link to access legal aid services.

[1280] Through this process, users can determine the reasonable degree of fault and, if necessary, receive support from the emotion engine. Furthermore, if further professional assistance is required, users will be efficiently directed to the appropriate service, ensuring a fair and appropriate solution.

[1281] The processing flow will be explained below.

[1282] Step 1:

[1283] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[1284] Step 2:

[1285] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[1286] Step 3:

[1287] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[1288] Step 4:

[1289] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1290] Step 5:

[1291] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[1292] Step 6:

[1293] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[1294] Step 7:

[1295] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[1296] Step 8:

[1297] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[1298] Step 9:

[1299] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[1300] Step 10:

[1301] The emotion engine analyzes the user's input data and voice to identify their emotional state, for example determining their stress or anxiety level through voice analysis.

[1302] Step 11:

[1303] Based on the emotional state obtained from the emotion engine, the server provides additional information on relaxation techniques and psychological support if the user is experiencing high levels of stress or anxiety.

[1304] Step 12:

[1305] If necessary, the server may display a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. When the user clicks on the link, they are taken to a booking or enquiry page for the legal aid service.

[1306] Example 2

[1307] 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."

[1308] Conventional traffic accident processing systems require complicated input and analysis of accident circumstances, making it difficult for users to calculate the degree of fault and access the legal support they need. Furthermore, they do not adequately consider the emotional state of the parties involved in the accident, and psychological support is rarely provided. This makes it difficult for users to understand the appropriate degree of fault and quickly obtain the necessary legal support, and also creates a significant mental burden after an accident.

[1309] 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.

[1310] In this invention, the server includes a means for analyzing the user's emotional state and providing information on psychological support as needed, a means for directing the user to relevant legal support services, and a means for passing the input accident situation data to an artificial intelligence module for analysis, thereby enabling the user to accurately determine the degree of fault in the accident and receive professional legal support, as well as psychological support according to the user's emotional state.

[1311] "User terminal" refers to an electronic device used by a user to input the details of a traffic accident, including devices such as smartphones, tablets, and personal computers.

[1312] "Server" refers to a central processing unit that receives, stores, and analyzes accident situation data sent from user terminals.

[1313] "Artificial intelligence module" refers to a software component that analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault.

[1314] "Means for searching for similar cases" refers to the function in which the artificial intelligence module searches for past traffic accident cases in the database based on the input accident situation data and identifies similar cases.

[1315] "Means for calculating the degree of fault" refers to the function in which the artificial intelligence module calculates a reasonable degree of fault based on similar precedents and returns the results to the server.

[1316] "Legal aid link" refers to a feature that provides links or information to help users access appropriate legal aid services.

[1317] "Means for analyzing emotional state" refers to a function that analyzes emotions such as stress and anxiety from the user's responses and voice input, and provides information on psychological support based on the results.

[1318] MODE FOR CARRYING OUT THE INVENTION

[1319] The present invention relates to a system for understanding the circumstances of a traffic accident and calculating the degree of fault, and is a system that includes a user terminal, a server, an artificial intelligence module, a database, an emotion analysis engine, a search means, a means for calculating the degree of fault, and a means for guiding legal support services.

[1320] System configuration

[1321] This system is configured as follows:

[1322] 1. User Device:

[1323] An electronic device used by users to input the circumstances of a traffic accident. This includes devices such as smartphones, tablets, and PCs. Users use these devices to access the system and enter details of the accident.

[1324] 2. Server:

[1325] This is a central processing unit that receives, stores, and analyzes accident situation data sent from user devices. The server temporarily stores the received data and passes it to an artificial intelligence module or emotion analysis engine as needed.

[1326] 3. Artificial Intelligence Module:

[1327] This software component analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault. Specifically, it extracts accident keywords, searches for related information, and generates additional questions if any information is missing.

[1328] 4. Database:

[1329] This is a database for storing past traffic accident precedents and related information, and is used by the artificial intelligence module to calculate the degree of fault.

[1330] 5. Sentiment Analysis Engine:

[1331] The engine analyzes the user's emotional state based on their answers and voice input, and provides information on psychological support based on the results. For example, it assesses stress and anxiety levels and provides relaxation techniques and psychological support.

[1332] 6. Legal Aid Service Guidance:

[1333] It is a means of providing links and information to help users access appropriate legal aid services, so that they can quickly get the legal advice they need.

[1334] Operational Overview

[1335] When a user is involved in a traffic accident, they use their device to input details of the accident (such as the location, date and time of the accident, and traffic light conditions). This data is sent to the server and temporarily stored. The server then passes this data to an artificial intelligence module, which analyzes the data and extracts key elements. Based on the analysis results, if any information is missing, additional questions are generated and sent to the user.

[1336] Furthermore, an emotion analysis engine analyzes the user's emotional state and provides psychological support information as needed. After all data is collected, an artificial intelligence module searches the database for past traffic accident cases and selects the most similar case that best matches. It then calculates the reasonable degree of fault based on the similar case and presents the results to the user. Links to legal support services are also provided as needed.

[1337] Specific examples

[1338] For example, if a user collides with a vehicle going straight while turning right at an intersection, the user inputs the accident details as follows: "Collision occurred while turning right at the intersection," "The light was green," "The other vehicle's speed was 60 km / h," etc. This data is received by the server and passed to an artificial intelligence module. The module analyzes the data, extracts keywords such as "right turn," "going straight," and "green light," and determines whether the necessary information is available.

[1339] If there is insufficient information, the system generates an additional question, such as "What direction was the other vehicle traveling?" If the user answers "north," the information is sent back to the server, and the AI ​​module searches the database for similar cases and calculates the percentage of fault, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The results are then presented to the user, along with a link to legal support services.

[1340] Prompt Sentence Examples

[1341] You can check the system's processing by entering the following prompt sentence into the generative AI model:

[1342] example:

[1343] Please calculate the percentage of fault in an accident where a vehicle collided with a vehicle going straight while turning right at an intersection. The accident circumstances were as follows: the vehicle collided while turning right at an intersection, the traffic light was green, the other vehicle was traveling at 60 km / h, and the other vehicle was heading north.

[1344] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1345] Step 1: Enter the accident details

[1346] When a traffic accident occurs, the user accesses the system using their own device (smartphone, tablet, PC, etc.). The device displays a dedicated form for entering the details of the accident. The user enters the location of the accident, date and time, traffic light conditions, vehicle direction of travel, speed, etc. in text or diagrams. Input data such as "XX intersection, Chiyoda-ku, Tokyo," "March 15, 2023, 2:30 p.m.," "traffic light is green," "vehicle direction of travel is west," and "speed is 30 km / h" is sent to the server. Specific operations of the device include displaying the input form and sending the data.

[1347] Step 2: Receiving and storing data

[1348] The server receives the accident situation data sent from the terminal. The received data is temporarily saved in text or image format, and is then prepared to proceed to the next step. Examples of saved data include "collision while turning right at an intersection," "the traffic light is green," and "the other vehicle's speed is 60 km / h." The specific operations of the server include data reception processing and data storage processing in a database.

[1349] Step 3: Analysis of the accident situation

[1350] The server passes the stored data to an artificial intelligence module. The artificial intelligence module analyzes the input data and understands the accident situation. Specifically, it extracts key elements related to the accident. For example, it extracts keywords such as "intersection," "right turn," "straight ahead," "green light," and "60 km / h." The input data in this process is the stored accident situation data, and the output is the extracted keywords. Specific operations of the server include data transfer processing.

[1351] Step 4: Missing information question generation

[1352] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault. If any information is missing, it generates an additional question (e.g., "What direction is the other vehicle traveling?") and passes it to the server. The server displays the generated question on the user's device. The user answers this and sends the information back to the server via the device. For example, the user answers, "The other vehicle is traveling north." The input data is the analysis results, and a question about the missing information is output. The specific operations of the device include displaying the question and sending the answer.

[1353] Step 5: Emotion Engine in Action

[1354] The emotion analysis engine analyzes the user's emotional state from their responses and voice input. For example, it evaluates their state as "stressed," "anxious," or "relaxed." If necessary, it provides information on relaxation techniques or psychological support. The server follows the instructions of the emotion analysis engine and displays additional support information on the user's device. The specific operations of the emotion engine include analyzing the emotional state and generating support information. The specific operations of the server include displaying support information based on the results of the emotion analysis.

[1355] Step 6: Search for similar cases

[1356] After the server has gathered all the necessary data, it sends it to the AI ​​module, which searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data. The input data is the complete accident situation data, and the output is a list of similar cases. The specific operations of the server include sending data and receiving results.

[1357] Step 7: Calculate the percentage of fault

[1358] The AI ​​module analyzes the details of similar cases and calculates the most reasonable fault ratio from them. For example, a result such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight" is sent to the server. The input data is the details of similar cases, and the output is the calculated fault ratio. The specific operations of the server include receiving the calculation results and saving the data.

[1359] Step 8: State the percentage of fault

[1360] The server generates a screen to present the calculated fault percentage and detailed reasons to the user. The presented information will be information such as "70% fault for vehicles turning right, 30% for vehicles going straight" and will be distributed to the user's terminal. The input data is the fault percentage calculation result, and the output is the screen displayed to the user. The specific operations of the server include generating the screen and distributing the data.

[1361] Step 9: Referral to legal aid services

[1362] If necessary, the server also displays a link on the page that directs the user to a legal aid service, allowing the user to obtain further professional advice. When the user clicks on the link, they can access the legal aid service's reservation or inquiry page. The input data is the user's request for assistance, and the output is the link information for the legal aid service. The specific operations of the server include generating the link and directing the user to the page.

[1363] (Application example 2)

[1364] 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."

[1365] When a traffic accident occurs, especially in an autonomous vehicle, it is necessary to analyze the accident situation quickly and accurately and calculate the degree of fault. However, conventional systems rely on human input, which can lead to delays in inputting the accident situation or analysis being based on incomplete information, making it difficult to respond quickly. Another issue is the lack of information and advice necessary to receive legal support.

[1366] 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 a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for necessary additional information, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the fault ratio based on the similar legal precedents and presenting it to the user, means for directing the user to relevant legal support services, means for automatically acquiring accident data of autonomous vehicles and calculating the fault ratio based on the data, and means for providing legal advice related to the autonomous vehicle accident. This enables quick and accurate analysis of the accident situation and calculation of the fault ratio, and enables necessary legal support to be provided promptly.

[1367] A "user terminal for inputting accident details" is a device that allows a user to input details of a traffic accident when one occurs.

[1368] "Server" refers to a central computer system that receives, analyzes, and processes accident situation data sent from user terminals.

[1369] The "artificial intelligence module" is a program built into the server that analyzes the received data, identifies any additional information needed, and presents it to the user.

[1370] The "means of searching for similar past cases" is a function that searches a database of past traffic accident cases to find cases similar to the current accident situation.

[1371] The "means for calculating the degree of fault" is a module that calculates the degree of responsibility in the current accident based on similar precedents that have been searched.

[1372] "Means for presenting to the user" refers to an interface that displays the calculated fault ratio and necessary information to the user.

[1373] "Legal assistance means" means a feature that provides links or information that directs users to professional services for legal advice or assistance.

[1374] "Means for automatically acquiring accident data from autonomous vehicles" refers to a device that automatically collects accident information from sensors and logging systems built into autonomous vehicles.

[1375] "Means for providing legal advice related to accidents involving autonomous vehicles" refers to a function that provides users with appropriate legal advice depending on the circumstances of an accident involving an autonomous vehicle.

[1376] The present invention is a system that includes a user terminal for inputting accident details, a server, an artificial intelligence module, a means for searching for similar past cases, a means for calculating the degree of fault, a means for directing to legal support services, a means for automatically acquiring accident data of autonomous vehicles, and a means for providing legal advice related to accidents involving autonomous vehicles.

[1377] System Overview

[1378] User Device

[1379] The user device provides an interface that allows users to manually input accident details. The device can be a smartphone, tablet, or PC, and a dedicated form for entering detailed accident information is displayed. Users can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[1380] server

[1381] The server receives and temporarily stores the accident situation data sent from the user terminal, and then passes the received data to the artificial intelligence module for analysis.

[1382] Artificial Intelligence Module

[1383] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key elements related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "green light." It also determines whether additional information is needed, and generates additional questions if there is insufficient information. Examples of missing information include "What was the speed of the other vehicle?" and "What is the damage to the vehicle?"

[1384] Search for similar cases and calculate the percentage of fault

[1385] Based on the information obtained by the AI ​​module, the server searches the database for past traffic accident cases. It selects the most similar cases and calculates the percentage of fault based on them. This percentage of fault and the case data showing the basis for it are presented to the user.

[1386] Legal Aid Services

[1387] The user is presented with a percentage of fault and detailed reasons for it, and if necessary, a link to a legal aid service is also provided, allowing the user to seek further professional advice.

[1388] Data acquisition from autonomous vehicles

[1389] Autonomous vehicles automatically collect accident situation data using internal sensors and logging systems and send it to a server, allowing detailed accident information to be transmitted to the server quickly and accurately.

[1390] Examples of prompt statements

[1391] Example of what the user enters as a prompt:

[1392] I collided with a vehicle going straight while turning right at an intersection. The light was green, and my vehicle was turning right while the other vehicle was going straight. The accident occurred at an intersection in Shibuya Ward, Tokyo. The other vehicle was traveling at a speed of approximately 60 km / h, damaging the front of my vehicle. I would like your help calculating the degree of fault so that I can receive appropriate legal assistance.

[1393] By combining these elements, users can quickly and accurately input accident details, and based on the results, appropriate fault allocation and legal support information can be provided. In addition, data acquisition from autonomous vehicles can speed up accident analysis and legal support for autonomous driving technology.

[1394] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1395] Step 1:

[1396] The user terminal inputs the accident situation data.

[1397] Input: location of accident, date and time, traffic light status, vehicle direction, speed, etc.

[1398] Data manipulation: converting data into text or graphical formats

[1399] Output: Accident situation data is entered into the user's terminal.

[1400] Specific operation: The user inputs the necessary information into a dedicated form using a smartphone or tablet. Example: "While turning right at an intersection in Shibuya Ward, Tokyo, a collision occurred with a vehicle going straight. The traffic light was green and the speed of the other vehicle was approximately 60 km / h."

[1401] Step 2:

[1402] The user terminal transmits the entered accident situation data to the server.

[1403] Input: Accident situation data entered into the user's terminal

[1404] Data processing: Convert data into JSON format

[1405] Output: Accident status data sent to the server

[1406] What it does: When a user presses a button on their device, data is sent to the server via an HTTP request, containing detailed information about the accident.

[1407] Step 3:

[1408] The server analyzes the received data and passes it to the artificial intelligence module.

[1409] Input: Accident situation data sent to the server

[1410] Data processing: Analyze the data and extract the main factors of the accident (e.g., "turning right at an intersection," "collision with a vehicle going straight," "green light")

[1411] Output: Analysis results passed to the AI ​​module

[1412] Specific operation: The data received by the server is analyzed using an internal analysis program (such as a Python script) to sort out the factors that caused the accident.

[1413] Step 4:

[1414] The artificial intelligence module determines what additional information is needed and generates follow-up questions for the user.

[1415] Input: Analysis results passed from the server

[1416] Data manipulation: Identifying missing information and generating follow-up questions

[1417] Output: Data with additional questions

[1418] How it works: The AI ​​module reviews the accident situation data, determines missing information, and generates additional questions, such as "What direction is the other vehicle heading?"

[1419] Step 5:

[1420] The server distributes the generated follow-up questions to the user terminal, and the user inputs the answers.

[1421] Input: Additional question data

[1422] Data processing: Display additional questions on the user's device interface

[1423] Output: Data on user answers to follow-up questions

[1424] Specific operation: The server sends a follow-up question to the user's device, and the user answers it. Example: "What direction is the other vehicle heading?" "Northbound"

[1425] Step 6:

[1426] The user terminal sends the answer to the additional question to the server.

[1427] Input: User's answer to follow-up question

[1428] Data processing: Converting answers to questions into JSON format

[1429] Output: The answer to the follow-up question has been sent to the server

[1430] What happens: The user answers additional questions and sends the data to the server.

[1431] Step 7:

[1432] The server gathers all the necessary data and passes it back to the artificial intelligence module.

[1433] Input: All accident situation data and answers to additional questions

[1434] Data processing: Re-analyzing data to understand the complete accident situation

[1435] Output: The AI ​​module has all the data.

[1436] Specific operation: The server collects all the data and passes it back to the AI ​​module, which performs the final analysis.

[1437] Step 8:

[1438] An artificial intelligence module searches for similar past cases and calculates the degree of fault.

[1439] Input: Complete accident situation data

[1440] Data processing: Search for similar cases in the database and calculate the percentage of fault

[1441] Output: Calculated fault percentage data

[1442] Specific operation: The artificial intelligence module uses a database search engine to identify similar cases and calculate the fault ratio, for example, "70% for the vehicle turning right, 30% for the vehicle going straight."

[1443] Step 9:

[1444] The server displays the calculated fault percentage and detailed reasons for it on the user's device.

[1445] Input: Calculated fault percentage data

[1446] Data processing: Converting data into a user-friendly format

[1447] Output: Fault percentage displayed on user device

[1448] Specific operation: The server sends the calculation results to the user's terminal and presents them to the user through the interface.

[1449] Step 10:

[1450] The server directs the user to legal assistance services if necessary.

[1451] Input: Calculated percentage of fault and legal assistance requirement

[1452] Data processing: generating links and details for legal aid services

[1453] Output: User device displays a link to a legal aid service

[1454] What it does: The server creates information about legal aid services and displays a link for the user to access them quickly.

[1455] 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.

[1456] 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.

[1457] 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.

[1458] [Fourth embodiment]

[1459] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1460] 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.

[1461] 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).

[1462] 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.

[1463] 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.

[1464] 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).

[1465] 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.

[1466] 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.

[1467] 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.

[1468] 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.

[1469] 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.

[1470] 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.

[1471] 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."

[1472] MODE FOR CARRYING OUT THE INVENTION

[1473] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means. The specific operation of this system and the processing of its programs are described below.

[1474] System Overview

[1475] 1. Enter the accident details

[1476] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[1477] The device displays a dedicated form for entering accident details, and the user can enter the location, date and time of the accident, traffic light conditions, vehicle direction, speed, etc. in text or by drawing.

[1478] 2. Receipt and storage of data

[1479] The server receives the accident situation data sent from the user terminal, and this data is temporarily stored in the server's storage.

[1480] 3. Analysis of the accident situation

[1481] The server passes the received data to the artificial intelligence module.

[1482] The AI ​​module analyzes the input data and understands the circumstances of the accident, specifically extracting key factors such as collisions when turning right at intersections, traffic light conditions, and the speed of each vehicle.

[1483] 4. Missing Information Questions

[1484] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault.

[1485] If there is missing information, the artificial intelligence module generates additional questions (e.g., "What is the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1486] The server then sends this question back to the user terminal.

[1487] The user answers the additional questions presented and sends them to the server via the terminal.

[1488] 5. Search for similar cases

[1489] After the server has all the necessary data, it sends it to the artificial intelligence module.

[1490] The artificial intelligence module searches the database for past traffic accident cases and selects the case that best matches the entered accident circumstances.

[1491] 6. Calculation of Fault Percentage

[1492] The artificial intelligence module calculates a reasonable degree of fault based on selected legal precedents.

[1493] The calculation results and details of the legal precedents on which they are based are sent to the server.

[1494] 7. Presentation of the percentage of fault

[1495] The server generates a screen to present the calculated fault ratio and its basis to the user.

[1496] The server distributes this information to the user's terminal so that the user can view it.

[1497] 8. Guidance for legal aid services

[1498] If necessary, the server will display a link directing the user to legal aid services, where they can obtain further professional advice.

[1499] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[1500] Specific examples

[1501] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[1502] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[1503] 2. The server receives this and passes it to the artificial intelligence module.

[1504] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[1505] 4. Determine whether all necessary information is available and generate additional questions if missing, such as "What direction is the other vehicle heading?"

[1506] 5. The user answers the follow-up question with "north direction."

[1507] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[1508] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[1509] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[1510] 9. Users can click on a link to access legal aid services.

[1511] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[1512] The processing flow will be explained below.

[1513] Step 1:

[1514] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user then enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[1515] Step 2:

[1516] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[1517] Step 3:

[1518] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[1519] Step 4:

[1520] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1521] Step 5:

[1522] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[1523] Step 6:

[1524] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[1525] Step 7:

[1526] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[1527] Step 8:

[1528] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[1529] Step 9:

[1530] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[1531] Step 10:

[1532] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[1533] This process allows users to determine a reasonable share of fault, obtain a fair settlement, and take steps to obtain professional legal assistance if necessary.

[1534] Example 1

[1535] 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."

[1536] When a traffic accident occurs, those involved are required to quickly and accurately determine the degree of fault, but doing so efficiently is not easy. Conventional methods require the manual collection and analysis of relevant information, which is time-consuming and labor-intensive. Furthermore, without thorough knowledge of the law and past legal precedents, it is difficult to respond appropriately. As a result, it often takes a long time for victims to receive appropriate legal support. The present invention aims to solve these problems and realize the rapid and accurate calculation of the degree of fault and the provision of legal support when a traffic accident occurs.

[1537] 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.

[1538] In this invention, the server includes a user terminal for inputting the accident situation, an information processing device that receives the accident situation data input from the user terminal, an artificial intelligence module that analyzes the data received by the information processing device and asks the user for any additional information needed, a means for searching for similar past cases based on the information obtained by the artificial intelligence module, a means for calculating the fault ratio based on the similar cases and presenting it to the user, and a means for directing the user to relevant legal support services. This enables the system to not only quickly and accurately calculate the fault ratio when a traffic accident occurs, but also enables the user to quickly receive the necessary legal support.

[1539] "Accident situation" refers to a series of information that constitutes the details of the accident, such as the location of the traffic accident, the date and time, the traffic light conditions, and the direction and speed of each vehicle.

[1540] A "user terminal" is an information processing device used by a user to input information about traffic accidents, and specifically includes devices such as smartphones, tablets, and personal computers.

[1541] The term "information processing device" refers to a computer system that receives, analyzes, and saves data acquired from a user terminal.

[1542] An "artificial intelligence module" is a software or hardware component that has the ability to analyze input accident situation data, identify missing information, and generate additional questions as necessary.

[1543] "Similar cases" are data extracted from legal precedents and examples of traffic accidents that have occurred in the past that most closely resemble the current accident situation.

[1544] "Fault ratio" indicates the percentage of fault of each party involved in a traffic accident, and is an important indicator for determining liability for compensation.

[1545] "Legal aid services" are professional organisations or services that provide expert advice and support on legal issues relating to road traffic accidents.

[1546] The present invention relates to a system for quickly and accurately calculating the degree of fault in a traffic accident and providing legal support to users. Specific embodiments of the system are described below.

[1547] System configuration

[1548] The present invention comprises a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, and a legal support service guidance means.

[1549] The user terminal is an information processing device such as a smartphone, tablet, or PC, and displays a dedicated form for the user to enter information about the traffic accident.

[1550] Specific operation of the system

[1551] Enter the accident details

[1552] When a traffic accident occurs, the user accesses the system from their own terminal.

[1553] The terminal displays a dedicated form for entering accident details, and the user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form.

[1554] Receiving and storing data

[1555] The user completes the input and presses the send button.

[1556] The terminal transmits the transmitted data to the server.

[1557] The server temporarily stores the received data in storage.

[1558] Analysis of the accident situation

[1559] The server passes the temporarily stored data to an artificial intelligence (AI) module.

[1560] The AI ​​module analyzes the received data and extracts important information related to the accident, such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light status, and the speed of each vehicle.

[1561] Missing Information Question

[1562] Based on the analysis results, the artificial intelligence module determines whether additional information is needed to accurately calculate the percentage of fault.

[1563] If there is missing information, the AI ​​module automatically generates additional questions, such as "What direction is the other vehicle heading?"

[1564] The server transmits the generated follow-up question to the user terminal.

[1565] The user answers the additional questions and sends them to the server via the terminal.

[1566] Search for similar cases

[1567] The server confirms that all necessary data is available and sends the data back to the artificial intelligence module.

[1568] The artificial intelligence module searches a database of past traffic accident cases and extracts the case that best matches the input accident situation.

[1569] Calculation of fault ratio

[1570] The AI ​​module calculates the percentage of fault based on selected legal precedents, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight."

[1571] The artificial intelligence module sends the results of this calculation and details of the legal precedents on which it is based to the server.

[1572] Presentation of the percentage of fault

[1573] The server generates a screen to present the received calculation results to the user.

[1574] The screen generated by the server is sent to the user's terminal, allowing the user to check the calculated fault ratio and its basis.

[1575] Guidance for legal aid services

[1576] The server displays a link on the screen that directs the user to legal assistance services if necessary.

[1577] When users click on the link, they will be taken to a booking or inquiry page for legal aid services.

[1578] Specific examples

[1579] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[1580] 1. The user enters information such as the location of the accident, the traffic light conditions, and the speed of the other vehicle into a dedicated form on the device ("Collision occurred while turning right at an intersection," "The traffic light was green," "The other vehicle's speed was 60 km / h").

[1581] 2. The terminal sends the entered data to the server.

[1582] 3. The server receives this data and stores it temporarily in storage.

[1583] 4. The server passes the data to an artificial intelligence module, which analyzes the circumstances of the accident and extracts important keywords such as "turn right," "go straight," and "green light."

[1584] 5. Based on the analysis results, the artificial intelligence module determines what information is missing and automatically generates additional questions, such as, "What direction is the other vehicle heading?"

[1585] 6. The server sends an additional question to the user's terminal, and the user answers "north."

[1586] 7. The server receives the answer and sends it back to the artificial intelligence module, which searches for similar precedents.

[1587] 8. The artificial intelligence module calculates the percentage of fault based on precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[1588] 9. The server generates a screen that presents the calculation results in an easy-to-view format for the user and sends it to the user's terminal.

[1589] 10. If necessary, the server will display a link to a legal assistance service on the screen, allowing the user to receive professional assistance through that link.

[1590] Example prompt sentence:

[1591] "You collide with a vehicle going straight while turning right at an intersection. The light is green and the other vehicle is traveling at 60km / h. Please calculate the percentage of fault in this situation."

[1592] In this way, the invention helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[1593] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1594] Step 1:

[1595] When a traffic accident occurs, the user accesses the system from their own terminal. The terminal displays a dedicated form for entering the accident details. The user enters the details of the accident (location, date and time, traffic light conditions, vehicle direction, speed, etc.) into the form and submits the input data. This data is then sent to the server.

[1596] input:

[1597] Accident details (location, date and time, traffic signal conditions, vehicle direction, speed, etc.)

[1598] output:

[1599] Data sent to the server

[1600] Specific behavior:

[1601] The user fills out the form and presses the submit button.

[1602] Step 2:

[1603] The server receives the accident situation data sent from the user terminal and temporarily stores it in storage, which is used for subsequent processing.

[1604] input:

[1605] Accident situation data sent from the user device

[1606] output:

[1607] Data stored in storage

[1608] Specific behavior:

[1609] The server receives the data and temporarily stores it in a database.

[1610] Step 3:

[1611] The server passes the temporarily stored data to an artificial intelligence (AI) module, which analyzes the received data and extracts important information about the accident (such as whether the vehicle turned right at the intersection, whether it was going straight, the traffic light conditions, and the speed of each vehicle).

[1612] input:

[1613] Temporarily saved accident situation data

[1614] output:

[1615] Analysis results (important information extracted)

[1616] Specific behavior:

[1617] The server sends the data to the AI ​​module, which analyzes the information and extracts important keywords.

[1618] Step 4:

[1619] Based on the analysis results, the AI ​​module determines whether additional information necessary to accurately calculate the percentage of fault is missing. If so, the AI ​​module automatically generates additional questions and passes them to the server.

[1620] input:

[1621] Analysis results

[1622] output:

[1623] Additional questions (if necessary)

[1624] Specific behavior:

[1625] The AI ​​module identifies missing information and generates follow-up questions if necessary.

[1626] Step 5:

[1627] The server transmits the generated follow-up question to the user terminal, and the user answers the follow-up question and transmits the answer to the server via the terminal.

[1628] input:

[1629] Additional questions

[1630] output:

[1631] User Answers

[1632] Specific behavior:

[1633] The server sends a question, and the user enters and sends the answer.

[1634] Step 6:

[1635] The server receives additional responses from the user and sends them back to the AI ​​module, which searches a database of past traffic accident cases and extracts the case that best matches the entered accident situation.

[1636] input:

[1637] Additional user answers

[1638] output:

[1639] Similar precedents

[1640] Specific behavior:

[1641] The server sends the additional answers to the AI ​​module, which then searches the database to extract similar cases.

[1642] Step 7:

[1643] The AI ​​module calculates the percentage of fault based on the selected legal precedents, and sends the calculation results and details of the legal precedents on which they are based to the server.

[1644] input:

[1645] Similar precedents

[1646] output:

[1647] Calculation results and basis of fault ratio

[1648] Specific behavior:

[1649] The AI ​​module calculates the degree of fault based on legal precedent and sends the results to the server.

[1650] Step 8:

[1651] The server generates a screen to display the received calculation results to the user, and sends it to the user's terminal. The user can check the calculated fault ratio and its basis.

[1652] input:

[1653] Calculation results and basis of fault ratio

[1654] output:

[1655] User presentation screen

[1656] Specific behavior:

[1657] The server generates a screen and sends it to the user's terminal to present the information.

[1658] Step 9:

[1659] The server displays a link on the screen to direct the user to the legal aid service as needed, and when the user clicks the link, the user can access the reservation page or inquiry page of the legal aid service.

[1660] input:

[1661] User presentation screen

[1662] output:

[1663] Links to legal aid services

[1664] Specific behavior:

[1665] The server displays a link that the user clicks to access the legal assistance service.

[1666] In this way, the system helps users rationally understand the degree of fault in a traffic accident and easily obtain professional assistance if necessary.

[1667] (Application example 1)

[1668] 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."

[1669] Traffic accidents involving autonomous vehicles generate unique data (camera footage, LiDAR data, etc.) that differs from those generated by human drivers, making them difficult to handle with conventional accident analysis systems. Furthermore, there is a need for rapid and accurate calculations of fault and legal support after accidents, but current systems require a lot of manual input and analysis, which is time-consuming and can lack accuracy. Therefore, there is a need for a system that can effectively incorporate data specific to autonomous vehicles, quickly and accurately calculate fault, and provide appropriate legal support to users.

[1670] 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.

[1671] In this invention, the server includes means for automatically acquiring sensor data from the autonomous vehicle as accident situation data, means for presenting accident analysis results on a head-mounted display, a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for any additional information required, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the degree of fault based on the similar legal precedents and presenting it to the user, and means for directing the user to relevant legal support services. This enables rapid and accurate analysis of autonomous vehicle accidents, calculation of the degree of fault, and provision of legal support services.

[1672] "Accident situation data" refers to information including the location, date and time, traffic light conditions, vehicle direction and speed, and related sensor data when a traffic accident occurs.

[1673] A "user terminal" is an electronic device, such as a smartphone, tablet, or PC, through which a user inputs data.

[1674] The "server" is a computer system that receives, analyzes, and stores accident situation data sent from user terminals.

[1675] An "artificial intelligence module" is software that analyzes received data, identifies any additional information needed, and performs advanced processing such as calculating fault percentages.

[1676] A "head-mounted display" is a device that a user wears on their head and displays information on a display.

[1677] "Sensor data" is digital information acquired by sensors (e.g., cameras, LiDAR, etc.) installed in autonomous vehicles.

[1678] "Similar precedents" refer to precedents of traffic accidents that occurred in the past and are similar to the current accident situation.

[1679] "Additional questions" are questions generated by the artificial intelligence module to supplement missing parts of the accident situation data.

[1680] "Legal aid service" means a professional organisation or service that provides legal advice and assistance in relation to road traffic accidents.

[1681] This invention is a system for analyzing traffic accident situations involving autonomous vehicles and accurately calculating the degree of fault. The system works by having the user input the accident situation using a smartphone or head-mounted display (HMD), and the server receives the data and analyzes it using an artificial intelligence module.

[1682] First, the user uses a device to input accident situation data. The input data includes sensor data acquired from the autonomous vehicle, such as camera footage and LiDAR data. This makes it possible to grasp the details of the accident situation, which was difficult to do using conventional methods.

[1683] The server then receives and temporarily stores the accident situation data sent from the user's device. The received data is then passed to an AI module for analysis. The AI ​​module extracts key elements from the data analysis process (e.g., collisions during right turns, traffic light conditions, and the speed of each vehicle).

[1684] If additional information is needed based on the analysis results, the AI ​​module will generate additional questions and send them to the server, which will then display them on the user's device and prompt the user to answer them. This process will allow for a complete understanding of the accident situation.

[1685] Once all the necessary data is collected, the server uses an artificial intelligence module to access the data in the database to search for similar past cases. Once a match is found, the fault ratio is calculated based on that case and the result is presented to the user by the server.

[1686] Finally, the system will display links to direct users to relevant legal aid services, allowing them to quickly receive professional legal assistance when needed.

[1687] During this process, the user can visually check the accident analysis results in real time using an HMD, which allows them to more intuitively understand the accident situation and the degree of fault.

[1688] Specific examples

[1689] For example, consider the case where an autonomous vehicle collides with a vehicle traveling straight while turning right at an intersection. The user inputs and transmits the accident details, such as "Intersection A," "2023-10-01 14:30," "Light is green," "Right-turn speed 15 km / h," and "Straight-moving vehicle speed 60 km / h." The server receives this data and passes it to an artificial intelligence module. After analysis, the missing information, "the direction of the other vehicle," is presented, and the user answers "northbound." The server then searches for similar cases and calculates the fault ratio: "70% for the right-turning vehicle, 30% for the straight-moving vehicle," which is presented to the user. After viewing the results, the user can easily access legal support services by clicking the displayed link.

[1690] Prompt Sentence Examples

[1691] "My self-driving vehicle collided with another vehicle at intersection A. Please analyze the following accident information and tell me the percentage of fault.

[1692] Location: Intersection A

[1693] Time: 2023-10-01 14:30

[1694] Signal status: Green

[1695] Vehicle direction: Turn right, go straight

[1696] Vehicle speed: Right-turning vehicles 15km / h, straight-going vehicles 60km / h

[1697] Sensor Data:

[1698] Camera footage: Image data

[1699] LiDAR: LiDAR data

[1700] In this way, it becomes possible to quickly and accurately analyze accidents involving autonomous vehicles and calculate the degree of fault.

[1701] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1702] Step 1:

[1703] The user inputs accident situation data using a terminal. The input data includes the location of the accident, date and time, traffic light conditions, vehicle direction, speed, and sensor data from the autonomous vehicle. The input data is then sent from the terminal to the server.

[1704] Step 2:

[1705] The server receives the accident situation data sent from the user terminal and temporarily stores it. The server then passes the received data to the artificial intelligence module. At this point, the input is the accident situation data entered by the user, and the output is the temporarily stored data.

[1706] Step 3:

[1707] The AI ​​module analyzes the received accident situation data and extracts important elements (e.g., "collision while turning right," "green light," "right turn speed 15 km / h," "straight vehicle speed 60 km / h," etc.). This extraction process organizes the data and identifies important information. The input is the received data, and the output is the analysis results, which are important elements.

[1708] Step 4:

[1709] Based on the analysis results, the AI ​​module determines whether additional information is needed to accurately calculate the degree of fault. If any information is missing, it generates additional questions. The generated questions are sent to the server. The input is the analysis results, and the output is the additional questions.

[1710] Step 5:

[1711] The server sends the generated follow-up question to the user terminal. The user terminal displays the question to the user, and the user inputs an answer to the follow-up question. The input is the follow-up question, and the output is the user's follow-up answer.

[1712] Step 6:

[1713] The server receives the user's additional answer and resends it to the AI ​​module. The AI ​​module analyzes the additional answer and searches the database for similar past cases. The input is the user's additional answer, and the output is the searched similar cases.

[1714] Step 7:

[1715] The AI ​​module calculates a reasonable percentage of fault based on the searched similar cases. The calculation results are sent to the server. The input is the similar cases, and the output is the calculated percentage of fault.

[1716] Step 8:

[1717] The server generates a screen to present the calculated fault ratio and its basis to the user. The generated screen is sent to the user's terminal so that the user can check it. The input is the calculated fault ratio, and the output is the generated presentation screen.

[1718] Step 9:

[1719] The server displays a link to direct the user to a legal aid service as needed. When the user clicks on this link, they can access the legal aid service's reservation page or inquiry page. The input is the legal aid service URL, and the output is a screen displaying the link.

[1720] In this way, by linking the server, terminal, and user, it is possible to analyze the accident situation and calculate the degree of fault quickly and accurately, and provide appropriate legal support.

[1721] 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.

[1722] MODE FOR CARRYING OUT THE INVENTION

[1723] The present invention is composed of a system including a user terminal, a server, an artificial intelligence module, a database, a search means, a fault ratio calculation means, a legal support service guidance means, and an emotion engine. The specific operation form of this system and the processing of its programs are described below.

[1724] System Overview

[1725] 1. Enter the accident details

[1726] When a traffic accident occurs, users access the system using their own devices (smartphones, tablets, computers, etc.).

[1727] The device displays a dedicated form for entering accident details, and the user can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[1728] 2. Receipt and storage of data

[1729] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[1730] 3. Analysis of the accident situation

[1731] The server passes the stored data to the artificial intelligence module.

[1732] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light is green."

[1733] 4. Missing Information Questions

[1734] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1735] The server displays the generated question on the user's device. The user answers the question and sends the information to the server via the device. For example, the user might answer, "The speed of the other vehicle is 60 km / h."

[1736] 5. Operation of the Emotion Engine

[1737] The emotion engine analyzes the user's emotional state from their responses and voice input, assessing, for example, their stress or anxiety levels and providing information on relaxation techniques or psychological support as needed.

[1738] The server displays additional support information to the user based on instructions from the emotion engine.

[1739] 6. Search for similar cases

[1740] After the server has all the necessary data, it sends it to the artificial intelligence module.

[1741] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[1742] 7. Calculation of Fault Percentage

[1743] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[1744] 8. Presentation of the percentage of fault

[1745] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The server then distributes this information to the user's device.

[1746] 9. Guidance for legal aid services

[1747] If necessary, the server also displays a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. Clicking on the link takes the user to a booking or enquiry page for the legal aid service.

[1748] Specific examples

[1749] For example, if a user is turning right at an intersection and is hit by a vehicle going straight:

[1750] 1. The user inputs the accident details, such as "collision while turning right at an intersection," "the traffic light was green," and "the other vehicle's speed was 60 km / h."

[1751] 2. The server receives this and passes it to the artificial intelligence module.

[1752] 3. The artificial intelligence module analyzes the data and extracts keywords such as "turn right," "go straight," and "green light."

[1753] 4. Determine whether all necessary information is available and generate follow-up questions if missing (e.g., "What direction is the other vehicle heading?").

[1754] 5. The user answers the follow-up question with "north direction."

[1755] 6. The server sends this information to an artificial intelligence module, which searches for similar cases.

[1756] 7. The artificial intelligence module calculates the percentage of fault based on legal precedent that "70% of the fault is with the vehicle turning right, 30% is with the vehicle going straight" and sends the result to the server.

[1757] 8. The server presents the result of "70%:30%" to the user and displays a link to a specialist institution.

[1758] 9. Users can click on a link to access legal aid services.

[1759] Through this process, users can determine the reasonable degree of fault and, if necessary, receive support from the emotion engine. Furthermore, if further professional assistance is required, users will be efficiently directed to the appropriate service, ensuring a fair and appropriate solution.

[1760] The processing flow will be explained below.

[1761] Step 1:

[1762] The user accesses the system through a website or application. The terminal displays a form for entering the details of a traffic accident. The user enters details of the accident, such as the location, date and time of the accident, traffic light conditions, vehicle direction, and speed, using text and diagrams.

[1763] Step 2:

[1764] The device sends the entered accident situation data in text and image format to the server, which temporarily stores the received data and prepares to proceed to the next step.

[1765] Step 3:

[1766] The server passes the saved data to an AI module, which analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key factors related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "the traffic light was green."

[1767] Step 4:

[1768] Based on the analysis results, the AI ​​module determines whether there is any missing information to accurately calculate the fault ratio. If there is missing information, the AI ​​module generates additional questions (e.g., "What was the speed of the other vehicle?", "What is the damage to the vehicle?") and passes them to the server.

[1769] Step 5:

[1770] The server displays the generated question to the user. The user answers the question and sends the information to the server via the terminal. For example, the user answers, "The speed of the other vehicle is 60 km / h."

[1771] Step 6:

[1772] The server passes any additional information sent by the user back to the AI ​​module, which then verifies that all necessary information is available and moves on to the next step.

[1773] Step 7:

[1774] The AI ​​module searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data.

[1775] Step 8:

[1776] The AI ​​module analyzes the details of similar cases and calculates the most reasonable percentage of fault from them. The calculated percentage of fault and the case data showing the basis for it are sent to the server.

[1777] Step 9:

[1778] The server generates a screen to show the calculated fault percentage and detailed reasons for it to the user. The displayed content might be something like "70% fault for the right-turning vehicle, 30% fault for the straight-moving vehicle." The server then distributes this information to the user's device.

[1779] Step 10:

[1780] The emotion engine analyzes the user's input data and voice to identify their emotional state, for example determining their stress or anxiety level through voice analysis.

[1781] Step 11:

[1782] Based on the emotional state obtained from the emotion engine, the server provides additional information on relaxation techniques and psychological support if the user is experiencing high levels of stress or anxiety.

[1783] Step 12:

[1784] If necessary, the server may display a link on the page that directs the user to a legal aid service, where they can obtain further professional advice. When the user clicks on the link, they are taken to a booking or enquiry page for the legal aid service.

[1785] Example 2

[1786] 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."

[1787] Conventional traffic accident processing systems require complicated input and analysis of accident circumstances, making it difficult for users to calculate the degree of fault and access the legal support they need. Furthermore, they do not adequately consider the emotional state of the parties involved in the accident, and psychological support is rarely provided. This makes it difficult for users to understand the appropriate degree of fault and quickly obtain the necessary legal support, and also creates a significant mental burden after an accident.

[1788] 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.

[1789] In this invention, the server includes a means for analyzing the user's emotional state and providing information on psychological support as needed, a means for directing the user to relevant legal support services, and a means for passing the input accident situation data to an artificial intelligence module for analysis, thereby enabling the user to accurately determine the degree of fault in the accident and receive professional legal support, as well as psychological support according to the user's emotional state.

[1790] "User terminal" refers to an electronic device used by a user to input the details of a traffic accident, including devices such as smartphones, tablets, and personal computers.

[1791] "Server" refers to a central processing unit that receives, stores, and analyzes accident situation data sent from user terminals.

[1792] "Artificial intelligence module" refers to a software component that analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault.

[1793] "Means for searching for similar cases" refers to the function in which the artificial intelligence module searches for past traffic accident cases in the database based on the input accident situation data and identifies similar cases.

[1794] "Means for calculating the degree of fault" refers to the function in which the artificial intelligence module calculates a reasonable degree of fault based on similar precedents and returns the results to the server.

[1795] "Legal aid link" refers to a feature that provides links or information to help users access appropriate legal aid services.

[1796] "Means for analyzing emotional state" refers to a function that analyzes emotions such as stress and anxiety from the user's responses and voice input, and provides information on psychological support based on the results.

[1797] MODE FOR CARRYING OUT THE INVENTION

[1798] The present invention relates to a system for understanding the circumstances of a traffic accident and calculating the degree of fault, and is a system that includes a user terminal, a server, an artificial intelligence module, a database, an emotion analysis engine, a search means, a means for calculating the degree of fault, and a means for guiding legal support services.

[1799] System configuration

[1800] This system is configured as follows:

[1801] 1. User Device:

[1802] An electronic device used by users to input the circumstances of a traffic accident. This includes devices such as smartphones, tablets, and PCs. Users use these devices to access the system and enter details of the accident.

[1803] 2. Server:

[1804] This is a central processing unit that receives, stores, and analyzes accident situation data sent from user devices. The server temporarily stores the received data and passes it to an artificial intelligence module or emotion analysis engine as needed.

[1805] 3. Artificial Intelligence Module:

[1806] This software component analyzes the data received by the server, understands the circumstances of the accident, asks for additional information, and calculates the degree of fault. Specifically, it extracts accident keywords, searches for related information, and generates additional questions if any information is missing.

[1807] 4. Database:

[1808] This is a database for storing past traffic accident precedents and related information, and is used by the artificial intelligence module to calculate the degree of fault.

[1809] 5. Sentiment Analysis Engine:

[1810] The engine analyzes the user's emotional state based on their answers and voice input, and provides information on psychological support based on the results. For example, it assesses stress and anxiety levels and provides relaxation techniques and psychological support.

[1811] 6. Legal Aid Service Guidance:

[1812] It is a means of providing links and information to help users access appropriate legal aid services, so that they can quickly get the legal advice they need.

[1813] Operational Overview

[1814] When a user is involved in a traffic accident, they use their device to input details of the accident (such as the location, date and time of the accident, and traffic light conditions). This data is sent to the server and temporarily stored. The server then passes this data to an artificial intelligence module, which analyzes the data and extracts key elements. Based on the analysis results, if any information is missing, additional questions are generated and sent to the user.

[1815] Furthermore, an emotion analysis engine analyzes the user's emotional state and provides psychological support information as needed. After all data is collected, an artificial intelligence module searches the database for past traffic accident cases and selects the most similar case that best matches. It then calculates the reasonable degree of fault based on the similar case and presents the results to the user. Links to legal support services are also provided as needed.

[1816] Specific examples

[1817] For example, if a user collides with a vehicle going straight while turning right at an intersection, the user inputs the accident details as follows: "Collision occurred while turning right at the intersection," "The light was green," "The other vehicle's speed was 60 km / h," etc. This data is received by the server and passed to an artificial intelligence module. The module analyzes the data, extracts keywords such as "right turn," "going straight," and "green light," and determines whether the necessary information is available.

[1818] If there is insufficient information, the system generates an additional question, such as "What direction was the other vehicle traveling?" If the user answers "north," the information is sent back to the server, and the AI ​​module searches the database for similar cases and calculates the percentage of fault, such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight." The results are then presented to the user, along with a link to legal support services.

[1819] Prompt Sentence Examples

[1820] You can check the system's processing by entering the following prompt sentence into the generative AI model:

[1821] example:

[1822] Please calculate the percentage of fault in an accident where a vehicle collided with a vehicle going straight while turning right at an intersection. The accident circumstances were as follows: the vehicle collided while turning right at an intersection, the traffic light was green, the other vehicle was traveling at 60 km / h, and the other vehicle was heading north.

[1823] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1824] Step 1: Enter the accident details

[1825] When a traffic accident occurs, the user accesses the system using their own device (smartphone, tablet, PC, etc.). The device displays a dedicated form for entering the details of the accident. The user enters the location of the accident, date and time, traffic light conditions, vehicle direction of travel, speed, etc. in text or diagrams. Input data such as "XX intersection, Chiyoda-ku, Tokyo," "March 15, 2023, 2:30 p.m.," "traffic light is green," "vehicle direction of travel is west," and "speed is 30 km / h" is sent to the server. Specific operations of the device include displaying the input form and sending the data.

[1826] Step 2: Receiving and storing data

[1827] The server receives the accident situation data sent from the terminal. The received data is temporarily saved in text or image format, and is then prepared to proceed to the next step. Examples of saved data include "collision while turning right at an intersection," "the traffic light is green," and "the other vehicle's speed is 60 km / h." The specific operations of the server include data reception processing and data storage processing in a database.

[1828] Step 3: Analysis of the accident situation

[1829] The server passes the stored data to an artificial intelligence module. The artificial intelligence module analyzes the input data and understands the accident situation. Specifically, it extracts key elements related to the accident. For example, it extracts keywords such as "intersection," "right turn," "straight ahead," "green light," and "60 km / h." The input data in this process is the stored accident situation data, and the output is the extracted keywords. Specific operations of the server include data transfer processing.

[1830] Step 4: Missing information question generation

[1831] Based on the analysis results, the artificial intelligence module determines whether any information is missing to accurately calculate the degree of fault. If any information is missing, it generates an additional question (e.g., "What direction is the other vehicle traveling?") and passes it to the server. The server displays the generated question on the user's device. The user answers this and sends the information back to the server via the device. For example, the user answers, "The other vehicle is traveling north." The input data is the analysis results, and a question about the missing information is output. The specific operations of the device include displaying the question and sending the answer.

[1832] Step 5: Emotion Engine in Action

[1833] The emotion analysis engine analyzes the user's emotional state from their responses and voice input. For example, it evaluates their state as "stressed," "anxious," or "relaxed." If necessary, it provides information on relaxation techniques or psychological support. The server follows the instructions of the emotion analysis engine and displays additional support information on the user's device. The specific operations of the emotion engine include analyzing the emotional state and generating support information. The specific operations of the server include displaying support information based on the results of the emotion analysis.

[1834] Step 6: Search for similar cases

[1835] After the server has gathered all the necessary data, it sends it to the AI ​​module, which searches the database for past traffic accident cases and selects several similar cases that most closely match the input accident situation data. The input data is the complete accident situation data, and the output is a list of similar cases. The specific operations of the server include sending data and receiving results.

[1836] Step 7: Calculate the percentage of fault

[1837] The AI ​​module analyzes the details of similar cases and calculates the most reasonable fault ratio from them. For example, a result such as "70% fault for the vehicle turning right, 30% fault for the vehicle going straight" is sent to the server. The input data is the details of similar cases, and the output is the calculated fault ratio. The specific operations of the server include receiving the calculation results and saving the data.

[1838] Step 8: State the percentage of fault

[1839] The server generates a screen to present the calculated fault percentage and detailed reasons to the user. The presented information will be information such as "70% fault for vehicles turning right, 30% for vehicles going straight" and will be distributed to the user's terminal. The input data is the fault percentage calculation result, and the output is the screen displayed to the user. The specific operations of the server include generating the screen and distributing the data.

[1840] Step 9: Referral to legal aid services

[1841] If necessary, the server also displays a link on the page that directs the user to a legal aid service, allowing the user to obtain further professional advice. When the user clicks on the link, they can access the legal aid service's reservation or inquiry page. The input data is the user's request for assistance, and the output is the link information for the legal aid service. The specific operations of the server include generating the link and directing the user to the page.

[1842] (Application example 2)

[1843] 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."

[1844] When a traffic accident occurs, especially in an autonomous vehicle, it is necessary to analyze the accident situation quickly and accurately and calculate the degree of fault. However, conventional systems rely on human input, which can lead to delays in inputting the accident situation or analysis being based on incomplete information, making it difficult to respond quickly. Another issue is the lack of information and advice necessary to receive legal support.

[1845] 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 a user terminal for inputting the accident situation, means for receiving the accident situation data input from the user terminal, an artificial intelligence module for analyzing the data received by the server and asking the user for necessary additional information, means for searching for similar past legal precedents based on the information obtained by the artificial intelligence module, means for calculating the fault ratio based on the similar legal precedents and presenting it to the user, means for directing the user to relevant legal support services, means for automatically acquiring accident data of autonomous vehicles and calculating the fault ratio based on the data, and means for providing legal advice related to the autonomous vehicle accident. This enables quick and accurate analysis of the accident situation and calculation of the fault ratio, and enables necessary legal support to be provided promptly.

[1846] A "user terminal for inputting accident details" is a device that allows a user to input details of a traffic accident when one occurs.

[1847] "Server" refers to a central computer system that receives, analyzes, and processes accident situation data sent from user terminals.

[1848] The "artificial intelligence module" is a program built into the server that analyzes the received data, identifies any additional information needed, and presents it to the user.

[1849] The "means of searching for similar past cases" is a function that searches a database of past traffic accident cases to find cases similar to the current accident situation.

[1850] The "means for calculating the degree of fault" is a module that calculates the degree of responsibility in the current accident based on similar precedents that have been searched.

[1851] "Means for presenting to the user" refers to an interface that displays the calculated fault ratio and necessary information to the user.

[1852] "Legal assistance means" means a feature that provides links or information that directs users to professional services for legal advice or assistance.

[1853] "Means for automatically acquiring accident data from autonomous vehicles" refers to a device that automatically collects accident information from sensors and logging systems built into autonomous vehicles.

[1854] "Means for providing legal advice related to accidents involving autonomous vehicles" refers to a function that provides users with appropriate legal advice depending on the circumstances of an accident involving an autonomous vehicle.

[1855] The present invention is a system that includes a user terminal for inputting accident details, a server, an artificial intelligence module, a means for searching for similar past cases, a means for calculating the degree of fault, a means for directing to legal support services, a means for automatically acquiring accident data of autonomous vehicles, and a means for providing legal advice related to accidents involving autonomous vehicles.

[1856] System Overview

[1857] User Device

[1858] The user device provides an interface that allows users to manually input accident details. The device can be a smartphone, tablet, or PC, and a dedicated form for entering detailed accident information is displayed. Users can enter information such as the location of the accident, date and time, traffic light conditions, vehicle direction and speed in text or diagrams.

[1859] server

[1860] The server receives and temporarily stores the accident situation data sent from the user terminal, and then passes the received data to the artificial intelligence module for analysis.

[1861] Artificial Intelligence Module

[1862] The AI ​​module analyzes the input data and understands the circumstances of the accident. Specifically, it extracts key elements related to the accident, such as "turning right at an intersection," "collision with a vehicle going straight," and "green light." It also determines whether additional information is needed, and generates additional questions if there is insufficient information. Examples of missing information include "What was the speed of the other vehicle?" and "What is the damage to the vehicle?"

[1863] Search for similar cases and calculate the percentage of fault

[1864] Based on the information obtained by the AI ​​module, the server searches the database for past traffic accident cases. It selects the most similar cases and calculates the percentage of fault based on them. This percentage of fault and the case data showing the basis for it are presented to the user.

[1865] Legal Aid Services

[1866] The user is presented with a percentage of fault and detailed reasons for it, and if necessary, a link to a legal aid service is also provided, allowing the user to seek further professional advice.

[1867] Data acquisition from autonomous vehicles

[1868] Autonomous vehicles automatically collect accident situation data using internal sensors and logging systems and send it to a server, allowing detailed accident information to be transmitted to the server quickly and accurately.

[1869] Examples of prompt statements

[1870] Example of what the user enters as a prompt:

[1871] I collided with a vehicle going straight while turning right at an intersection. The light was green, and my vehicle was turning right while the other vehicle was going straight. The accident occurred at an intersection in Shibuya Ward, Tokyo. The other vehicle was traveling at a speed of approximately 60 km / h, damaging the front of my vehicle. I would like your help calculating the degree of fault so that I can receive appropriate legal assistance.

[1872] By combining these elements, users can quickly and accurately input accident details, and based on the results, appropriate fault allocation and legal support information can be provided. In addition, data acquisition from autonomous vehicles can speed up accident analysis and legal support for autonomous driving technology.

[1873] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1874] Step 1:

[1875] The user terminal inputs the accident situation data.

[1876] Input: location of accident, date and time, traffic light status, vehicle direction, speed, etc.

[1877] Data manipulation: converting data into text or graphical formats

[1878] Output: Accident situation data is entered into the user's terminal.

[1879] Specific operation: The user inputs the necessary information into a dedicated form using a smartphone or tablet. Example: "While turning right at an intersection in Shibuya Ward, Tokyo, a collision occurred with a vehicle going straight. The traffic light was green and the speed of the other vehicle was approximately 60 km / h."

[1880] Step 2:

[1881] The user terminal transm...

Claims

1. A user terminal for inputting accident details; a server that receives accident situation data input from the user terminal; an artificial intelligence module that analyzes the data received by the server and queries the user for any additional information needed; A means for searching for similar past cases based on the information obtained by the artificial intelligence module; A means for calculating the percentage of fault based on the similar precedent and presenting it to the user; and means for directing said user to relevant legal assistance services.

2. The system of claim 1 , wherein the artificial intelligence module identifies missing information and generates follow-up questions.

3. The system according to claim 1, further comprising a database for searching similar past precedents.

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