system

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting driving data from a plurality of sensors and a GPS mounted on a vehicle; means for preprocessing the driving data and converting the driving data into an analyzable data format; means for transmitting the preprocessed data to a server via a network; means for storing the transmitted data in a database and calculating an evaluation score using an analysis algorithm; means for calculating an insurance premium for each driver based on the evaluation score; and means for notifying the calculated insurance premium in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional methods for calculating automobile insurance premiums primarily rely on basic driver information (e.g., years of driving experience and accident history), and are unable to accurately reflect a driver's actual driving skills, driving habits, or compliance with traffic rules. As a result, it is difficult to provide fair and appropriate insurance premiums, and there is a lack of motivation to promote safe driving. The present invention aims to solve these problems. [Means for solving the problem]

[0005] The present invention provides a system including: means for collecting driving data from multiple sensors and GPS mounted on a vehicle; means for preprocessing the driving data and converting it into an analyzable data format; means for transmitting the preprocessed data to a server via a network; means for storing the transmitted data in a database and calculating an evaluation score using an analysis algorithm; means for calculating insurance premiums for each driver based on the evaluation score; and means for notifying the calculated insurance premiums in real time. This system is expected to promote safe driving by calculating fair and appropriate insurance premiums based on actual driving skills, driving habits, and compliance with traffic rules.

[0006] A "sensor" is a device attached to a vehicle that detects and collects driving data (such as sudden braking, sudden acceleration, seat belt use, and turn signal use).

[0007] "GPS" is a global positioning system used to collect vehicle location information, driving route, and driving speed.

[0008] "Driving data" refers to data related to the driver's driving behavior (e.g., driving distance, speed, sudden braking, sudden acceleration, etc.).

[0009] "Pre-processing" refers to the data processing process used to convert collected driving data into an analyzable format.

[0010] A "network" is a communications infrastructure that connects terminals and servers and transmits and receives data.

[0011] A "server" is a computer system responsible for receiving, storing, and analyzing driving data.

[0012] "Database" means a system that stores and manages operational data in a structured format.

[0013] An "analysis algorithm" is a calculation method for evaluating collected driving data and calculating an evaluation score.

[0014] The "evaluation score" is the result of a numerical evaluation of driving skills, driving habits, and compliance with traffic rules.

[0015] "Insurance premium" is the amount of car insurance calculated based on the evaluation score.

[0016] "Notification" is a means of transmitting information to the user to inform them of the calculated insurance premium.

[0017] "Terminal" means a device in a vehicle that displays driving data and insurance premium notifications to the user. [Brief explanation of the drawings]

[0018] [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

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] 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).

[0022] 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.

[0023] 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.

[0024] 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.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] 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).

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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."

[0039] The automobile insurance fair amount calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on a vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver. Specific embodiments are described below.

[0040] Terminal handling

[0041] Data collection

[0042] The device collects driving data (location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt usage status, turn signal usage status, etc.) from the vehicle's GPS and multiple sensors. For example, while the vehicle is driving, the GPS constantly records location information, and the brake sensor detects the number of sudden braking events.

[0043] Data Preprocessing

[0044] The device preprocesses the collected driving data and converts it into the required format, including cleaning up the data by filtering out noise and extracting key indicators such as mileage and frequency of hard braking.

[0045] Data transmission

[0046] The device then transmits the pre-processed data to a server over the Internet, which is done periodically to ensure communication stability and security.

[0047] Server Processing

[0048] Data reception

[0049] The server receives the driving data sent from the device and stores it in a database. For example, the data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[0050] Data analysis

[0051] The server analyzes the received driving data using an analytical algorithm. In this analysis, evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. For example, if user A's driving distance in one day is 50 km and he or she braked suddenly five times, the data is evaluated and a driving skill score is calculated.

[0052] Insurance premium calculation

[0053] The server calculates an appropriate insurance premium based on the analysis results. For example, the insurance premium calculated based on User A's evaluation score is 3,000 yen / month.

[0054] Insurance premium notification

[0055] The server generates data to notify the user of the calculated insurance premium in real time and transmits it to the terminal.

[0056] User Action

[0057] Receive insurance premium notifications

[0058] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user. For example, "Current insurance premium: 3000 yen / month" is displayed on the terminal screen.

[0059] User Feedback

[0060] Users can review their driving style based on the notified insurance premium. For example, if they know that they frequently apply sudden brakes, they will be able to develop driving habits to avoid this and become more conscious of safe driving.

[0061] As described above, this system collects, pre-processes, and analyzes driving data in real time, enabling appropriate insurance premiums to be calculated and notified. This is expected to reflect drivers' driving behavior more fairly and promote safe driving.

[0062] The processing flow will be explained below.

[0063] Step 1: Data collection

[0064] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking events, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is being used.

[0065] Step 2: Data Preprocessing

[0066] The device preprocesses the collected data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by the time of recording. For example, it extracts information such as driving distance, frequency of sudden braking, and whether or not there was a speeding violation, and compiles it into a database.

[0067] Step 3: Send data

[0068] The device transmits the pre-processed data to a server over the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data during transmission.

[0069] Step 4: Receiving Data

[0070] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, information such as User A's driving distance, number of sudden braking attempts, and speeding violations are registered in the database.

[0071] Step 5: Data analysis

[0072] The server uses an analysis algorithm to analyze the received driving data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day and brakes suddenly five times during that time, the evaluation score is calculated based on that data.

[0073] Step 6: Premium calculation

[0074] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A's overall evaluation score is low, the insurance premium may be set high.

[0075] Step 7: Premium Notification

[0076] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for screen display and sent.

[0077] Step 8: Receive premium notification

[0078] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0079] Step 9: User Feedback

[0080] Users can review their driving style based on the insurance premiums they receive. For example, if a user sees the notification and decides that their insurance premiums are high because they brake suddenly a lot, they can make an effort to drive more calmly in the future to lower their insurance premiums.

[0081] Example 1

[0082] 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."

[0083] Conventional automobile insurance systems have difficulty accurately reflecting a driver's driving behavior, and insurance premiums are often set at a flat rate. This creates the problem that drivers who are mindful of safe driving are not fairly evaluated. Furthermore, conventional systems are unable to notify insurance premiums in real time, which can result in drivers missing an opportunity to review their driving behavior.

[0084] 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.

[0085] In this invention, the server includes a means for collecting driving data from multiple sensors and GPS mounted on the vehicle, a means for converting the data into an analyzable data format by noise filtering and extracting key indicators, and a means for transmitting the data to the server via the Internet, which makes it possible to calculate appropriate insurance premiums for each driver and notify them in real time.

[0086] "Driving data" refers to data such as location information obtained from sensors and GPS installed in the vehicle, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are used, and whether turn signals are used.

[0087] "Preprocessing" refers to the process of filtering out noise from collected driving data, extracting important indicators, and converting the data into an analyzable format.

[0088] "Data transmission" refers to the process of transmitting the pre-processed data over the Internet to a server.

[0089] "Data reception" refers to the process in which the server receives driving data sent from the terminal and stores it in a database.

[0090] "Analysis Algorithm" means the mathematical and statistical methods and procedures used to calculate a driver's evaluation score using collected driving data.

[0091] "Evaluation score" refers to a numerical value calculated based on an analytical algorithm that evaluates the safety and appropriateness of a driver's driving behavior.

[0092] "Premium" refers to the automobile insurance premium payable by the driver, calculated based on the rating score.

[0093] "Notification" refers to the process of communicating information to inform users of calculated insurance premiums in real time.

[0094] The automobile insurance fair amount calculation system of the present invention is a system that collects driving data from multiple sensors and GPS installed in the vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver.

[0095] Terminal handling

[0096] Data collection

[0097] The device collects driving data from a GPS system and multiple sensors. The hardware used in this process includes the u-blox Neo-7M GPS system, brake sensors, and an OBD-II interface. These sensors collect information such as location, route, speed, frequency of sudden braking and acceleration, seatbelt use, and turn signal use.

[0098] Specific examples

[0099] For example, while the vehicle is moving, GPS records its location every second, the brake sensor detects the number of sudden braking attempts, and the vehicle speed is acquired using the OBD-II interface.

[0100] Data Preprocessing

[0101] The device preprocesses the collected driving data and converts it into the required format. For preprocessing, Python scripts and the SciPy library are used to clean up the data by filtering noise. Key indicators such as mileage and frequency of sudden braking are then calculated and extracted.

[0102] Specific examples

[0103] Noise filtering uses a low-pass filter from the SciPy library to remove outliers (spikes) from the location data, after which distance traveled and frequency of hard braking are calculated.

[0104] Data transmission

[0105] The device sends the pre-processed data to the server over the Internet using an HTTP POST request using the "requests" library.

[0106] Specific examples

[0107] The preprocessed data is serialized in JSON format and sent periodically via HTTP POST request. For example, the sending interval is set to every 10 minutes.

[0108] Server Processing

[0109] Data reception

[0110] The server receives the driving data sent from the device and stores it in a database using an API endpoint using Flask, and the data is stored in a MySQL database.

[0111] Specific examples

[0112] The Flask API on the server receives the HTTP POST request, parses the data, and inserts it into a database table. For example, data from user "A" is stored in table "user_data."

[0113] Data analysis

[0114] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[0115] Specific examples

[0116] Features such as driving distance, frequency of sudden braking, frequency of sudden acceleration, and compliance with traffic rules are extracted, and an evaluation score is calculated using a random forest model. For example, if user "A" drives 50km per day and brakes suddenly five times, an analysis will be performed based on that data.

[0117] Insurance premium calculation

[0118] The server calculates the appropriate insurance premium based on the rating score, using a regression model for the premium.

[0119] Specific examples

[0120] For example, if the evaluation score is "80," the monthly insurance premium will be calculated based on that score, at 3,000 yen.

[0121] Insurance premium notification

[0122] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[0123] Specific examples

[0124] The calculated insurance premium is serialized in JSON format and sent to the terminal via an HTTP POST request. For example, the data "Insurance premium: 3000 yen / month" is sent.

[0125] User Action

[0126] Insurance premium display

[0127] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[0128] Specific examples

[0129] The terminal displays a notification message such as "Current insurance premium: 3,000 yen / month" on the LCD screen.

[0130] User Feedback

[0131] The user can review and improve his / her driving style based on the notified insurance premium.

[0132] Specific examples

[0133] For example, if you notice that you frequently slam on the brakes, you can consciously try to avoid doing so in order to improve your behavior.

[0134] Prompt Sentence Examples

[0135] "How can I design a system to calculate fair insurance premiums based on driving style data? Please include the following data: location, route, speed, frequency of sudden braking and acceleration, seat belt use, and turn signal use."

[0136] As described above, this system works in cooperation with the terminal and server to calculate and notify appropriate insurance premiums in real time, thereby raising awareness of safe driving.

[0137] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0138] Step 1: Data collection

[0139] Terminal handling

[0140] The terminal collects driving data from various sensors installed in the vehicle (e.g., GPS module, brake sensor, OBD-II interface). Specifically, the GPS module records location information every second, and the brake sensor counts the number of sudden braking events. Data collected through the OBD-II interface includes information such as driving speed and engine status.

[0141] Input and Output

[0142] Inputs: GPS module, brake sensor, raw data from OBD-II interface

[0143] Output: Collected driving data (location information, number of sudden braking, driving speed, etc.)

[0144] Specific examples

[0145] Whenever the vehicle is traveling, the GPS system records its location every second. For example, the current location is recorded as "Latitude: 35.6895, Longitude: 139.6917." In addition, the brake sensor counts each sudden braking as "1 sudden braking."

[0146] Step 2: Data Preprocessing

[0147] Terminal handling

[0148] The device preprocesses the collected driving data and converts it into an analyzable format. Specific preprocessing operations include noise filtering and data cleansing. Using the Python SciPy library, for example, outliers and spikes are removed. Furthermore, key indicators such as mileage and frequency of sudden braking are extracted.

[0149] Input and Output

[0150] Input: Collected raw driving data

[0151] Output: Clean data after preprocessing (filtered location information, number of hard braking incidents, mileage, etc.)

[0152] Specific examples

[0153] Noise filtering is performed on the location data to remove outliers. The distance traveled is also calculated from the location information, and the data is formatted as "Distance traveled: 50km".

[0154] Step 3: Send data

[0155] Terminal handling

[0156] The terminal sends the preprocessed data to the server via the Internet. Using the "requests" library, the data is sent by HTTP POST request.

[0157] Input and Output

[0158] Input: Preprocessed operating data

[0159] Output: Data sent to the server in JSON format

[0160] Specific examples

[0161] The preprocessed data is converted to JSON format and sent using an HTTP POST request. For example, data such as "Distance traveled: 50km, Number of hard braking attempts: 5" is sent every 10 minutes.

[0162] Step 4: Receiving Data

[0163] Server Processing

[0164] The server receives the driving data sent from the device and stores it in a database using a Flask-based API endpoint, and the data is stored in a MySQL database.

[0165] Input and Output

[0166] Input: Driving data sent from the terminal

[0167] Output: Operation data stored in the database

[0168] Specific examples

[0169] When the server's Flask API receives the HTTP POST request, it parses the data and inserts it into the database's "user_data" table. For example, it stores "User ID: A, Mileage: 50km, Number of Hard Brakes: 5".

[0170] Step 5: Data analysis

[0171] Server Processing

[0172] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[0173] Input and Output

[0174] Input: Saved driving data

[0175] Output: Driver's rating score

[0176] Specific examples

[0177] The driving distance and frequency of sudden braking stored in the database are extracted as features and analyzed using a random forest model. For example, the result is calculated as "User ID: A, Evaluation score: 80."

[0178] Step 6: Premium calculation

[0179] Server Processing

[0180] The server calculates the appropriate insurance premium based on the rating score, using a regression model.

[0181] Input and Output

[0182] Input: Rating score

[0183] Output: Calculated insurance premium

[0184] Specific examples

[0185] If the evaluation score is 80, the monthly insurance premium will be calculated based on that score: 3,000 yen.

[0186] Step 7: Premium Notification

[0187] Server Processing

[0188] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[0189] Input and Output

[0190] Input: Calculated insurance premium

[0191] Output: Premium notification sent to the terminal

[0192] Specific examples

[0193] The calculated insurance premium is serialized in JSON format and sent to the terminal as "Insurance premium: 3,000 yen / month."

[0194] Step 8: View premiums

[0195] Terminal handling

[0196] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[0197] Input and Output

[0198] Input: Premium notification sent from the server

[0199] Output: Insurance premium displayed on the device screen

[0200] Specific examples

[0201] The message "Current insurance premium: 3,000 yen / month" is displayed in real time on the terminal's LCD screen.

[0202] Step 9: User Feedback

[0203] User Action

[0204] The user can review and improve his / her driving style based on the notified insurance premium.

[0205] Input and Output

[0206] Input: Insurance premium displayed on the terminal

[0207] Power: Improved driving style

[0208] Specific examples

[0209] For example, the goal is to lower insurance premiums by driving more carefully to reduce sudden braking.

[0210] (Application example 1)

[0211] 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."

[0212] In recent years, systems that collect and analyze driving data to calculate appropriate automobile insurance premiums have become widespread. However, conventional systems often require manual operation by the driver, making it difficult to calculate insurance premiums that take into account data from autonomous driving. Furthermore, since evaluation scores and premium notifications are not calculated in real time, it is difficult for drivers to obtain useful feedback. Furthermore, conventional systems do not utilize generative AI models, which hinders the accuracy of data analysis and premium calculation.

[0213] 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.

[0214] In this invention, the server includes means for collecting and analyzing driving data while the vehicle is driving autonomously, means for generating prompt messages using the driving data so that the generative AI model can calculate an appropriate insurance premium, means for calculating the evaluation score based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type, and the notification means includes means for displaying the insurance premium on a terminal screen or a head-mounted display. This enables a comprehensive evaluation that includes driving data during autonomous driving, and makes it possible to provide the driver with accurate and appropriate insurance premium feedback in real time.

[0215] A "vehicle" is a mechanical device that can travel on land, is usually equipped with an engine or motor, and is a vehicle for transporting people or goods.

[0216] A "sensor" is a device that detects physical conditions or changes and outputs them as electrical signals.

[0217] "GPS" is an abbreviation for Global Positioning System, a system that uses artificial satellites to measure and display positions on Earth.

[0218] "Driving data" refers to information relating to the driving conditions of a vehicle, including, specifically, location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are fastened, and whether turn signals are used.

[0219] "Preprocessing" refers to data processing operations to remove unnecessary noise from raw data and convert it into a format suitable for analysis.

[0220] An "analytic algorithm" is a computational procedure used to analyze collected data and find patterns and trends.

[0221] The "evaluation score" is a numerical representation of the driver's driving behavior based on the analysis results, and serves as the basis for calculating insurance premiums.

[0222] "Premium" means the amount payable by a driver for motor vehicle insurance, which is determined based on a risk assessment.

[0223] "Real-time" refers to processing and information provision occurring almost simultaneously with actual time.

[0224] "Autonomous driving" is a technology that allows vehicles to drive autonomously without human intervention, utilizing sensors and AI technology.

[0225] A "generative AI model" is a type of artificial intelligence designed to learn from large amounts of data and perform specific tasks.

[0226] A "prompt statement" is an input statement that instructs a generative AI model to perform a specific process, and explicitly states the conditions and requirements for analysis and calculation.

[0227] A "terminal" is an electronic device that a user directly operates to input and display information, including smartphones and head-mounted displays.

[0228] The insurance premium calculation system of this invention calculates appropriate insurance premiums for each driver by collecting driving data from multiple sensors and GPS installed in the vehicle. Implementing this system requires sensors, GPS, a smartphone, a head-mounted display (HMD), a server, and an AI model.

[0229] System Configuration

[0230] Terminal handling

[0231] Data collection

[0232] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, an application on the device records in real time location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt use, turn signal use, and other information.

[0233] Data Preprocessing

[0234] The device preprocesses the collected driving data and converts it into the required format, which includes cleaning up the data by filtering out noise and extracting key indicators (e.g., frequency of hard braking).

[0235] Data transmission

[0236] The pre-processed data is then sent to a server via the internet. This communication is carried out periodically to ensure stability and security of the communication.

[0237] Server Processing

[0238] Data reception and storage

[0239] The server receives the driving data sent from the device and stores it in a database. For example, data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[0240] Data analysis

[0241] The server then uses an analysis algorithm to calculate an evaluation score based on the received data, including the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The resulting evaluation score is then used to calculate a driving skill score.

[0242] Insurance premium calculation and notification

[0243] The server calculates the appropriate insurance premium based on the analysis results. For example, based on User A's evaluation score, the premium is calculated to be 3,000 yen per month. The server then generates data to notify the user of the calculated premium in real time and sends it to the device. This notification is displayed on the smartphone or HMD.

[0244] Using generative AI models

[0245] Using driving data, the generative AI model generates prompts and calculates appropriate insurance premiums. For example, a prompt such as "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data" can be generated and sent to the AI ​​model, enabling highly accurate calculation of insurance premiums.

[0246] Specific examples

[0247] Example 1

[0248] For example, if it is detected that User A has a driving style that frequently involves sudden braking, that information is preprocessed in real time and sent to the server. The server analyzes the data and calculates the insurance premium based on a generative AI model that sets a higher premium for frequent sudden braking. As a result, "Current insurance premium: 3,500 yen / month" is displayed on the user's smartphone or HMD.

[0249] Example 2

[0250] Data during autonomous driving is also collected and analyzed in the same way. For example, if User B's vehicle is driving safely and efficiently in autonomous mode, that data is sent to the server, and the insurance premium is calculated lower due to safe driving. The prompt text based on the generative AI model is generated as follows: "User B recorded a mileage of 100 km in autonomous mode with no sudden braking. Please adjust the insurance premium accordingly." As a result, "Current insurance premium: 2,500 yen / month" is displayed.

[0251] As described above, this system enables real-time data collection and analysis, highly accurate insurance premium calculations, and is capable of providing users with timely and appropriate feedback.

[0252] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0253] Step 1:

[0254] The terminal collects driving data from the vehicle's GPS and multiple sensors. Specifically, it obtains current location information from the GPS and collects driving data such as the frequency of sudden braking and sudden acceleration, seat belt use, and turn signal use from the sensors. The input is raw data from the GPS and sensors. The output is a set of collected driving data.

[0255] Step 2:

[0256] The terminal preprocesses the collected driving data and converts it into the required format. Specifically, it performs noise filtering and data cleanup, and extracts key indicators such as the frequency of sudden braking and mileage. The input is the driving data collected in step 1. The output is a set of preprocessed driving data.

[0257] Step 3:

[0258] The terminal transmits the preprocessed data to the server via the Internet. This transmission occurs periodically to ensure communication stability and security. The input is the preprocessed data from step 2. The output is the data transmitted via the network.

[0259] Step 4:

[0260] The server receives the driving data sent from the terminal and stores it in a database. Specifically, it records the received data in a database for analysis. The input is the data sent in step 3. The output is the driving data stored in the database.

[0261] Step 5:

[0262] The server uses an analysis algorithm to calculate an evaluation score based on the received driving data. The score is calculated based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The input is driving data obtained from the database. The output is an evaluation score.

[0263] Step 6:

[0264] The server calculates an appropriate insurance premium based on the analysis results. At this time, it generates a prompt for the generative AI model and requests it to calculate the insurance premium. Specifically, based on data such as driving distance and frequency of sudden braking, it generates a prompt such as, "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data." The input is the evaluation score and driving data. The output is the generated prompt and the calculated insurance premium.

[0265] Step 7:

[0266] The server generates data to notify the calculated insurance premium in real time and sends it to the terminal. This data is to be displayed on the user's smartphone or HMD. The input is the calculated insurance premium. The output is the insurance premium notification data sent to the terminal.

[0267] Step 8:

[0268] The device receives the insurance premium notification and visually displays it to the user. Specifically, it displays a notification such as "Current insurance premium: 3,000 yen / month" on the device screen or HMD. The input is the insurance premium notification data received from the server. The output is the insurance premium information displayed to the user.

[0269] 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.

[0270] The automobile insurance fairness calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on vehicles, preprocesses and analyzes the data, and calculates and notifies the appropriate insurance premium for each driver. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score. Specific embodiments are described below.

[0271] Terminal handling

[0272] Data collection

[0273] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking attempts, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is used.

[0274] Collecting Emotional Data

[0275] The device is also equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expressions, allowing it to detect the user's emotional state, such as stress or impatience, in real time while driving.

[0276] Data Preprocessing

[0277] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[0278] Data transmission

[0279] The device transmits the pre-processed driving and emotion data to a server via the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data.

[0280] Server Processing

[0281] Data reception

[0282] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database.

[0283] Data analysis

[0284] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[0285] Insurance premium calculation

[0286] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[0287] Insurance premium notification

[0288] The server generates data to notify users of calculated insurance premiums in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[0289] User Action

[0290] Receive insurance premium notifications

[0291] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0292] User Feedback

[0293] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[0294] In this way, this system collects, pre-processes, and analyzes driving and emotional data, and calculates and notifies appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[0295] The processing flow will be explained below.

[0296] Step 1: Data collection

[0297] The device collects driving data from the vehicle's GPS and multiple sensors. For example, GPS records location information, driving route, and driving speed, while a brake sensor detects the number of sudden braking attempts. Furthermore, biometric sensors record heart rate and facial expressions in real time to collect the user's emotional state.

[0298] Step 2: Data Preprocessing

[0299] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[0300] Step 3: Send data

[0301] The device transmits the pre-processed data to a server over the Internet, either in real time or periodically, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[0302] Step 4: Receiving Data

[0303] The server receives the driving data and emotion data sent from the device and stores them in a temporary buffer. The received data is sequentially recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, emotion data, etc. are registered in the database.

[0304] Step 5: Data analysis

[0305] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and a high level of stress is detected while driving, an evaluation score is calculated based on that data.

[0306] Step 6: Premium calculation

[0307] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium will be set high.

[0308] Step 7: Premium Notification

[0309] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. For example, it generates notification data such as "Current insurance premium: 3,000 yen / month" and converts it into a format for visual display.

[0310] Step 8: Receive premium notification

[0311] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0312] Step 9: User Feedback

[0313] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to lower their insurance premiums by driving more calmly and in a relaxed state.

[0314] Example 2

[0315] 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."

[0316] Conventional automobile insurance calculation systems calculate insurance premiums based solely on the driver's driving data, without taking into account factors such as the driver's emotional state or stress level, limiting the ability to optimize insurance premiums. Furthermore, there is a lack of a system that reflects the driver's emotional data in real time, which creates the problem that the driver's psychological state is not reflected in the insurance premium. The present invention aims to solve these problems and realize more appropriate and fair insurance premium calculations.

[0317] 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.

[0318] In this invention, the server includes means for collecting driving data from multiple sensors and a location information system mounted on the vehicle, means for collecting biometric data such as the user's heart rate and facial expression to detect the emotional state while driving, and means for preprocessing the driving data and emotional data and transmitting the preprocessed data to a central processing unit via a network. This enables fair and appropriate insurance premium calculation based on detailed driving data including the driver's emotional state.

[0319] "Vehicle-mounted sensors" refers to multiple detection devices attached to a vehicle to collect various data while driving.

[0320] "Location information system" refers to a device that records location information such as a vehicle's current location, route, and speed.

[0321] "Driving data" refers to information related to the driving status of a vehicle, including, specifically, driving distance, driving speed, number of sudden braking attempts, whether seat belts are fastened, and whether turn signals are used.

[0322] "Preprocessing" refers to converting the collected raw data into an analyzable format and performing processes such as noise removal and adding timestamps.

[0323] "Central processing unit" refers to a processing unit such as a server that receives, analyzes, and stores data sent from a terminal.

[0324] "Analytics Algorithm" refers to the mathematical models and computational methods used to calculate the assessment score using the collected data.

[0325] The "evaluation score" is a numerical value calculated based on driving data and emotional data, and is an indicator that serves as the basis for calculating insurance premiums.

[0326] "Premium" refers to the amount a driver pays under an automobile insurance policy.

[0327] "Real-time" refers to data being collected, processed, and communicated immediately.

[0328] "Emotional state" refers to the psychological and physiological state of the driver while driving, and specifically includes emotions such as stress and impatience.

[0329] "Biometric data" refers to data that indicates the driver's physical state, such as the driver's heart rate or facial expression.

[0330] The automobile insurance fair amount calculation system of the present invention is a system that calculates and notifies the fair insurance premium for each driver by collecting driving data from multiple sensors and a location information system installed in the vehicle and preprocessing and analyzing the data. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score.

[0331] Terminal handling

[0332] Data collection

[0333] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, while the brake sensor detects the number of sudden braking events. The seatbelt sensor also checks whether the seatbelt is fastened, and the turn signal sensor detects whether the turn signal is being used. This data is temporarily stored in the device.

[0334] Collecting Emotional Data

[0335] The device is equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expression. For example, heart rate data is obtained from a smartwatch, and facial expression data is analyzed using an in-car camera. This allows the system to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data.

[0336] Data Preprocessing

[0337] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. Specifically, it performs noise filtering and removes unnecessary data. It also categorizes the collected data by timestamp and compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database.

[0338] Data transmission

[0339] The device transmits the preprocessed driving and emotion data to a server via the Internet, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[0340] Server Processing

[0341] Data reception

[0342] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then stored in a database, where, for example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotion data are registered.

[0343] Data analysis

[0344] The server then uses an analysis algorithm to analyze the received driving data and emotional data. The analysis algorithm uses software such as TensorFlow to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state as evaluation criteria, and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[0345] Insurance premium calculation

[0346] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[0347] Insurance premium notification

[0348] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[0349] User Action

[0350] Receive insurance premium notifications

[0351] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0352] User Feedback

[0353] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[0354] This will enable the system to collect, pre-process, and analyze driving and emotional data, and then calculate and notify appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[0355] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0356] Step 1: Data collection

[0357] The device collects driving data from the vehicle's GPS and multiple sensors. As input, it obtains location information, driving route, and driving speed from the location information system, and obtains the number of sudden braking attempts, seat belt usage status, and turn signal usage status from various sensors. This data is temporarily stored in the device. In concrete terms, the GPS module periodically updates location information, and the sensors collect data in real time.

[0358] Step 2: Collecting emotion data

[0359] The device uses biometric sensors to collect biometric data such as the user's heart rate and facial expressions. It receives input from the smartwatch and in-car camera. This allows the device to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data. Specifically, the smartwatch updates the user's heart rate every second, and the camera analyzes facial expressions in real time.

[0360] Step 3: Data Preprocessing

[0361] The terminal preprocesses the collected driving and emotional data and converts it into an analyzable format. As input, it receives all data collected in the previous step. It performs noise filtering to remove unnecessary data and classifies the collected data by timestamp. As output, it compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database format. Specifically, a filtering algorithm is applied and the data is converted into an appropriate format.

[0362] Step 4: Send data

[0363] The terminal transmits the preprocessed driving data and emotion data to the server via the Internet. As input, it receives the database-formatted data generated in the previous step. During transmission, a security protocol (e.g., SSL / TLS) is used to ensure the confidentiality and integrity of the data. As output, transmission confirmation data is generated. Specifically, the data transmission module encrypts the data and transmits it to the server.

[0364] Step 5: Receiving Data

[0365] The server receives data sent from the terminal and stores it in a temporary buffer. As input, it receives data received via the network. As output, it prepares the data for storage in the database. In concrete terms, the data receiving module authenticates the data and stores it in a temporary buffer.

[0366] Step 6: Data analysis

[0367] The server analyzes the received driving data and emotion data using an analysis algorithm. As input, it receives all data stored in the buffer. It uses the analysis algorithm (e.g., a TensorFlow model) to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state, and calculates an evaluation score. As output, an individual evaluation score is generated. Specifically, the algorithm processes the data and calculates the evaluation score.

[0368] Step 7: Premium calculation

[0369] The server calculates the appropriate insurance premium for each driver based on the analysis results. As input, it receives the evaluation score generated in the previous step. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. As output, individual insurance premium data is generated. In specific operation, the evaluation score is substituted into the insurance premium calculation formula to calculate the optimal insurance premium.

[0370] Step 8: Premium Notification

[0371] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. Insurance premium data is received as input. Data converted into a notification format is generated as output. Specifically, the notification data is sent to the terminal and converted into a format that the user can view.

[0372] Step 9: Receive premium notification

[0373] The terminal receives the insurance premium notification data sent from the server and displays it to the user. As input, it receives the notification data sent from the server. As output, it displays it on the screen in a format that the user can check. Specifically, the terminal's screen display module converts the data into a display format and notifies the user.

[0374] Step 10: User feedback

[0375] The user can review their driving style and emotional management based on the notified insurance premium. As input, they check the insurance premium data displayed on the notification screen. As output, improved driving behavior and emotional management methods are implemented. As a specific action, the user changes their driving style or adopts new methods for stress management.

[0376] (Application example 2)

[0377] 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."

[0378] Conventional automobile insurance premium calculation systems do not fully take into account the driver's driving skills and biometric data, making it difficult to accurately calculate insurance premiums based on actual risk.In addition, there is a lack of a mechanism to notify drivers of the appropriateness of their insurance premiums in real time, which limits opportunities for drivers to review their driving style and emotional management.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0380] In this invention, the server includes: means for collecting driving data from multiple sensors and a location information system mounted on the vehicle; means for preprocessing the driving data and biometric data and converting them into an analyzable data format; means for transmitting the preprocessed driving data and biometric data to the server via a network; means for storing the transmitted data in a database and calculating an evaluation score using an analysis algorithm; means for calculating insurance premiums for each driver based on the evaluation score; means for notifying the calculated insurance premiums in real time; and means for displaying the appropriate premium on the user's terminal, allowing the user to review their driving style and emotional state. This enables fair calculation of insurance premiums based on the driver's actual driving skills and emotional state, thereby promoting safe driving by drivers.

[0381] "Multiple sensors mounted on a vehicle" refers to various devices installed to measure and record the vehicle's operating status, including brake sensors, seat belt sensors, turn signal sensors, etc.

[0382] A "location information system" is a device that uses satellite navigation technology to measure a vehicle's location and collect data in real time.

[0383] "Driving data" refers to information collected while a vehicle is in operation, such as speed, location, and number of sudden braking attempts.

[0384] "Biometric data" refers to data that indicates the driver's physical and psychological state, such as heart rate and facial expressions.

[0385] "Preprocessing" refers to a series of data preparation procedures, such as data filtering and time stamping, to convert collected data into an analyzable format.

[0386] "Network" refers to a system that transmits and receives data using communication infrastructure such as the Internet.

[0387] The "server" is a computer system that receives driving data and biometric data, processes them using analytical algorithms, and stores them.

[0388] A "database" is a collection of information that systematically organizes and stores received data, making it easy to later analyze and search.

[0389] "Analysis algorithm" refers to the mathematical method or calculation program used to calculate an evaluation score based on collected driving data and biometric data.

[0390] The "evaluation score" is a numerical index calculated based on the analysis results of driving data and biometric data, and is a standard for evaluating the driver's driving skills and emotional state.

[0391] "Premium" means the monetary consideration paid by a driver under an automobile insurance policy.

[0392] "Real-time notification" refers to a means of immediately transmitting information to the driver based on the results of data analysis.

[0393] "User device" refers to a device that can receive and display notifications, such as a driver's smartphone or smart glasses.

[0394] A system for carrying out the present invention utilizes multiple sensors and a location information system mounted on a vehicle to collect and analyze driving data and biological data. A specific embodiment of the system is described below.

[0395] Hardware Configuration

[0396] 1. Vehicle sensors

[0397] GPS location system:

[0398] Record the vehicle's location, route, and speed.

[0399] Brake Sensor:

[0400] Detects the number of sudden braking events.

[0401] Seat belt sensor:

[0402] Check that the seat belt is fastened properly.

[0403] Turn Signal Sensor:

[0404] Detects turn signal usage.

[0405] Biometric Sensors:

[0406] It collects biometric data such as heart rate and facial expressions.

[0407] 2. User's Device

[0408] Smartphone:

[0409] Display and notify data.

[0410] Smart Glasses:

[0411] Display and notify data in real time.

[0412] Software Configuration

[0413] A program is installed on both the server and the terminal, and performs the following processes:

[0414] Terminal handling

[0415] 1. Data Collection

[0416] The device collects driving and biometric data in real time from sensors and location systems installed in the vehicle.

[0417] 2. Data Preprocessing

[0418] The terminal pre-processes the collected data, filtering out noise, time-stamping it and converting it into an organized format.

[0419] 3. Data Transmission

[0420] The pre-processed data is sent to a server over the Internet using security protocols (SSL / TLS).

[0421] Server Processing

[0422] 1. Data Reception

[0423] The server receives data sent from the terminal and stores it in a temporary buffer. The received data is then recorded in a database sequentially.

[0424] 2. Data Analysis

[0425] The server uses an analytical algorithm to analyze the driving data and biometric data, and calculates an overall evaluation score based on evaluation criteria such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on the biometric data.

[0426] 3. Insurance premium calculation

[0427] Based on the analysis results, the appropriate insurance premium is calculated for each driver. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user.

[0428] 4. Notification of insurance premiums

[0429] The server notifies the user of the calculated insurance premium in real time. The premium notification data for each user is converted into a format suitable for display on the screen and then sent.

[0430] User Action

[0431] 1. Receiving premium notices

[0432] The user's terminal receives the insurance premium notification data sent from the server and displays the insurance premium in real time.

[0433] 2. User Feedback

[0434] The user can review his / her driving style and emotional management based on the notified insurance premium.

[0435] Specific examples

[0436] For example, if an autonomous vehicle repeatedly brakes suddenly while driving on a highway, causing the driver's heart rate to rise, this data will be collected and analyzed and reflected in the insurance premium. A notification such as "Current insurance premium: 3,000 yen / month" will also be displayed on the user's smartphone.

[0437] Example prompts for the generative AI

[0438] "I would like to develop an application that collects driving data from autonomous vehicles and driver biometric data, analyzes it in real time, and calculates and notifies appropriate insurance premiums. Please tell me the types of sensors required, the data preprocessing method, and details of the insurance premium calculation algorithm."

[0439] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0440] Step 1:

[0441] The device collects driving data and biometric data in real time from the vehicle's onboard location information system and multiple sensors. Specifically, it obtains current coordinate data, driving route, and driving speed from the location information system, the number of sudden braking events from the brake sensor, and heart rate and facial expression data from the biometric sensor.

[0442] Step 2:

[0443] The device preprocesses the collected driving data and biometric data. Specifically, it performs noise filtering and removes unnecessary data. It also adds timestamps to organize and classify the data. This preprocessing converts the data into an analyzable format. For example, indicators such as driving distance, frequency of sudden braking, whether or not a speeding violation occurred, and emotional state based on biometric data can be extracted.

[0444] Step 3:

[0445] The terminal transmits the preprocessed driving data and biometric data to a server via the Internet. Security protocols (SSL / TLS) are used for transmission to ensure confidentiality and integrity of the data. The input at this stage is the preprocessed data, and the output is the data transmission to the server.

[0446] Step 4:

[0447] The server receives the data sent from the device and stores it in a temporary buffer. It then records it sequentially in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database. The input of this step is the data sent from the device, and the output is the record in the database.

[0448] Step 5:

[0449] The server analyzes the data recorded in the database using an analytical algorithm. Specifically, factors such as driving distance, frequency of sudden braking, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. The analytical algorithm quantifies each factor and calculates an overall evaluation score. The input of this step is the database data, and the output is the evaluation score.

[0450] Step 6:

[0451] The server calculates the insurance premium for each driver based on the evaluation score. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and has many high stress states, the insurance premium will be set high. The input of this step is the evaluation score, and the output is the calculated insurance premium.

[0452] Step 7:

[0453] The server notifies the terminal of the calculated insurance premium in real time. The notification data is converted into a format for screen display and sent. For example, a specific notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen. The input of this step is the calculated insurance premium, and the output is the notification data.

[0454] Step 8:

[0455] The user's terminal receives the insurance premium notification data sent from the server and displays it to the user. This allows the user to review their driving style and emotional management. Based on the notified insurance premium, the user can make efforts to lower the insurance premium by driving safely. The input of this step is the notification data, and the output is the insurance premium displayed on the screen.

[0456] 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.

[0457] 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.

[0458] 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.

[0459] [Second embodiment]

[0460] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0461] 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.

[0462] 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).

[0463] 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.

[0464] 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.

[0465] 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).

[0466] 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.

[0467] 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.

[0468] 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.

[0469] 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.

[0470] 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.

[0471] 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."

[0472] The automobile insurance fair amount calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on a vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver. Specific embodiments are described below.

[0473] Terminal handling

[0474] Data collection

[0475] The device collects driving data (location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt usage status, turn signal usage status, etc.) from the vehicle's GPS and multiple sensors. For example, while the vehicle is driving, the GPS constantly records location information, and the brake sensor detects the number of sudden braking events.

[0476] Data Preprocessing

[0477] The device preprocesses the collected driving data and converts it into the required format, including cleaning up the data by filtering out noise and extracting key indicators such as mileage and frequency of hard braking.

[0478] Data transmission

[0479] The device then transmits the pre-processed data to a server over the Internet, which is done periodically to ensure communication stability and security.

[0480] Server Processing

[0481] Data reception

[0482] The server receives the driving data sent from the device and stores it in a database. For example, the data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[0483] Data analysis

[0484] The server analyzes the received driving data using an analytical algorithm. In this analysis, evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. For example, if user A's driving distance in one day is 50 km and he or she braked suddenly five times, the data is evaluated and a driving skill score is calculated.

[0485] Insurance premium calculation

[0486] The server calculates an appropriate insurance premium based on the analysis results. For example, the insurance premium calculated based on User A's evaluation score is 3,000 yen / month.

[0487] Insurance premium notification

[0488] The server generates data to notify the user of the calculated insurance premium in real time and transmits it to the terminal.

[0489] User Action

[0490] Receive insurance premium notifications

[0491] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user. For example, "Current insurance premium: 3000 yen / month" is displayed on the terminal screen.

[0492] User Feedback

[0493] Users can review their driving style based on the notified insurance premium. For example, if they know that they frequently apply sudden brakes, they will be able to develop driving habits to avoid this and become more conscious of safe driving.

[0494] As described above, this system collects, pre-processes, and analyzes driving data in real time, enabling appropriate insurance premiums to be calculated and notified. This is expected to reflect drivers' driving behavior more fairly and promote safe driving.

[0495] The processing flow will be explained below.

[0496] Step 1: Data collection

[0497] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking events, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is being used.

[0498] Step 2: Data Preprocessing

[0499] The device preprocesses the collected data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by the time of recording. For example, it extracts information such as driving distance, frequency of sudden braking, and whether or not there was a speeding violation, and compiles it into a database.

[0500] Step 3: Send data

[0501] The device transmits the pre-processed data to a server over the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data during transmission.

[0502] Step 4: Receiving Data

[0503] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, information such as User A's driving distance, number of sudden braking attempts, and speeding violations are registered in the database.

[0504] Step 5: Data analysis

[0505] The server uses an analysis algorithm to analyze the received driving data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day and brakes suddenly five times during that time, the evaluation score is calculated based on that data.

[0506] Step 6: Premium calculation

[0507] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A's overall evaluation score is low, the insurance premium may be set high.

[0508] Step 7: Premium Notification

[0509] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for screen display and sent.

[0510] Step 8: Receive premium notification

[0511] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0512] Step 9: User Feedback

[0513] Users can review their driving style based on the insurance premiums they receive. For example, if a user sees the notification and decides that their insurance premiums are high because they brake suddenly a lot, they can make an effort to drive more calmly in the future to lower their insurance premiums.

[0514] Example 1

[0515] 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."

[0516] Conventional automobile insurance systems have difficulty accurately reflecting a driver's driving behavior, and insurance premiums are often set at a flat rate. This creates the problem that drivers who are mindful of safe driving are not fairly evaluated. Furthermore, conventional systems are unable to notify insurance premiums in real time, which can result in drivers missing an opportunity to review their driving behavior.

[0517] 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.

[0518] In this invention, the server includes a means for collecting driving data from multiple sensors and GPS mounted on the vehicle, a means for converting the data into an analyzable data format by noise filtering and extracting key indicators, and a means for transmitting the data to the server via the Internet, which makes it possible to calculate appropriate insurance premiums for each driver and notify them in real time.

[0519] "Driving data" refers to data such as location information obtained from sensors and GPS installed in the vehicle, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are used, and whether turn signals are used.

[0520] "Preprocessing" refers to the process of filtering out noise from collected driving data, extracting important indicators, and converting the data into an analyzable format.

[0521] "Data transmission" refers to the process of transmitting the pre-processed data over the Internet to a server.

[0522] "Data reception" refers to the process in which the server receives driving data sent from the terminal and stores it in a database.

[0523] "Analysis Algorithm" means the mathematical and statistical methods and procedures used to calculate a driver's evaluation score using collected driving data.

[0524] "Evaluation score" refers to a numerical value calculated based on an analytical algorithm that evaluates the safety and appropriateness of a driver's driving behavior.

[0525] "Premium" refers to the automobile insurance premium payable by the driver, calculated based on the rating score.

[0526] "Notification" refers to the process of communicating information to inform users of calculated insurance premiums in real time.

[0527] The automobile insurance fair amount calculation system of the present invention is a system that collects driving data from multiple sensors and GPS installed in the vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver.

[0528] Terminal handling

[0529] Data collection

[0530] The device collects driving data from a GPS system and multiple sensors. The hardware used in this process includes the u-blox Neo-7M GPS system, brake sensors, and an OBD-II interface. These sensors collect information such as location, route, speed, frequency of sudden braking and acceleration, seatbelt use, and turn signal use.

[0531] Specific examples

[0532] For example, while the vehicle is moving, GPS records its location every second, the brake sensor detects the number of sudden braking attempts, and the vehicle speed is acquired using the OBD-II interface.

[0533] Data Preprocessing

[0534] The device preprocesses the collected driving data and converts it into the required format. For preprocessing, Python scripts and the SciPy library are used to clean up the data by filtering noise. Key indicators such as mileage and frequency of sudden braking are then calculated and extracted.

[0535] Specific examples

[0536] Noise filtering uses a low-pass filter from the SciPy library to remove outliers (spikes) from the location data, after which distance traveled and frequency of hard braking are calculated.

[0537] Data transmission

[0538] The device sends the pre-processed data to the server over the Internet using an HTTP POST request using the "requests" library.

[0539] Specific examples

[0540] The preprocessed data is serialized in JSON format and sent periodically via HTTP POST request. For example, the sending interval is set to every 10 minutes.

[0541] Server Processing

[0542] Data reception

[0543] The server receives the driving data sent from the device and stores it in a database using an API endpoint using Flask, and the data is stored in a MySQL database.

[0544] Specific examples

[0545] The Flask API on the server receives the HTTP POST request, parses the data, and inserts it into a database table. For example, data from user "A" is stored in table "user_data."

[0546] Data analysis

[0547] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[0548] Specific examples

[0549] Features such as driving distance, frequency of sudden braking, frequency of sudden acceleration, and compliance with traffic rules are extracted, and an evaluation score is calculated using a random forest model. For example, if user "A" drives 50km per day and brakes suddenly five times, an analysis will be performed based on that data.

[0550] Insurance premium calculation

[0551] The server calculates the appropriate insurance premium based on the rating score, using a regression model for the premium.

[0552] Specific examples

[0553] For example, if the evaluation score is "80," the monthly insurance premium will be calculated based on that score, at 3,000 yen.

[0554] Insurance premium notification

[0555] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[0556] Specific examples

[0557] The calculated insurance premium is serialized in JSON format and sent to the terminal via an HTTP POST request. For example, the data "Insurance premium: 3000 yen / month" is sent.

[0558] User Action

[0559] Insurance premium display

[0560] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[0561] Specific examples

[0562] The terminal displays a notification message such as "Current insurance premium: 3,000 yen / month" on the LCD screen.

[0563] User Feedback

[0564] The user can review and improve his / her driving style based on the notified insurance premium.

[0565] Specific examples

[0566] For example, if you notice that you frequently slam on the brakes, you can consciously try to avoid doing so in order to improve your behavior.

[0567] Prompt Sentence Examples

[0568] "How can I design a system to calculate fair insurance premiums based on driving style data? Please include the following data: location, route, speed, frequency of sudden braking and acceleration, seat belt use, and turn signal use."

[0569] As described above, this system works in cooperation with the terminal and server to calculate and notify appropriate insurance premiums in real time, thereby raising awareness of safe driving.

[0570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0571] Step 1: Data collection

[0572] Terminal handling

[0573] The terminal collects driving data from various sensors installed in the vehicle (e.g., GPS module, brake sensor, OBD-II interface). Specifically, the GPS module records location information every second, and the brake sensor counts the number of sudden braking events. Data collected through the OBD-II interface includes information such as driving speed and engine status.

[0574] Input and Output

[0575] Inputs: GPS module, brake sensor, raw data from OBD-II interface

[0576] Output: Collected driving data (location information, number of sudden braking, driving speed, etc.)

[0577] Specific examples

[0578] Whenever the vehicle is traveling, the GPS system records its location every second. For example, the current location is recorded as "Latitude: 35.6895, Longitude: 139.6917." In addition, the brake sensor counts each sudden braking as "1 sudden braking."

[0579] Step 2: Data Preprocessing

[0580] Terminal handling

[0581] The device preprocesses the collected driving data and converts it into an analyzable format. Specific preprocessing operations include noise filtering and data cleansing. Using the Python SciPy library, for example, outliers and spikes are removed. Furthermore, key indicators such as mileage and frequency of sudden braking are extracted.

[0582] Input and Output

[0583] Input: Collected raw driving data

[0584] Output: Clean data after preprocessing (filtered location information, number of hard braking incidents, mileage, etc.)

[0585] Specific examples

[0586] Noise filtering is performed on the location data to remove outliers. The distance traveled is also calculated from the location information, and the data is formatted as "Distance traveled: 50km".

[0587] Step 3: Send data

[0588] Terminal handling

[0589] The terminal sends the preprocessed data to the server via the Internet. Using the "requests" library, the data is sent by HTTP POST request.

[0590] Input and Output

[0591] Input: Preprocessed operating data

[0592] Output: Data sent to the server in JSON format

[0593] Specific examples

[0594] The preprocessed data is converted to JSON format and sent using an HTTP POST request. For example, data such as "Distance traveled: 50km, Number of hard braking attempts: 5" is sent every 10 minutes.

[0595] Step 4: Receiving Data

[0596] Server Processing

[0597] The server receives the driving data sent from the device and stores it in a database using a Flask-based API endpoint, and the data is stored in a MySQL database.

[0598] Input and Output

[0599] Input: Driving data sent from the terminal

[0600] Output: Operation data stored in the database

[0601] Specific examples

[0602] When the server's Flask API receives the HTTP POST request, it parses the data and inserts it into the database's "user_data" table. For example, it stores "User ID: A, Mileage: 50km, Number of Hard Brakes: 5".

[0603] Step 5: Data analysis

[0604] Server Processing

[0605] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[0606] Input and Output

[0607] Input: Saved driving data

[0608] Output: Driver's rating score

[0609] Specific examples

[0610] The driving distance and frequency of sudden braking stored in the database are extracted as features and analyzed using a random forest model. For example, the result is calculated as "User ID: A, Evaluation score: 80."

[0611] Step 6: Premium calculation

[0612] Server Processing

[0613] The server calculates the appropriate insurance premium based on the rating score, using a regression model.

[0614] Input and Output

[0615] Input: Rating score

[0616] Output: Calculated insurance premium

[0617] Specific examples

[0618] If the evaluation score is 80, the monthly insurance premium will be calculated based on that score: 3,000 yen.

[0619] Step 7: Premium Notification

[0620] Server Processing

[0621] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[0622] Input and Output

[0623] Input: Calculated insurance premium

[0624] Output: Premium notification sent to the terminal

[0625] Specific examples

[0626] The calculated insurance premium is serialized in JSON format and sent to the terminal as "Insurance premium: 3,000 yen / month."

[0627] Step 8: View premiums

[0628] Terminal handling

[0629] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[0630] Input and Output

[0631] Input: Premium notification sent from the server

[0632] Output: Insurance premium displayed on the device screen

[0633] Specific examples

[0634] The message "Current insurance premium: 3,000 yen / month" is displayed in real time on the terminal's LCD screen.

[0635] Step 9: User Feedback

[0636] User Action

[0637] The user can review and improve his / her driving style based on the notified insurance premium.

[0638] Input and Output

[0639] Input: Insurance premium displayed on the terminal

[0640] Power: Improved driving style

[0641] Specific examples

[0642] For example, the goal is to lower insurance premiums by driving more carefully to reduce sudden braking.

[0643] (Application example 1)

[0644] 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."

[0645] In recent years, systems that collect and analyze driving data to calculate appropriate automobile insurance premiums have become widespread. However, conventional systems often require manual operation by the driver, making it difficult to calculate insurance premiums that take into account data from autonomous driving. Furthermore, since evaluation scores and premium notifications are not calculated in real time, it is difficult for drivers to obtain useful feedback. Furthermore, conventional systems do not utilize generative AI models, which hinders the accuracy of data analysis and premium calculation.

[0646] 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.

[0647] In this invention, the server includes means for collecting and analyzing driving data while the vehicle is driving autonomously, means for generating prompt messages using the driving data so that the generative AI model can calculate an appropriate insurance premium, means for calculating the evaluation score based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type, and the notification means includes means for displaying the insurance premium on a terminal screen or a head-mounted display. This enables a comprehensive evaluation that includes driving data during autonomous driving, and makes it possible to provide the driver with accurate and appropriate insurance premium feedback in real time.

[0648] A "vehicle" is a mechanical device that can travel on land, is usually equipped with an engine or motor, and is a vehicle for transporting people or goods.

[0649] A "sensor" is a device that detects physical conditions or changes and outputs them as electrical signals.

[0650] "GPS" is an abbreviation for Global Positioning System, a system that uses artificial satellites to measure and display positions on Earth.

[0651] "Driving data" refers to information relating to the driving conditions of a vehicle, including, specifically, location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are fastened, and whether turn signals are used.

[0652] "Preprocessing" refers to data processing operations to remove unnecessary noise from raw data and convert it into a format suitable for analysis.

[0653] An "analytic algorithm" is a computational procedure used to analyze collected data and find patterns and trends.

[0654] The "evaluation score" is a numerical representation of the driver's driving behavior based on the analysis results, and serves as the basis for calculating insurance premiums.

[0655] "Premium" means the amount payable by a driver for motor vehicle insurance, which is determined based on a risk assessment.

[0656] "Real-time" refers to processing and information provision occurring almost simultaneously with actual time.

[0657] "Autonomous driving" is a technology that allows vehicles to drive autonomously without human intervention, utilizing sensors and AI technology.

[0658] A "generative AI model" is a type of artificial intelligence designed to learn from large amounts of data and perform specific tasks.

[0659] A "prompt statement" is an input statement that instructs a generative AI model to perform a specific process, and explicitly states the conditions and requirements for analysis and calculation.

[0660] A "terminal" is an electronic device that a user directly operates to input and display information, including smartphones and head-mounted displays.

[0661] The insurance premium calculation system of this invention calculates appropriate insurance premiums for each driver by collecting driving data from multiple sensors and GPS installed in the vehicle. Implementing this system requires sensors, GPS, a smartphone, a head-mounted display (HMD), a server, and an AI model.

[0662] System Configuration

[0663] Terminal handling

[0664] Data collection

[0665] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, an application on the device records in real time location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt use, turn signal use, and other information.

[0666] Data Preprocessing

[0667] The device preprocesses the collected driving data and converts it into the required format, which includes cleaning up the data by filtering out noise and extracting key indicators (e.g., frequency of hard braking).

[0668] Data transmission

[0669] The pre-processed data is then sent to a server via the internet. This communication is carried out periodically to ensure stability and security of the communication.

[0670] Server Processing

[0671] Data reception and storage

[0672] The server receives the driving data sent from the device and stores it in a database. For example, data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[0673] Data analysis

[0674] The server then uses an analysis algorithm to calculate an evaluation score based on the received data, including the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The resulting evaluation score is then used to calculate a driving skill score.

[0675] Insurance premium calculation and notification

[0676] The server calculates the appropriate insurance premium based on the analysis results. For example, based on User A's evaluation score, the premium is calculated to be 3,000 yen per month. The server then generates data to notify the user of the calculated premium in real time and sends it to the device. This notification is displayed on the smartphone or HMD.

[0677] Using generative AI models

[0678] Using driving data, the generative AI model generates prompts and calculates appropriate insurance premiums. For example, a prompt such as "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data" can be generated and sent to the AI ​​model, enabling highly accurate calculation of insurance premiums.

[0679] Specific examples

[0680] Example 1

[0681] For example, if it is detected that User A has a driving style that frequently involves sudden braking, that information is preprocessed in real time and sent to the server. The server analyzes the data and calculates the insurance premium based on a generative AI model that sets a higher premium for frequent sudden braking. As a result, "Current insurance premium: 3,500 yen / month" is displayed on the user's smartphone or HMD.

[0682] Example 2

[0683] Data during autonomous driving is also collected and analyzed in the same way. For example, if User B's vehicle is driving safely and efficiently in autonomous mode, that data is sent to the server, and the insurance premium is calculated lower due to safe driving. The prompt text based on the generative AI model is generated as follows: "User B recorded a mileage of 100 km in autonomous mode with no sudden braking. Please adjust the insurance premium accordingly." As a result, "Current insurance premium: 2,500 yen / month" is displayed.

[0684] As described above, this system enables real-time data collection and analysis, highly accurate insurance premium calculations, and is capable of providing users with timely and appropriate feedback.

[0685] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0686] Step 1:

[0687] The terminal collects driving data from the vehicle's GPS and multiple sensors. Specifically, it obtains current location information from the GPS and collects driving data such as the frequency of sudden braking and sudden acceleration, seat belt use, and turn signal use from the sensors. The input is raw data from the GPS and sensors. The output is a set of collected driving data.

[0688] Step 2:

[0689] The terminal preprocesses the collected driving data and converts it into the required format. Specifically, it performs noise filtering and data cleanup, and extracts key indicators such as the frequency of sudden braking and mileage. The input is the driving data collected in step 1. The output is a set of preprocessed driving data.

[0690] Step 3:

[0691] The terminal transmits the preprocessed data to the server via the Internet. This transmission occurs periodically to ensure communication stability and security. The input is the preprocessed data from step 2. The output is the data transmitted via the network.

[0692] Step 4:

[0693] The server receives the driving data sent from the terminal and stores it in a database. Specifically, it records the received data in a database for analysis. The input is the data sent in step 3. The output is the driving data stored in the database.

[0694] Step 5:

[0695] The server uses an analysis algorithm to calculate an evaluation score based on the received driving data. The score is calculated based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The input is driving data obtained from the database. The output is an evaluation score.

[0696] Step 6:

[0697] The server calculates an appropriate insurance premium based on the analysis results. At this time, it generates a prompt for the generative AI model and requests it to calculate the insurance premium. Specifically, based on data such as driving distance and frequency of sudden braking, it generates a prompt such as, "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data." The input is the evaluation score and driving data. The output is the generated prompt and the calculated insurance premium.

[0698] Step 7:

[0699] The server generates data to notify the calculated insurance premium in real time and sends it to the terminal. This data is to be displayed on the user's smartphone or HMD. The input is the calculated insurance premium. The output is the insurance premium notification data sent to the terminal.

[0700] Step 8:

[0701] The device receives the insurance premium notification and visually displays it to the user. Specifically, it displays a notification such as "Current insurance premium: 3,000 yen / month" on the device screen or HMD. The input is the insurance premium notification data received from the server. The output is the insurance premium information displayed to the user.

[0702] 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.

[0703] The automobile insurance fairness calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on vehicles, preprocesses and analyzes the data, and calculates and notifies the appropriate insurance premium for each driver. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score. Specific embodiments are described below.

[0704] Terminal handling

[0705] Data collection

[0706] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking attempts, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is used.

[0707] Collecting Emotional Data

[0708] The device is also equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expressions, allowing it to detect the user's emotional state, such as stress or impatience, in real time while driving.

[0709] Data Preprocessing

[0710] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[0711] Data transmission

[0712] The device transmits the pre-processed driving and emotion data to a server via the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data.

[0713] Server Processing

[0714] Data reception

[0715] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database.

[0716] Data analysis

[0717] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[0718] Insurance premium calculation

[0719] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[0720] Insurance premium notification

[0721] The server generates data to notify users of calculated insurance premiums in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[0722] User Action

[0723] Receive insurance premium notifications

[0724] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0725] User Feedback

[0726] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[0727] In this way, this system collects, pre-processes, and analyzes driving and emotional data, and calculates and notifies appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[0728] The processing flow will be explained below.

[0729] Step 1: Data collection

[0730] The device collects driving data from the vehicle's GPS and multiple sensors. For example, GPS records location information, driving route, and driving speed, while a brake sensor detects the number of sudden braking attempts. Furthermore, biometric sensors record heart rate and facial expressions in real time to collect the user's emotional state.

[0731] Step 2: Data Preprocessing

[0732] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[0733] Step 3: Send data

[0734] The device transmits the pre-processed data to a server over the Internet, either in real time or periodically, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[0735] Step 4: Receiving Data

[0736] The server receives the driving data and emotion data sent from the device and stores them in a temporary buffer. The received data is sequentially recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, emotion data, etc. are registered in the database.

[0737] Step 5: Data analysis

[0738] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and a high level of stress is detected while driving, an evaluation score is calculated based on that data.

[0739] Step 6: Premium calculation

[0740] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium will be set high.

[0741] Step 7: Premium Notification

[0742] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. For example, it generates notification data such as "Current insurance premium: 3,000 yen / month" and converts it into a format for visual display.

[0743] Step 8: Receive premium notification

[0744] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0745] Step 9: User Feedback

[0746] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to lower their insurance premiums by driving more calmly and in a relaxed state.

[0747] Example 2

[0748] 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."

[0749] Conventional automobile insurance calculation systems calculate insurance premiums based solely on the driver's driving data, without taking into account factors such as the driver's emotional state or stress level, limiting the ability to optimize insurance premiums. Furthermore, there is a lack of a system that reflects the driver's emotional data in real time, which creates the problem that the driver's psychological state is not reflected in the insurance premium. The present invention aims to solve these problems and realize more appropriate and fair insurance premium calculations.

[0750] 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.

[0751] In this invention, the server includes means for collecting driving data from multiple sensors and a location information system mounted on the vehicle, means for collecting biometric data such as the user's heart rate and facial expression to detect the emotional state while driving, and means for preprocessing the driving data and emotional data and transmitting the preprocessed data to a central processing unit via a network. This enables fair and appropriate insurance premium calculation based on detailed driving data including the driver's emotional state.

[0752] "Vehicle-mounted sensors" refers to multiple detection devices attached to a vehicle to collect various data while driving.

[0753] "Location information system" refers to a device that records location information such as a vehicle's current location, route, and speed.

[0754] "Driving data" refers to information related to the driving status of a vehicle, including, specifically, driving distance, driving speed, number of sudden braking attempts, whether seat belts are fastened, and whether turn signals are used.

[0755] "Preprocessing" refers to converting the collected raw data into an analyzable format and performing processes such as noise removal and adding timestamps.

[0756] "Central processing unit" refers to a processing unit such as a server that receives, analyzes, and stores data sent from a terminal.

[0757] "Analytics Algorithm" refers to the mathematical models and computational methods used to calculate the assessment score using the collected data.

[0758] The "evaluation score" is a numerical value calculated based on driving data and emotional data, and is an indicator that serves as the basis for calculating insurance premiums.

[0759] "Premium" refers to the amount a driver pays under an automobile insurance policy.

[0760] "Real-time" refers to data being collected, processed, and communicated immediately.

[0761] "Emotional state" refers to the psychological and physiological state of the driver while driving, and specifically includes emotions such as stress and impatience.

[0762] "Biometric data" refers to data that indicates the driver's physical state, such as the driver's heart rate or facial expression.

[0763] The automobile insurance fair amount calculation system of the present invention is a system that calculates and notifies the fair insurance premium for each driver by collecting driving data from multiple sensors and a location information system installed in the vehicle and preprocessing and analyzing the data. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score.

[0764] Terminal handling

[0765] Data collection

[0766] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, while the brake sensor detects the number of sudden braking events. The seatbelt sensor also checks whether the seatbelt is fastened, and the turn signal sensor detects whether the turn signal is being used. This data is temporarily stored in the device.

[0767] Collecting Emotional Data

[0768] The device is equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expression. For example, heart rate data is obtained from a smartwatch, and facial expression data is analyzed using an in-car camera. This allows the system to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data.

[0769] Data Preprocessing

[0770] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. Specifically, it performs noise filtering and removes unnecessary data. It also categorizes the collected data by timestamp and compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database.

[0771] Data transmission

[0772] The device transmits the preprocessed driving and emotion data to a server via the Internet, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[0773] Server Processing

[0774] Data reception

[0775] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then stored in a database, where, for example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotion data are registered.

[0776] Data analysis

[0777] The server then uses an analysis algorithm to analyze the received driving data and emotional data. The analysis algorithm uses software such as TensorFlow to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state as evaluation criteria, and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[0778] Insurance premium calculation

[0779] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[0780] Insurance premium notification

[0781] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[0782] User Action

[0783] Receive insurance premium notifications

[0784] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0785] User Feedback

[0786] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[0787] This will enable the system to collect, pre-process, and analyze driving and emotional data, and then calculate and notify appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[0788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0789] Step 1: Data collection

[0790] The device collects driving data from the vehicle's GPS and multiple sensors. As input, it obtains location information, driving route, and driving speed from the location information system, and obtains the number of sudden braking attempts, seat belt usage status, and turn signal usage status from various sensors. This data is temporarily stored in the device. In concrete terms, the GPS module periodically updates location information, and the sensors collect data in real time.

[0791] Step 2: Collecting emotion data

[0792] The device uses biometric sensors to collect biometric data such as the user's heart rate and facial expressions. It receives input from the smartwatch and in-car camera. This allows the device to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data. Specifically, the smartwatch updates the user's heart rate every second, and the camera analyzes facial expressions in real time.

[0793] Step 3: Data Preprocessing

[0794] The terminal preprocesses the collected driving and emotional data and converts it into an analyzable format. As input, it receives all data collected in the previous step. It performs noise filtering to remove unnecessary data and classifies the collected data by timestamp. As output, it compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database format. Specifically, a filtering algorithm is applied and the data is converted into an appropriate format.

[0795] Step 4: Send data

[0796] The terminal transmits the preprocessed driving data and emotion data to the server via the Internet. As input, it receives the database-formatted data generated in the previous step. During transmission, a security protocol (e.g., SSL / TLS) is used to ensure the confidentiality and integrity of the data. As output, transmission confirmation data is generated. Specifically, the data transmission module encrypts the data and transmits it to the server.

[0797] Step 5: Receiving Data

[0798] The server receives data sent from the terminal and stores it in a temporary buffer. As input, it receives data received via the network. As output, it prepares the data for storage in the database. In concrete terms, the data receiving module authenticates the data and stores it in a temporary buffer.

[0799] Step 6: Data analysis

[0800] The server analyzes the received driving data and emotion data using an analysis algorithm. As input, it receives all data stored in the buffer. It uses the analysis algorithm (e.g., a TensorFlow model) to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state, and calculates an evaluation score. As output, an individual evaluation score is generated. Specifically, the algorithm processes the data and calculates the evaluation score.

[0801] Step 7: Premium calculation

[0802] The server calculates the appropriate insurance premium for each driver based on the analysis results. As input, it receives the evaluation score generated in the previous step. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. As output, individual insurance premium data is generated. In specific operation, the evaluation score is substituted into the insurance premium calculation formula to calculate the optimal insurance premium.

[0803] Step 8: Premium Notification

[0804] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. Insurance premium data is received as input. Data converted into a notification format is generated as output. Specifically, the notification data is sent to the terminal and converted into a format that the user can view.

[0805] Step 9: Receive premium notification

[0806] The terminal receives the insurance premium notification data sent from the server and displays it to the user. As input, it receives the notification data sent from the server. As output, it displays it on the screen in a format that the user can check. Specifically, the terminal's screen display module converts the data into a display format and notifies the user.

[0807] Step 10: User feedback

[0808] The user can review their driving style and emotional management based on the notified insurance premium. As input, they check the insurance premium data displayed on the notification screen. As output, improved driving behavior and emotional management methods are implemented. As a specific action, the user changes their driving style or adopts new methods for stress management.

[0809] (Application example 2)

[0810] 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."

[0811] Conventional automobile insurance premium calculation systems do not fully take into account the driver's driving skills and biometric data, making it difficult to accurately calculate insurance premiums based on actual risk.In addition, there is a lack of a mechanism to notify drivers of the appropriateness of their insurance premiums in real time, which limits opportunities for drivers to review their driving style and emotional management.

[0812] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0813] In this invention, the server includes: means for collecting driving data from multiple sensors and a location information system mounted on the vehicle; means for preprocessing the driving data and biometric data and converting them into an analyzable data format; means for transmitting the preprocessed driving data and biometric data to the server via a network; means for storing the transmitted data in a database and calculating an evaluation score using an analysis algorithm; means for calculating insurance premiums for each driver based on the evaluation score; means for notifying the calculated insurance premiums in real time; and means for displaying the appropriate premium on the user's terminal, allowing the user to review their driving style and emotional state. This enables fair calculation of insurance premiums based on the driver's actual driving skills and emotional state, thereby promoting safe driving by drivers.

[0814] "Multiple sensors mounted on a vehicle" refers to various devices installed to measure and record the vehicle's operating status, including brake sensors, seat belt sensors, turn signal sensors, etc.

[0815] A "location information system" is a device that uses satellite navigation technology to measure a vehicle's location and collect data in real time.

[0816] "Driving data" refers to information collected while a vehicle is in operation, such as speed, location, and number of sudden braking attempts.

[0817] "Biometric data" refers to data that indicates the driver's physical and psychological state, such as heart rate and facial expressions.

[0818] "Preprocessing" refers to a series of data preparation procedures, such as data filtering and time stamping, to convert collected data into an analyzable format.

[0819] "Network" refers to a system that transmits and receives data using communication infrastructure such as the Internet.

[0820] The "server" is a computer system that receives driving data and biometric data, processes them using analytical algorithms, and stores them.

[0821] A "database" is a collection of information that systematically organizes and stores received data, making it easy to later analyze and search.

[0822] "Analysis algorithm" refers to the mathematical method or calculation program used to calculate an evaluation score based on collected driving data and biometric data.

[0823] The "evaluation score" is a numerical index calculated based on the analysis results of driving data and biometric data, and is a standard for evaluating the driver's driving skills and emotional state.

[0824] "Premium" means the monetary consideration paid by a driver under an automobile insurance policy.

[0825] "Real-time notification" refers to a means of immediately transmitting information to the driver based on the results of data analysis.

[0826] "User device" refers to a device that can receive and display notifications, such as a driver's smartphone or smart glasses.

[0827] A system for carrying out the present invention utilizes multiple sensors and a location information system mounted on a vehicle to collect and analyze driving data and biological data. A specific embodiment of the system is described below.

[0828] Hardware Configuration

[0829] 1. Vehicle sensors

[0830] GPS location system:

[0831] Record the vehicle's location, route, and speed.

[0832] Brake Sensor:

[0833] Detects the number of sudden braking events.

[0834] Seat belt sensor:

[0835] Check that the seat belt is fastened properly.

[0836] Turn Signal Sensor:

[0837] Detects turn signal usage.

[0838] Biometric Sensors:

[0839] It collects biometric data such as heart rate and facial expressions.

[0840] 2. User's Device

[0841] Smartphone:

[0842] Display and notify data.

[0843] Smart Glasses:

[0844] Display and notify data in real time.

[0845] Software Configuration

[0846] A program is installed on both the server and the terminal, and performs the following processes:

[0847] Terminal handling

[0848] 1. Data Collection

[0849] The device collects driving and biometric data in real time from sensors and location systems installed in the vehicle.

[0850] 2. Data Preprocessing

[0851] The terminal pre-processes the collected data, filtering out noise, time-stamping it and converting it into an organized format.

[0852] 3. Data Transmission

[0853] The pre-processed data is sent to a server over the Internet using security protocols (SSL / TLS).

[0854] Server Processing

[0855] 1. Data Reception

[0856] The server receives data sent from the terminal and stores it in a temporary buffer. The received data is then recorded in a database sequentially.

[0857] 2. Data Analysis

[0858] The server uses an analytical algorithm to analyze the driving data and biometric data, and calculates an overall evaluation score based on evaluation criteria such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on the biometric data.

[0859] 3. Insurance premium calculation

[0860] Based on the analysis results, the appropriate insurance premium is calculated for each driver. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user.

[0861] 4. Notification of insurance premiums

[0862] The server notifies the user of the calculated insurance premium in real time. The premium notification data for each user is converted into a format suitable for display on the screen and then sent.

[0863] User Action

[0864] 1. Receiving premium notices

[0865] The user's terminal receives the insurance premium notification data sent from the server and displays the insurance premium in real time.

[0866] 2. User Feedback

[0867] The user can review his / her driving style and emotional management based on the notified insurance premium.

[0868] Specific examples

[0869] For example, if an autonomous vehicle repeatedly brakes suddenly while driving on a highway, causing the driver's heart rate to rise, this data will be collected and analyzed and reflected in the insurance premium. A notification such as "Current insurance premium: 3,000 yen / month" will also be displayed on the user's smartphone.

[0870] Example prompts for the generative AI

[0871] "I would like to develop an application that collects driving data from autonomous vehicles and driver biometric data, analyzes it in real time, and calculates and notifies appropriate insurance premiums. Please tell me the types of sensors required, the data preprocessing method, and details of the insurance premium calculation algorithm."

[0872] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0873] Step 1:

[0874] The device collects driving data and biometric data in real time from the vehicle's onboard location information system and multiple sensors. Specifically, it obtains current coordinate data, driving route, and driving speed from the location information system, the number of sudden braking events from the brake sensor, and heart rate and facial expression data from the biometric sensor.

[0875] Step 2:

[0876] The device preprocesses the collected driving data and biometric data. Specifically, it performs noise filtering and removes unnecessary data. It also adds timestamps to organize and classify the data. This preprocessing converts the data into an analyzable format. For example, indicators such as driving distance, frequency of sudden braking, whether or not a speeding violation occurred, and emotional state based on biometric data can be extracted.

[0877] Step 3:

[0878] The terminal transmits the preprocessed driving data and biometric data to a server via the Internet. Security protocols (SSL / TLS) are used for transmission to ensure confidentiality and integrity of the data. The input at this stage is the preprocessed data, and the output is the data transmission to the server.

[0879] Step 4:

[0880] The server receives the data sent from the device and stores it in a temporary buffer. It then records it sequentially in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database. The input of this step is the data sent from the device, and the output is the record in the database.

[0881] Step 5:

[0882] The server analyzes the data recorded in the database using an analytical algorithm. Specifically, factors such as driving distance, frequency of sudden braking, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. The analytical algorithm quantifies each factor and calculates an overall evaluation score. The input of this step is the database data, and the output is the evaluation score.

[0883] Step 6:

[0884] The server calculates the insurance premium for each driver based on the evaluation score. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and has many high stress states, the insurance premium will be set high. The input of this step is the evaluation score, and the output is the calculated insurance premium.

[0885] Step 7:

[0886] The server notifies the terminal of the calculated insurance premium in real time. The notification data is converted into a format for screen display and sent. For example, a specific notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen. The input of this step is the calculated insurance premium, and the output is the notification data.

[0887] Step 8:

[0888] The user's terminal receives the insurance premium notification data sent from the server and displays it to the user. This allows the user to review their driving style and emotional management. Based on the notified insurance premium, the user can make efforts to lower the insurance premium by driving safely. The input of this step is the notification data, and the output is the insurance premium displayed on the screen.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] [Third embodiment]

[0893] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0894] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0895] 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).

[0896] 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.

[0897] 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.

[0898] 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).

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] 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.

[0904] 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."

[0905] The automobile insurance fair amount calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on a vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver. Specific embodiments are described below.

[0906] Terminal handling

[0907] Data collection

[0908] The device collects driving data (location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt usage status, turn signal usage status, etc.) from the vehicle's GPS and multiple sensors. For example, while the vehicle is driving, the GPS constantly records location information, and the brake sensor detects the number of sudden braking events.

[0909] Data Preprocessing

[0910] The device preprocesses the collected driving data and converts it into the required format, including cleaning up the data by filtering out noise and extracting key indicators such as mileage and frequency of hard braking.

[0911] Data transmission

[0912] The device then transmits the pre-processed data to a server over the Internet, which is done periodically to ensure communication stability and security.

[0913] Server Processing

[0914] Data reception

[0915] The server receives the driving data sent from the device and stores it in a database. For example, the data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[0916] Data analysis

[0917] The server analyzes the received driving data using an analytical algorithm. In this analysis, evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. For example, if user A's driving distance in one day is 50 km and he or she braked suddenly five times, the data is evaluated and a driving skill score is calculated.

[0918] Insurance premium calculation

[0919] The server calculates an appropriate insurance premium based on the analysis results. For example, the insurance premium calculated based on User A's evaluation score is 3,000 yen / month.

[0920] Insurance premium notification

[0921] The server generates data to notify the user of the calculated insurance premium in real time and transmits it to the terminal.

[0922] User Action

[0923] Receive insurance premium notifications

[0924] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user. For example, "Current insurance premium: 3000 yen / month" is displayed on the terminal screen.

[0925] User Feedback

[0926] Users can review their driving style based on the notified insurance premium. For example, if they know that they frequently apply sudden brakes, they will be able to develop driving habits to avoid this and become more conscious of safe driving.

[0927] As described above, this system collects, pre-processes, and analyzes driving data in real time, enabling appropriate insurance premiums to be calculated and notified. This is expected to reflect drivers' driving behavior more fairly and promote safe driving.

[0928] The processing flow will be explained below.

[0929] Step 1: Data collection

[0930] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking events, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is being used.

[0931] Step 2: Data Preprocessing

[0932] The device preprocesses the collected data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by the time of recording. For example, it extracts information such as driving distance, frequency of sudden braking, and whether or not there was a speeding violation, and compiles it into a database.

[0933] Step 3: Send data

[0934] The device transmits the pre-processed data to a server over the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data during transmission.

[0935] Step 4: Receiving Data

[0936] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, information such as User A's driving distance, number of sudden braking attempts, and speeding violations are registered in the database.

[0937] Step 5: Data analysis

[0938] The server uses an analysis algorithm to analyze the received driving data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day and brakes suddenly five times during that time, the evaluation score is calculated based on that data.

[0939] Step 6: Premium calculation

[0940] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A's overall evaluation score is low, the insurance premium may be set high.

[0941] Step 7: Premium Notification

[0942] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for screen display and sent.

[0943] Step 8: Receive premium notification

[0944] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[0945] Step 9: User Feedback

[0946] Users can review their driving style based on the insurance premiums they receive. For example, if a user sees the notification and decides that their insurance premiums are high because they brake suddenly a lot, they can make an effort to drive more calmly in the future to lower their insurance premiums.

[0947] Example 1

[0948] 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."

[0949] Conventional automobile insurance systems have difficulty accurately reflecting a driver's driving behavior, and insurance premiums are often set at a flat rate. This creates the problem that drivers who are mindful of safe driving are not fairly evaluated. Furthermore, conventional systems are unable to notify insurance premiums in real time, which can result in drivers missing an opportunity to review their driving behavior.

[0950] 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.

[0951] In this invention, the server includes a means for collecting driving data from multiple sensors and GPS mounted on the vehicle, a means for converting the data into an analyzable data format by noise filtering and extracting key indicators, and a means for transmitting the data to the server via the Internet, which makes it possible to calculate appropriate insurance premiums for each driver and notify them in real time.

[0952] "Driving data" refers to data such as location information obtained from sensors and GPS installed in the vehicle, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are used, and whether turn signals are used.

[0953] "Preprocessing" refers to the process of filtering out noise from collected driving data, extracting important indicators, and converting the data into an analyzable format.

[0954] "Data transmission" refers to the process of transmitting the pre-processed data over the Internet to a server.

[0955] "Data reception" refers to the process in which the server receives driving data sent from the terminal and stores it in a database.

[0956] "Analysis Algorithm" means the mathematical and statistical methods and procedures used to calculate a driver's evaluation score using collected driving data.

[0957] "Evaluation score" refers to a numerical value calculated based on an analytical algorithm that evaluates the safety and appropriateness of a driver's driving behavior.

[0958] "Premium" refers to the automobile insurance premium payable by the driver, calculated based on the rating score.

[0959] "Notification" refers to the process of communicating information to inform users of calculated insurance premiums in real time.

[0960] The automobile insurance fair amount calculation system of the present invention is a system that collects driving data from multiple sensors and GPS installed in the vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver.

[0961] Terminal handling

[0962] Data collection

[0963] The device collects driving data from a GPS system and multiple sensors. The hardware used in this process includes the u-blox Neo-7M GPS system, brake sensors, and an OBD-II interface. These sensors collect information such as location, route, speed, frequency of sudden braking and acceleration, seatbelt use, and turn signal use.

[0964] Specific examples

[0965] For example, while the vehicle is moving, GPS records its location every second, the brake sensor detects the number of sudden braking attempts, and the vehicle speed is acquired using the OBD-II interface.

[0966] Data Preprocessing

[0967] The device preprocesses the collected driving data and converts it into the required format. For preprocessing, Python scripts and the SciPy library are used to clean up the data by filtering noise. Key indicators such as mileage and frequency of sudden braking are then calculated and extracted.

[0968] Specific examples

[0969] Noise filtering uses a low-pass filter from the SciPy library to remove outliers (spikes) from the location data, after which distance traveled and frequency of hard braking are calculated.

[0970] Data transmission

[0971] The device sends the pre-processed data to the server over the Internet using an HTTP POST request using the "requests" library.

[0972] Specific examples

[0973] The preprocessed data is serialized in JSON format and sent periodically via HTTP POST request. For example, the sending interval is set to every 10 minutes.

[0974] Server Processing

[0975] Data reception

[0976] The server receives the driving data sent from the device and stores it in a database using an API endpoint using Flask, and the data is stored in a MySQL database.

[0977] Specific examples

[0978] The Flask API on the server receives the HTTP POST request, parses the data, and inserts it into a database table. For example, data from user "A" is stored in table "user_data."

[0979] Data analysis

[0980] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[0981] Specific examples

[0982] Features such as driving distance, frequency of sudden braking, frequency of sudden acceleration, and compliance with traffic rules are extracted, and an evaluation score is calculated using a random forest model. For example, if user "A" drives 50km per day and brakes suddenly five times, an analysis will be performed based on that data.

[0983] Insurance premium calculation

[0984] The server calculates the appropriate insurance premium based on the rating score, using a regression model for the premium.

[0985] Specific examples

[0986] For example, if the evaluation score is "80," the monthly insurance premium will be calculated based on that score, at 3,000 yen.

[0987] Insurance premium notification

[0988] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[0989] Specific examples

[0990] The calculated insurance premium is serialized in JSON format and sent to the terminal via an HTTP POST request. For example, the data "Insurance premium: 3000 yen / month" is sent.

[0991] User Action

[0992] Insurance premium display

[0993] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[0994] Specific examples

[0995] The terminal displays a notification message such as "Current insurance premium: 3,000 yen / month" on the LCD screen.

[0996] User Feedback

[0997] The user can review and improve his / her driving style based on the notified insurance premium.

[0998] Specific examples

[0999] For example, if you notice that you frequently slam on the brakes, you can consciously try to avoid doing so in order to improve your behavior.

[1000] Prompt Sentence Examples

[1001] "How can I design a system to calculate fair insurance premiums based on driving style data? Please include the following data: location, route, speed, frequency of sudden braking and acceleration, seat belt use, and turn signal use."

[1002] As described above, this system works in cooperation with the terminal and server to calculate and notify appropriate insurance premiums in real time, thereby raising awareness of safe driving.

[1003] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1004] Step 1: Data collection

[1005] Terminal handling

[1006] The terminal collects driving data from various sensors installed in the vehicle (e.g., GPS module, brake sensor, OBD-II interface). Specifically, the GPS module records location information every second, and the brake sensor counts the number of sudden braking events. Data collected through the OBD-II interface includes information such as driving speed and engine status.

[1007] Input and Output

[1008] Inputs: GPS module, brake sensor, raw data from OBD-II interface

[1009] Output: Collected driving data (location information, number of sudden braking, driving speed, etc.)

[1010] Specific examples

[1011] Whenever the vehicle is traveling, the GPS system records its location every second. For example, the current location is recorded as "Latitude: 35.6895, Longitude: 139.6917." In addition, the brake sensor counts each sudden braking as "1 sudden braking."

[1012] Step 2: Data Preprocessing

[1013] Terminal handling

[1014] The device preprocesses the collected driving data and converts it into an analyzable format. Specific preprocessing operations include noise filtering and data cleansing. Using the Python SciPy library, for example, outliers and spikes are removed. Furthermore, key indicators such as mileage and frequency of sudden braking are extracted.

[1015] Input and Output

[1016] Input: Collected raw driving data

[1017] Output: Clean data after preprocessing (filtered location information, number of hard braking incidents, mileage, etc.)

[1018] Specific examples

[1019] Noise filtering is performed on the location data to remove outliers. The distance traveled is also calculated from the location information, and the data is formatted as "Distance traveled: 50km".

[1020] Step 3: Send data

[1021] Terminal handling

[1022] The terminal sends the preprocessed data to the server via the Internet. Using the "requests" library, the data is sent by HTTP POST request.

[1023] Input and Output

[1024] Input: Preprocessed operating data

[1025] Output: Data sent to the server in JSON format

[1026] Specific examples

[1027] The preprocessed data is converted to JSON format and sent using an HTTP POST request. For example, data such as "Distance traveled: 50km, Number of hard braking attempts: 5" is sent every 10 minutes.

[1028] Step 4: Receiving Data

[1029] Server Processing

[1030] The server receives the driving data sent from the device and stores it in a database using a Flask-based API endpoint, and the data is stored in a MySQL database.

[1031] Input and Output

[1032] Input: Driving data sent from the terminal

[1033] Output: Operation data stored in the database

[1034] Specific examples

[1035] When the server's Flask API receives the HTTP POST request, it parses the data and inserts it into the database's "user_data" table. For example, it stores "User ID: A, Mileage: 50km, Number of Hard Brakes: 5".

[1036] Step 5: Data analysis

[1037] Server Processing

[1038] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[1039] Input and Output

[1040] Input: Saved driving data

[1041] Output: Driver's rating score

[1042] Specific examples

[1043] The driving distance and frequency of sudden braking stored in the database are extracted as features and analyzed using a random forest model. For example, the result is calculated as "User ID: A, Evaluation score: 80."

[1044] Step 6: Premium calculation

[1045] Server Processing

[1046] The server calculates the appropriate insurance premium based on the rating score, using a regression model.

[1047] Input and Output

[1048] Input: Rating score

[1049] Output: Calculated insurance premium

[1050] Specific examples

[1051] If the evaluation score is 80, the monthly insurance premium will be calculated based on that score: 3,000 yen.

[1052] Step 7: Premium Notification

[1053] Server Processing

[1054] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[1055] Input and Output

[1056] Input: Calculated insurance premium

[1057] Output: Premium notification sent to the terminal

[1058] Specific examples

[1059] The calculated insurance premium is serialized in JSON format and sent to the terminal as "Insurance premium: 3,000 yen / month."

[1060] Step 8: View premiums

[1061] Terminal handling

[1062] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[1063] Input and Output

[1064] Input: Premium notification sent from the server

[1065] Output: Insurance premium displayed on the device screen

[1066] Specific examples

[1067] The message "Current insurance premium: 3,000 yen / month" is displayed in real time on the terminal's LCD screen.

[1068] Step 9: User Feedback

[1069] User Action

[1070] The user can review and improve his / her driving style based on the notified insurance premium.

[1071] Input and Output

[1072] Input: Insurance premium displayed on the terminal

[1073] Power: Improved driving style

[1074] Specific examples

[1075] For example, the goal is to lower insurance premiums by driving more carefully to reduce sudden braking.

[1076] (Application example 1)

[1077] 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."

[1078] In recent years, systems that collect and analyze driving data to calculate appropriate automobile insurance premiums have become widespread. However, conventional systems often require manual operation by the driver, making it difficult to calculate insurance premiums that take into account data from autonomous driving. Furthermore, since evaluation scores and premium notifications are not calculated in real time, it is difficult for drivers to obtain useful feedback. Furthermore, conventional systems do not utilize generative AI models, which hinders the accuracy of data analysis and premium calculation.

[1079] 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.

[1080] In this invention, the server includes means for collecting and analyzing driving data while the vehicle is driving autonomously, means for generating prompt messages using the driving data so that the generative AI model can calculate an appropriate insurance premium, means for calculating the evaluation score based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type, and the notification means includes means for displaying the insurance premium on a terminal screen or a head-mounted display. This enables a comprehensive evaluation that includes driving data during autonomous driving, and makes it possible to provide the driver with accurate and appropriate insurance premium feedback in real time.

[1081] A "vehicle" is a mechanical device that can travel on land, is usually equipped with an engine or motor, and is a vehicle for transporting people or goods.

[1082] A "sensor" is a device that detects physical conditions or changes and outputs them as electrical signals.

[1083] "GPS" is an abbreviation for Global Positioning System, a system that uses artificial satellites to measure and display positions on Earth.

[1084] "Driving data" refers to information relating to the driving conditions of a vehicle, including, specifically, location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are fastened, and whether turn signals are used.

[1085] "Preprocessing" refers to data processing operations to remove unnecessary noise from raw data and convert it into a format suitable for analysis.

[1086] An "analytic algorithm" is a computational procedure used to analyze collected data and find patterns and trends.

[1087] The "evaluation score" is a numerical representation of the driver's driving behavior based on the analysis results, and serves as the basis for calculating insurance premiums.

[1088] "Premium" means the amount payable by a driver for motor vehicle insurance, which is determined based on a risk assessment.

[1089] "Real-time" refers to processing and information provision occurring almost simultaneously with actual time.

[1090] "Autonomous driving" is a technology that allows vehicles to drive autonomously without human intervention, utilizing sensors and AI technology.

[1091] A "generative AI model" is a type of artificial intelligence designed to learn from large amounts of data and perform specific tasks.

[1092] A "prompt statement" is an input statement that instructs a generative AI model to perform a specific process, and explicitly states the conditions and requirements for analysis and calculation.

[1093] A "terminal" is an electronic device that a user directly operates to input and display information, including smartphones and head-mounted displays.

[1094] The insurance premium calculation system of this invention calculates appropriate insurance premiums for each driver by collecting driving data from multiple sensors and GPS installed in the vehicle. Implementing this system requires sensors, GPS, a smartphone, a head-mounted display (HMD), a server, and an AI model.

[1095] System Configuration

[1096] Terminal handling

[1097] Data collection

[1098] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, an application on the device records in real time location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt use, turn signal use, and other information.

[1099] Data Preprocessing

[1100] The device preprocesses the collected driving data and converts it into the required format, which includes cleaning up the data by filtering out noise and extracting key indicators (e.g., frequency of hard braking).

[1101] Data transmission

[1102] The pre-processed data is then sent to a server via the internet. This communication is carried out periodically to ensure stability and security of the communication.

[1103] Server Processing

[1104] Data reception and storage

[1105] The server receives the driving data sent from the device and stores it in a database. For example, data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[1106] Data analysis

[1107] The server then uses an analysis algorithm to calculate an evaluation score based on the received data, including the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The resulting evaluation score is then used to calculate a driving skill score.

[1108] Insurance premium calculation and notification

[1109] The server calculates the appropriate insurance premium based on the analysis results. For example, based on User A's evaluation score, the premium is calculated to be 3,000 yen per month. The server then generates data to notify the user of the calculated premium in real time and sends it to the device. This notification is displayed on the smartphone or HMD.

[1110] Using generative AI models

[1111] Using driving data, the generative AI model generates prompts and calculates appropriate insurance premiums. For example, a prompt such as "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data" can be generated and sent to the AI ​​model, enabling highly accurate calculation of insurance premiums.

[1112] Specific examples

[1113] Example 1

[1114] For example, if it is detected that User A has a driving style that frequently involves sudden braking, that information is preprocessed in real time and sent to the server. The server analyzes the data and calculates the insurance premium based on a generative AI model that sets a higher premium for frequent sudden braking. As a result, "Current insurance premium: 3,500 yen / month" is displayed on the user's smartphone or HMD.

[1115] Example 2

[1116] Data during autonomous driving is also collected and analyzed in the same way. For example, if User B's vehicle is driving safely and efficiently in autonomous mode, that data is sent to the server, and the insurance premium is calculated lower due to safe driving. The prompt text based on the generative AI model is generated as follows: "User B recorded a mileage of 100 km in autonomous mode with no sudden braking. Please adjust the insurance premium accordingly." As a result, "Current insurance premium: 2,500 yen / month" is displayed.

[1117] As described above, this system enables real-time data collection and analysis, highly accurate insurance premium calculations, and is capable of providing users with timely and appropriate feedback.

[1118] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1119] Step 1:

[1120] The terminal collects driving data from the vehicle's GPS and multiple sensors. Specifically, it obtains current location information from the GPS and collects driving data such as the frequency of sudden braking and sudden acceleration, seat belt use, and turn signal use from the sensors. The input is raw data from the GPS and sensors. The output is a set of collected driving data.

[1121] Step 2:

[1122] The terminal preprocesses the collected driving data and converts it into the required format. Specifically, it performs noise filtering and data cleanup, and extracts key indicators such as the frequency of sudden braking and mileage. The input is the driving data collected in step 1. The output is a set of preprocessed driving data.

[1123] Step 3:

[1124] The terminal transmits the preprocessed data to the server via the Internet. This transmission occurs periodically to ensure communication stability and security. The input is the preprocessed data from step 2. The output is the data transmitted via the network.

[1125] Step 4:

[1126] The server receives the driving data sent from the terminal and stores it in a database. Specifically, it records the received data in a database for analysis. The input is the data sent in step 3. The output is the driving data stored in the database.

[1127] Step 5:

[1128] The server uses an analysis algorithm to calculate an evaluation score based on the received driving data. The score is calculated based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The input is driving data obtained from the database. The output is an evaluation score.

[1129] Step 6:

[1130] The server calculates an appropriate insurance premium based on the analysis results. At this time, it generates a prompt for the generative AI model and requests it to calculate the insurance premium. Specifically, based on data such as driving distance and frequency of sudden braking, it generates a prompt such as, "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data." The input is the evaluation score and driving data. The output is the generated prompt and the calculated insurance premium.

[1131] Step 7:

[1132] The server generates data to notify the calculated insurance premium in real time and sends it to the terminal. This data is to be displayed on the user's smartphone or HMD. The input is the calculated insurance premium. The output is the insurance premium notification data sent to the terminal.

[1133] Step 8:

[1134] The device receives the insurance premium notification and visually displays it to the user. Specifically, it displays a notification such as "Current insurance premium: 3,000 yen / month" on the device screen or HMD. The input is the insurance premium notification data received from the server. The output is the insurance premium information displayed to the user.

[1135] 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.

[1136] The automobile insurance fairness calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on vehicles, preprocesses and analyzes the data, and calculates and notifies the appropriate insurance premium for each driver. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score. Specific embodiments are described below.

[1137] Terminal handling

[1138] Data collection

[1139] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking attempts, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is used.

[1140] Collecting Emotional Data

[1141] The device is also equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expressions, allowing it to detect the user's emotional state, such as stress or impatience, in real time while driving.

[1142] Data Preprocessing

[1143] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[1144] Data transmission

[1145] The device transmits the pre-processed driving and emotion data to a server via the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data.

[1146] Server Processing

[1147] Data reception

[1148] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database.

[1149] Data analysis

[1150] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[1151] Insurance premium calculation

[1152] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[1153] Insurance premium notification

[1154] The server generates data to notify users of calculated insurance premiums in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[1155] User Action

[1156] Receive insurance premium notifications

[1157] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[1158] User Feedback

[1159] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[1160] In this way, this system collects, pre-processes, and analyzes driving and emotional data, and calculates and notifies appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[1161] The processing flow will be explained below.

[1162] Step 1: Data collection

[1163] The device collects driving data from the vehicle's GPS and multiple sensors. For example, GPS records location information, driving route, and driving speed, while a brake sensor detects the number of sudden braking attempts. Furthermore, biometric sensors record heart rate and facial expressions in real time to collect the user's emotional state.

[1164] Step 2: Data Preprocessing

[1165] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[1166] Step 3: Send data

[1167] The device transmits the pre-processed data to a server over the Internet, either in real time or periodically, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[1168] Step 4: Receiving Data

[1169] The server receives the driving data and emotion data sent from the device and stores them in a temporary buffer. The received data is sequentially recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, emotion data, etc. are registered in the database.

[1170] Step 5: Data analysis

[1171] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and a high level of stress is detected while driving, an evaluation score is calculated based on that data.

[1172] Step 6: Premium calculation

[1173] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium will be set high.

[1174] Step 7: Premium Notification

[1175] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. For example, it generates notification data such as "Current insurance premium: 3,000 yen / month" and converts it into a format for visual display.

[1176] Step 8: Receive premium notification

[1177] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[1178] Step 9: User Feedback

[1179] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to lower their insurance premiums by driving more calmly and in a relaxed state.

[1180] Example 2

[1181] 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."

[1182] Conventional automobile insurance calculation systems calculate insurance premiums based solely on the driver's driving data, without taking into account factors such as the driver's emotional state or stress level, limiting the ability to optimize insurance premiums. Furthermore, there is a lack of a system that reflects the driver's emotional data in real time, which creates the problem that the driver's psychological state is not reflected in the insurance premium. The present invention aims to solve these problems and realize more appropriate and fair insurance premium calculations.

[1183] 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.

[1184] In this invention, the server includes means for collecting driving data from multiple sensors and a location information system mounted on the vehicle, means for collecting biometric data such as the user's heart rate and facial expression to detect the emotional state while driving, and means for preprocessing the driving data and emotional data and transmitting the preprocessed data to a central processing unit via a network. This enables fair and appropriate insurance premium calculation based on detailed driving data including the driver's emotional state.

[1185] "Vehicle-mounted sensors" refers to multiple detection devices attached to a vehicle to collect various data while driving.

[1186] "Location information system" refers to a device that records location information such as a vehicle's current location, route, and speed.

[1187] "Driving data" refers to information related to the driving status of a vehicle, including, specifically, driving distance, driving speed, number of sudden braking attempts, whether seat belts are fastened, and whether turn signals are used.

[1188] "Preprocessing" refers to converting the collected raw data into an analyzable format and performing processes such as noise removal and adding timestamps.

[1189] "Central processing unit" refers to a processing unit such as a server that receives, analyzes, and stores data sent from a terminal.

[1190] "Analytics Algorithm" refers to the mathematical models and computational methods used to calculate the assessment score using the collected data.

[1191] The "evaluation score" is a numerical value calculated based on driving data and emotional data, and is an indicator that serves as the basis for calculating insurance premiums.

[1192] "Premium" refers to the amount a driver pays under an automobile insurance policy.

[1193] "Real-time" refers to data being collected, processed, and communicated immediately.

[1194] "Emotional state" refers to the psychological and physiological state of the driver while driving, and specifically includes emotions such as stress and impatience.

[1195] "Biometric data" refers to data that indicates the driver's physical state, such as the driver's heart rate or facial expression.

[1196] The automobile insurance fair amount calculation system of the present invention is a system that calculates and notifies the fair insurance premium for each driver by collecting driving data from multiple sensors and a location information system installed in the vehicle and preprocessing and analyzing the data. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score.

[1197] Terminal handling

[1198] Data collection

[1199] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, while the brake sensor detects the number of sudden braking events. The seatbelt sensor also checks whether the seatbelt is fastened, and the turn signal sensor detects whether the turn signal is being used. This data is temporarily stored in the device.

[1200] Collecting Emotional Data

[1201] The device is equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expression. For example, heart rate data is obtained from a smartwatch, and facial expression data is analyzed using an in-car camera. This allows the system to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data.

[1202] Data Preprocessing

[1203] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. Specifically, it performs noise filtering and removes unnecessary data. It also categorizes the collected data by timestamp and compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database.

[1204] Data transmission

[1205] The device transmits the preprocessed driving and emotion data to a server via the Internet, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[1206] Server Processing

[1207] Data reception

[1208] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then stored in a database, where, for example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotion data are registered.

[1209] Data analysis

[1210] The server then uses an analysis algorithm to analyze the received driving data and emotional data. The analysis algorithm uses software such as TensorFlow to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state as evaluation criteria, and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[1211] Insurance premium calculation

[1212] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[1213] Insurance premium notification

[1214] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[1215] User Action

[1216] Receive insurance premium notifications

[1217] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[1218] User Feedback

[1219] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[1220] This will enable the system to collect, pre-process, and analyze driving and emotional data, and then calculate and notify appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[1221] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1222] Step 1: Data collection

[1223] The device collects driving data from the vehicle's GPS and multiple sensors. As input, it obtains location information, driving route, and driving speed from the location information system, and obtains the number of sudden braking attempts, seat belt usage status, and turn signal usage status from various sensors. This data is temporarily stored in the device. In concrete terms, the GPS module periodically updates location information, and the sensors collect data in real time.

[1224] Step 2: Collecting emotion data

[1225] The device uses biometric sensors to collect biometric data such as the user's heart rate and facial expressions. It receives input from the smartwatch and in-car camera. This allows the device to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data. Specifically, the smartwatch updates the user's heart rate every second, and the camera analyzes facial expressions in real time.

[1226] Step 3: Data Preprocessing

[1227] The terminal preprocesses the collected driving and emotional data and converts it into an analyzable format. As input, it receives all data collected in the previous step. It performs noise filtering to remove unnecessary data and classifies the collected data by timestamp. As output, it compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database format. Specifically, a filtering algorithm is applied and the data is converted into an appropriate format.

[1228] Step 4: Send data

[1229] The terminal transmits the preprocessed driving data and emotion data to the server via the Internet. As input, it receives the database-formatted data generated in the previous step. During transmission, a security protocol (e.g., SSL / TLS) is used to ensure the confidentiality and integrity of the data. As output, transmission confirmation data is generated. Specifically, the data transmission module encrypts the data and transmits it to the server.

[1230] Step 5: Receiving Data

[1231] The server receives data sent from the terminal and stores it in a temporary buffer. As input, it receives data received via the network. As output, it prepares the data for storage in the database. In concrete terms, the data receiving module authenticates the data and stores it in a temporary buffer.

[1232] Step 6: Data analysis

[1233] The server analyzes the received driving data and emotion data using an analysis algorithm. As input, it receives all data stored in the buffer. It uses the analysis algorithm (e.g., a TensorFlow model) to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state, and calculates an evaluation score. As output, an individual evaluation score is generated. Specifically, the algorithm processes the data and calculates the evaluation score.

[1234] Step 7: Premium calculation

[1235] The server calculates the appropriate insurance premium for each driver based on the analysis results. As input, it receives the evaluation score generated in the previous step. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. As output, individual insurance premium data is generated. In specific operation, the evaluation score is substituted into the insurance premium calculation formula to calculate the optimal insurance premium.

[1236] Step 8: Premium Notification

[1237] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. Insurance premium data is received as input. Data converted into a notification format is generated as output. Specifically, the notification data is sent to the terminal and converted into a format that the user can view.

[1238] Step 9: Receive premium notification

[1239] The terminal receives the insurance premium notification data sent from the server and displays it to the user. As input, it receives the notification data sent from the server. As output, it displays it on the screen in a format that the user can check. Specifically, the terminal's screen display module converts the data into a display format and notifies the user.

[1240] Step 10: User feedback

[1241] The user can review their driving style and emotional management based on the notified insurance premium. As input, they check the insurance premium data displayed on the notification screen. As output, improved driving behavior and emotional management methods are implemented. As a specific action, the user changes their driving style or adopts new methods for stress management.

[1242] (Application example 2)

[1243] 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."

[1244] Conventional automobile insurance premium calculation systems do not fully take into account the driver's driving skills and biometric data, making it difficult to accurately calculate insurance premiums based on actual risk.In addition, there is a lack of a mechanism to notify drivers of the appropriateness of their insurance premiums in real time, which limits opportunities for drivers to review their driving style and emotional management.

[1245] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1246] In this invention, the server includes: means for collecting driving data from multiple sensors and a location information system mounted on the vehicle; means for preprocessing the driving data and biometric data and converting them into an analyzable data format; means for transmitting the preprocessed driving data and biometric data to the server via a network; means for storing the transmitted data in a database and calculating an evaluation score using an analysis algorithm; means for calculating insurance premiums for each driver based on the evaluation score; means for notifying the calculated insurance premiums in real time; and means for displaying the appropriate premium on the user's terminal, allowing the user to review their driving style and emotional state. This enables fair calculation of insurance premiums based on the driver's actual driving skills and emotional state, thereby promoting safe driving by drivers.

[1247] "Multiple sensors mounted on a vehicle" refers to various devices installed to measure and record the vehicle's operating status, including brake sensors, seat belt sensors, turn signal sensors, etc.

[1248] A "location information system" is a device that uses satellite navigation technology to measure a vehicle's location and collect data in real time.

[1249] "Driving data" refers to information collected while a vehicle is in operation, such as speed, location, and number of sudden braking attempts.

[1250] "Biometric data" refers to data that indicates the driver's physical and psychological state, such as heart rate and facial expressions.

[1251] "Preprocessing" refers to a series of data preparation procedures, such as data filtering and time stamping, to convert collected data into an analyzable format.

[1252] "Network" refers to a system that transmits and receives data using communication infrastructure such as the Internet.

[1253] The "server" is a computer system that receives driving data and biometric data, processes them using analytical algorithms, and stores them.

[1254] A "database" is a collection of information that systematically organizes and stores received data, making it easy to later analyze and search.

[1255] "Analysis algorithm" refers to the mathematical method or calculation program used to calculate an evaluation score based on collected driving data and biometric data.

[1256] The "evaluation score" is a numerical index calculated based on the analysis results of driving data and biometric data, and is a standard for evaluating the driver's driving skills and emotional state.

[1257] "Premium" means the monetary consideration paid by a driver under an automobile insurance policy.

[1258] "Real-time notification" refers to a means of immediately transmitting information to the driver based on the results of data analysis.

[1259] "User device" refers to a device that can receive and display notifications, such as a driver's smartphone or smart glasses.

[1260] A system for carrying out the present invention utilizes multiple sensors and a location information system mounted on a vehicle to collect and analyze driving data and biological data. A specific embodiment of the system is described below.

[1261] Hardware Configuration

[1262] 1. Vehicle sensors

[1263] GPS location system:

[1264] Record the vehicle's location, route, and speed.

[1265] Brake Sensor:

[1266] Detects the number of sudden braking events.

[1267] Seat belt sensor:

[1268] Check that the seat belt is fastened properly.

[1269] Turn Signal Sensor:

[1270] Detects turn signal usage.

[1271] Biometric Sensors:

[1272] It collects biometric data such as heart rate and facial expressions.

[1273] 2. User's Device

[1274] Smartphone:

[1275] Display and notify data.

[1276] Smart Glasses:

[1277] Display and notify data in real time.

[1278] Software Configuration

[1279] A program is installed on both the server and the terminal, and performs the following processes:

[1280] Terminal handling

[1281] 1. Data Collection

[1282] The device collects driving and biometric data in real time from sensors and location systems installed in the vehicle.

[1283] 2. Data Preprocessing

[1284] The terminal pre-processes the collected data, filtering out noise, time-stamping it and converting it into an organized format.

[1285] 3. Data Transmission

[1286] The pre-processed data is sent to a server over the Internet using security protocols (SSL / TLS).

[1287] Server Processing

[1288] 1. Data Reception

[1289] The server receives data sent from the terminal and stores it in a temporary buffer. The received data is then recorded in a database sequentially.

[1290] 2. Data Analysis

[1291] The server uses an analytical algorithm to analyze the driving data and biometric data, and calculates an overall evaluation score based on evaluation criteria such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on the biometric data.

[1292] 3. Insurance premium calculation

[1293] Based on the analysis results, the appropriate insurance premium is calculated for each driver. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user.

[1294] 4. Notification of insurance premiums

[1295] The server notifies the user of the calculated insurance premium in real time. The premium notification data for each user is converted into a format suitable for display on the screen and then sent.

[1296] User Action

[1297] 1. Receiving premium notices

[1298] The user's terminal receives the insurance premium notification data sent from the server and displays the insurance premium in real time.

[1299] 2. User Feedback

[1300] The user can review his / her driving style and emotional management based on the notified insurance premium.

[1301] Specific examples

[1302] For example, if an autonomous vehicle repeatedly brakes suddenly while driving on a highway, causing the driver's heart rate to rise, this data will be collected and analyzed and reflected in the insurance premium. A notification such as "Current insurance premium: 3,000 yen / month" will also be displayed on the user's smartphone.

[1303] Example prompts for the generative AI

[1304] "I would like to develop an application that collects driving data from autonomous vehicles and driver biometric data, analyzes it in real time, and calculates and notifies appropriate insurance premiums. Please tell me the types of sensors required, the data preprocessing method, and details of the insurance premium calculation algorithm."

[1305] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1306] Step 1:

[1307] The device collects driving data and biometric data in real time from the vehicle's onboard location information system and multiple sensors. Specifically, it obtains current coordinate data, driving route, and driving speed from the location information system, the number of sudden braking events from the brake sensor, and heart rate and facial expression data from the biometric sensor.

[1308] Step 2:

[1309] The device preprocesses the collected driving data and biometric data. Specifically, it performs noise filtering and removes unnecessary data. It also adds timestamps to organize and classify the data. This preprocessing converts the data into an analyzable format. For example, indicators such as driving distance, frequency of sudden braking, whether or not a speeding violation occurred, and emotional state based on biometric data can be extracted.

[1310] Step 3:

[1311] The terminal transmits the preprocessed driving data and biometric data to a server via the Internet. Security protocols (SSL / TLS) are used for transmission to ensure confidentiality and integrity of the data. The input at this stage is the preprocessed data, and the output is the data transmission to the server.

[1312] Step 4:

[1313] The server receives the data sent from the device and stores it in a temporary buffer. It then records it sequentially in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database. The input of this step is the data sent from the device, and the output is the record in the database.

[1314] Step 5:

[1315] The server analyzes the data recorded in the database using an analytical algorithm. Specifically, factors such as driving distance, frequency of sudden braking, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. The analytical algorithm quantifies each factor and calculates an overall evaluation score. The input of this step is the database data, and the output is the evaluation score.

[1316] Step 6:

[1317] The server calculates the insurance premium for each driver based on the evaluation score. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and has many high stress states, the insurance premium will be set high. The input of this step is the evaluation score, and the output is the calculated insurance premium.

[1318] Step 7:

[1319] The server notifies the terminal of the calculated insurance premium in real time. The notification data is converted into a format for screen display and sent. For example, a specific notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen. The input of this step is the calculated insurance premium, and the output is the notification data.

[1320] Step 8:

[1321] The user's terminal receives the insurance premium notification data sent from the server and displays it to the user. This allows the user to review their driving style and emotional management. Based on the notified insurance premium, the user can make efforts to lower the insurance premium by driving safely. The input of this step is the notification data, and the output is the insurance premium displayed on the screen.

[1322] 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.

[1323] 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.

[1324] 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.

[1325] [Fourth embodiment]

[1326] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1327] 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.

[1328] 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).

[1329] 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.

[1330] 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.

[1331] 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).

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] 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."

[1339] The automobile insurance fair amount calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on a vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver. Specific embodiments are described below.

[1340] Terminal handling

[1341] Data collection

[1342] The device collects driving data (location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt usage status, turn signal usage status, etc.) from the vehicle's GPS and multiple sensors. For example, while the vehicle is driving, the GPS constantly records location information, and the brake sensor detects the number of sudden braking events.

[1343] Data Preprocessing

[1344] The device preprocesses the collected driving data and converts it into the required format, including cleaning up the data by filtering out noise and extracting key indicators such as mileage and frequency of hard braking.

[1345] Data transmission

[1346] The device then transmits the pre-processed data to a server over the Internet, which is done periodically to ensure communication stability and security.

[1347] Server Processing

[1348] Data reception

[1349] The server receives the driving data sent from the device and stores it in a database. For example, the data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[1350] Data analysis

[1351] The server analyzes the received driving data using an analytical algorithm. In this analysis, evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. For example, if user A's driving distance in one day is 50 km and he or she braked suddenly five times, the data is evaluated and a driving skill score is calculated.

[1352] Insurance premium calculation

[1353] The server calculates an appropriate insurance premium based on the analysis results. For example, the insurance premium calculated based on User A's evaluation score is 3,000 yen / month.

[1354] Insurance premium notification

[1355] The server generates data to notify the user of the calculated insurance premium in real time and transmits it to the terminal.

[1356] User Action

[1357] Receive insurance premium notifications

[1358] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user. For example, "Current insurance premium: 3000 yen / month" is displayed on the terminal screen.

[1359] User Feedback

[1360] Users can review their driving style based on the notified insurance premium. For example, if they know that they frequently apply sudden brakes, they will be able to develop driving habits to avoid this and become more conscious of safe driving.

[1361] As described above, this system collects, pre-processes, and analyzes driving data in real time, enabling appropriate insurance premiums to be calculated and notified. This is expected to reflect drivers' driving behavior more fairly and promote safe driving.

[1362] The processing flow will be explained below.

[1363] Step 1: Data collection

[1364] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking events, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is being used.

[1365] Step 2: Data Preprocessing

[1366] The device preprocesses the collected data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by the time of recording. For example, it extracts information such as driving distance, frequency of sudden braking, and whether or not there was a speeding violation, and compiles it into a database.

[1367] Step 3: Send data

[1368] The device transmits the pre-processed data to a server over the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data during transmission.

[1369] Step 4: Receiving Data

[1370] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, information such as User A's driving distance, number of sudden braking attempts, and speeding violations are registered in the database.

[1371] Step 5: Data analysis

[1372] The server uses an analysis algorithm to analyze the received driving data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, and vehicle type. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day and brakes suddenly five times during that time, the evaluation score is calculated based on that data.

[1373] Step 6: Premium calculation

[1374] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A's overall evaluation score is low, the insurance premium may be set high.

[1375] Step 7: Premium Notification

[1376] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for screen display and sent.

[1377] Step 8: Receive premium notification

[1378] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[1379] Step 9: User Feedback

[1380] Users can review their driving style based on the insurance premiums they receive. For example, if a user sees the notification and decides that their insurance premiums are high because they brake suddenly a lot, they can make an effort to drive more calmly in the future to lower their insurance premiums.

[1381] Example 1

[1382] 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."

[1383] Conventional automobile insurance systems have difficulty accurately reflecting a driver's driving behavior, and insurance premiums are often set at a flat rate. This creates the problem that drivers who are mindful of safe driving are not fairly evaluated. Furthermore, conventional systems are unable to notify insurance premiums in real time, which can result in drivers missing an opportunity to review their driving behavior.

[1384] 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.

[1385] In this invention, the server includes a means for collecting driving data from multiple sensors and GPS mounted on the vehicle, a means for converting the data into an analyzable data format by noise filtering and extracting key indicators, and a means for transmitting the data to the server via the Internet, which makes it possible to calculate appropriate insurance premiums for each driver and notify them in real time.

[1386] "Driving data" refers to data such as location information obtained from sensors and GPS installed in the vehicle, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are used, and whether turn signals are used.

[1387] "Preprocessing" refers to the process of filtering out noise from collected driving data, extracting important indicators, and converting the data into an analyzable format.

[1388] "Data transmission" refers to the process of transmitting the pre-processed data over the Internet to a server.

[1389] "Data reception" refers to the process in which the server receives driving data sent from the terminal and stores it in a database.

[1390] "Analysis Algorithm" means the mathematical and statistical methods and procedures used to calculate a driver's evaluation score using collected driving data.

[1391] "Evaluation score" refers to a numerical value calculated based on an analytical algorithm that evaluates the safety and appropriateness of a driver's driving behavior.

[1392] "Premium" refers to the automobile insurance premium payable by the driver, calculated based on the rating score.

[1393] "Notification" refers to the process of communicating information to inform users of calculated insurance premiums in real time.

[1394] The automobile insurance fair amount calculation system of the present invention is a system that collects driving data from multiple sensors and GPS installed in the vehicle, preprocesses and analyzes the data, and calculates and notifies the fair insurance premium for each driver.

[1395] Terminal handling

[1396] Data collection

[1397] The device collects driving data from a GPS system and multiple sensors. The hardware used in this process includes the u-blox Neo-7M GPS system, brake sensors, and an OBD-II interface. These sensors collect information such as location, route, speed, frequency of sudden braking and acceleration, seatbelt use, and turn signal use.

[1398] Specific examples

[1399] For example, while the vehicle is moving, GPS records its location every second, the brake sensor detects the number of sudden braking attempts, and the vehicle speed is acquired using the OBD-II interface.

[1400] Data Preprocessing

[1401] The device preprocesses the collected driving data and converts it into the required format. For preprocessing, Python scripts and the SciPy library are used to clean up the data by filtering noise. Key indicators such as mileage and frequency of sudden braking are then calculated and extracted.

[1402] Specific examples

[1403] Noise filtering uses a low-pass filter from the SciPy library to remove outliers (spikes) from the location data, after which distance traveled and frequency of hard braking are calculated.

[1404] Data transmission

[1405] The device sends the pre-processed data to the server over the Internet using an HTTP POST request using the "requests" library.

[1406] Specific examples

[1407] The preprocessed data is serialized in JSON format and sent periodically via HTTP POST request. For example, the sending interval is set to every 10 minutes.

[1408] Server Processing

[1409] Data reception

[1410] The server receives the driving data sent from the device and stores it in a database using an API endpoint using Flask, and the data is stored in a MySQL database.

[1411] Specific examples

[1412] The Flask API on the server receives the HTTP POST request, parses the data, and inserts it into a database table. For example, data from user "A" is stored in table "user_data."

[1413] Data analysis

[1414] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[1415] Specific examples

[1416] Features such as driving distance, frequency of sudden braking, frequency of sudden acceleration, and compliance with traffic rules are extracted, and an evaluation score is calculated using a random forest model. For example, if user "A" drives 50km per day and brakes suddenly five times, an analysis will be performed based on that data.

[1417] Insurance premium calculation

[1418] The server calculates the appropriate insurance premium based on the rating score, using a regression model for the premium.

[1419] Specific examples

[1420] For example, if the evaluation score is "80," the monthly insurance premium will be calculated based on that score, at 3,000 yen.

[1421] Insurance premium notification

[1422] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[1423] Specific examples

[1424] The calculated insurance premium is serialized in JSON format and sent to the terminal via an HTTP POST request. For example, the data "Insurance premium: 3000 yen / month" is sent.

[1425] User Action

[1426] Insurance premium display

[1427] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[1428] Specific examples

[1429] The terminal displays a notification message such as "Current insurance premium: 3,000 yen / month" on the LCD screen.

[1430] User Feedback

[1431] The user can review and improve his / her driving style based on the notified insurance premium.

[1432] Specific examples

[1433] For example, if you notice that you frequently slam on the brakes, you can consciously try to avoid doing so in order to improve your behavior.

[1434] Prompt Sentence Examples

[1435] "How can I design a system to calculate fair insurance premiums based on driving style data? Please include the following data: location, route, speed, frequency of sudden braking and acceleration, seat belt use, and turn signal use."

[1436] As described above, this system works in cooperation with the terminal and server to calculate and notify appropriate insurance premiums in real time, thereby raising awareness of safe driving.

[1437] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1438] Step 1: Data collection

[1439] Terminal handling

[1440] The terminal collects driving data from various sensors installed in the vehicle (e.g., GPS module, brake sensor, OBD-II interface). Specifically, the GPS module records location information every second, and the brake sensor counts the number of sudden braking events. Data collected through the OBD-II interface includes information such as driving speed and engine status.

[1441] Input and Output

[1442] Inputs: GPS module, brake sensor, raw data from OBD-II interface

[1443] Output: Collected driving data (location information, number of sudden braking, driving speed, etc.)

[1444] Specific examples

[1445] Whenever the vehicle is traveling, the GPS system records its location every second. For example, the current location is recorded as "Latitude: 35.6895, Longitude: 139.6917." In addition, the brake sensor counts each sudden braking as "1 sudden braking."

[1446] Step 2: Data Preprocessing

[1447] Terminal handling

[1448] The device preprocesses the collected driving data and converts it into an analyzable format. Specific preprocessing operations include noise filtering and data cleansing. Using the Python SciPy library, for example, outliers and spikes are removed. Furthermore, key indicators such as mileage and frequency of sudden braking are extracted.

[1449] Input and Output

[1450] Input: Collected raw driving data

[1451] Output: Clean data after preprocessing (filtered location information, number of hard braking incidents, mileage, etc.)

[1452] Specific examples

[1453] Noise filtering is performed on the location data to remove outliers. The distance traveled is also calculated from the location information, and the data is formatted as "Distance traveled: 50km".

[1454] Step 3: Send data

[1455] Terminal handling

[1456] The terminal sends the preprocessed data to the server via the Internet. Using the "requests" library, the data is sent by HTTP POST request.

[1457] Input and Output

[1458] Input: Preprocessed operating data

[1459] Output: Data sent to the server in JSON format

[1460] Specific examples

[1461] The preprocessed data is converted to JSON format and sent using an HTTP POST request. For example, data such as "Distance traveled: 50km, Number of hard braking attempts: 5" is sent every 10 minutes.

[1462] Step 4: Receiving Data

[1463] Server Processing

[1464] The server receives the driving data sent from the device and stores it in a database using a Flask-based API endpoint, and the data is stored in a MySQL database.

[1465] Input and Output

[1466] Input: Driving data sent from the terminal

[1467] Output: Operation data stored in the database

[1468] Specific examples

[1469] When the server's Flask API receives the HTTP POST request, it parses the data and inserts it into the database's "user_data" table. For example, it stores "User ID: A, Mileage: 50km, Number of Hard Brakes: 5".

[1470] Step 5: Data analysis

[1471] Server Processing

[1472] The server analyzes the driving data stored in the database using an analytical algorithm to calculate an evaluation score. The analysis uses Scikit-learn and machine learning algorithms such as the random forest model.

[1473] Input and Output

[1474] Input: Saved driving data

[1475] Output: Driver's rating score

[1476] Specific examples

[1477] The driving distance and frequency of sudden braking stored in the database are extracted as features and analyzed using a random forest model. For example, the result is calculated as "User ID: A, Evaluation score: 80."

[1478] Step 6: Premium calculation

[1479] Server Processing

[1480] The server calculates the appropriate insurance premium based on the rating score, using a regression model.

[1481] Input and Output

[1482] Input: Rating score

[1483] Output: Calculated insurance premium

[1484] Specific examples

[1485] If the evaluation score is 80, the monthly insurance premium will be calculated based on that score: 3,000 yen.

[1486] Step 7: Premium Notification

[1487] Server Processing

[1488] The server sends the calculated insurance premium to the terminal in real time, using an HTTP POST request.

[1489] Input and Output

[1490] Input: Calculated insurance premium

[1491] Output: Premium notification sent to the terminal

[1492] Specific examples

[1493] The calculated insurance premium is serialized in JSON format and sent to the terminal as "Insurance premium: 3,000 yen / month."

[1494] Step 8: View premiums

[1495] Terminal handling

[1496] The terminal receives the latest insurance premium notification sent from the server and visually displays it to the user using an LCD module.

[1497] Input and Output

[1498] Input: Premium notification sent from the server

[1499] Output: Insurance premium displayed on the device screen

[1500] Specific examples

[1501] The message "Current insurance premium: 3,000 yen / month" is displayed in real time on the terminal's LCD screen.

[1502] Step 9: User Feedback

[1503] User Action

[1504] The user can review and improve his / her driving style based on the notified insurance premium.

[1505] Input and Output

[1506] Input: Insurance premium displayed on the terminal

[1507] Power: Improved driving style

[1508] Specific examples

[1509] For example, the goal is to lower insurance premiums by driving more carefully to reduce sudden braking.

[1510] (Application example 1)

[1511] 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."

[1512] In recent years, systems that collect and analyze driving data to calculate appropriate automobile insurance premiums have become widespread. However, conventional systems often require manual operation by the driver, making it difficult to calculate insurance premiums that take into account data from autonomous driving. Furthermore, since evaluation scores and premium notifications are not calculated in real time, it is difficult for drivers to obtain useful feedback. Furthermore, conventional systems do not utilize generative AI models, which hinders the accuracy of data analysis and premium calculation.

[1513] 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.

[1514] In this invention, the server includes means for collecting and analyzing driving data while the vehicle is driving autonomously, means for generating prompt messages using the driving data so that the generative AI model can calculate an appropriate insurance premium, means for calculating the evaluation score based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type, and the notification means includes means for displaying the insurance premium on a terminal screen or a head-mounted display. This enables a comprehensive evaluation that includes driving data during autonomous driving, and makes it possible to provide the driver with accurate and appropriate insurance premium feedback in real time.

[1515] A "vehicle" is a mechanical device that can travel on land, is usually equipped with an engine or motor, and is a vehicle for transporting people or goods.

[1516] A "sensor" is a device that detects physical conditions or changes and outputs them as electrical signals.

[1517] "GPS" is an abbreviation for Global Positioning System, a system that uses artificial satellites to measure and display positions on Earth.

[1518] "Driving data" refers to information relating to the driving conditions of a vehicle, including, specifically, location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, whether seat belts are fastened, and whether turn signals are used.

[1519] "Preprocessing" refers to data processing operations to remove unnecessary noise from raw data and convert it into a format suitable for analysis.

[1520] An "analytic algorithm" is a computational procedure used to analyze collected data and find patterns and trends.

[1521] The "evaluation score" is a numerical representation of the driver's driving behavior based on the analysis results, and serves as the basis for calculating insurance premiums.

[1522] "Premium" means the amount payable by a driver for motor vehicle insurance, which is determined based on a risk assessment.

[1523] "Real-time" refers to processing and information provision occurring almost simultaneously with actual time.

[1524] "Autonomous driving" is a technology that allows vehicles to drive autonomously without human intervention, utilizing sensors and AI technology.

[1525] A "generative AI model" is a type of artificial intelligence designed to learn from large amounts of data and perform specific tasks.

[1526] A "prompt statement" is an input statement that instructs a generative AI model to perform a specific process, and explicitly states the conditions and requirements for analysis and calculation.

[1527] A "terminal" is an electronic device that a user directly operates to input and display information, including smartphones and head-mounted displays.

[1528] The insurance premium calculation system of this invention calculates appropriate insurance premiums for each driver by collecting driving data from multiple sensors and GPS installed in the vehicle. Implementing this system requires sensors, GPS, a smartphone, a head-mounted display (HMD), a server, and an AI model.

[1529] System Configuration

[1530] Terminal handling

[1531] Data collection

[1532] The device collects driving data from the vehicle's GPS and multiple sensors. Specifically, an application on the device records in real time location information, driving route, driving speed, frequency of sudden braking and sudden acceleration, seat belt use, turn signal use, and other information.

[1533] Data Preprocessing

[1534] The device preprocesses the collected driving data and converts it into the required format, which includes cleaning up the data by filtering out noise and extracting key indicators (e.g., frequency of hard braking).

[1535] Data transmission

[1536] The pre-processed data is then sent to a server via the internet. This communication is carried out periodically to ensure stability and security of the communication.

[1537] Server Processing

[1538] Data reception and storage

[1539] The server receives the driving data sent from the device and stores it in a database. For example, data sent by User A includes the distance traveled in a day and the number of times the device braked suddenly.

[1540] Data analysis

[1541] The server then uses an analysis algorithm to calculate an evaluation score based on the received data, including the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The resulting evaluation score is then used to calculate a driving skill score.

[1542] Insurance premium calculation and notification

[1543] The server calculates the appropriate insurance premium based on the analysis results. For example, based on User A's evaluation score, the premium is calculated to be 3,000 yen per month. The server then generates data to notify the user of the calculated premium in real time and sends it to the device. This notification is displayed on the smartphone or HMD.

[1544] Using generative AI models

[1545] Using driving data, the generative AI model generates prompts and calculates appropriate insurance premiums. For example, a prompt such as "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data" can be generated and sent to the AI ​​model, enabling highly accurate calculation of insurance premiums.

[1546] Specific examples

[1547] Example 1

[1548] For example, if it is detected that User A has a driving style that frequently involves sudden braking, that information is preprocessed in real time and sent to the server. The server analyzes the data and calculates the insurance premium based on a generative AI model that sets a higher premium for frequent sudden braking. As a result, "Current insurance premium: 3,500 yen / month" is displayed on the user's smartphone or HMD.

[1549] Example 2

[1550] Data during autonomous driving is also collected and analyzed in the same way. For example, if User B's vehicle is driving safely and efficiently in autonomous mode, that data is sent to the server, and the insurance premium is calculated lower due to safe driving. The prompt text based on the generative AI model is generated as follows: "User B recorded a mileage of 100 km in autonomous mode with no sudden braking. Please adjust the insurance premium accordingly." As a result, "Current insurance premium: 2,500 yen / month" is displayed.

[1551] As described above, this system enables real-time data collection and analysis, highly accurate insurance premium calculations, and is capable of providing users with timely and appropriate feedback.

[1552] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1553] Step 1:

[1554] The terminal collects driving data from the vehicle's GPS and multiple sensors. Specifically, it obtains current location information from the GPS and collects driving data such as the frequency of sudden braking and sudden acceleration, seat belt use, and turn signal use from the sensors. The input is raw data from the GPS and sensors. The output is a set of collected driving data.

[1555] Step 2:

[1556] The terminal preprocesses the collected driving data and converts it into the required format. Specifically, it performs noise filtering and data cleanup, and extracts key indicators such as the frequency of sudden braking and mileage. The input is the driving data collected in step 1. The output is a set of preprocessed driving data.

[1557] Step 3:

[1558] The terminal transmits the preprocessed data to the server via the Internet. This transmission occurs periodically to ensure communication stability and security. The input is the preprocessed data from step 2. The output is the data transmitted via the network.

[1559] Step 4:

[1560] The server receives the driving data sent from the terminal and stores it in a database. Specifically, it records the received data in a database for analysis. The input is the data sent in step 3. The output is the driving data stored in the database.

[1561] Step 5:

[1562] The server uses an analysis algorithm to calculate an evaluation score based on the received driving data. The score is calculated based on the driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, behavior during autonomous driving, and vehicle type. The input is driving data obtained from the database. The output is an evaluation score.

[1563] Step 6:

[1564] The server calculates an appropriate insurance premium based on the analysis results. At this time, it generates a prompt for the generative AI model and requests it to calculate the insurance premium. Specifically, based on data such as driving distance and frequency of sudden braking, it generates a prompt such as, "User A drove a total distance of 80 km on October 4, 2023, and braked suddenly 10 times. Please optimize the insurance premium based on this data." The input is the evaluation score and driving data. The output is the generated prompt and the calculated insurance premium.

[1565] Step 7:

[1566] The server generates data to notify the calculated insurance premium in real time and sends it to the terminal. This data is to be displayed on the user's smartphone or HMD. The input is the calculated insurance premium. The output is the insurance premium notification data sent to the terminal.

[1567] Step 8:

[1568] The device receives the insurance premium notification and visually displays it to the user. Specifically, it displays a notification such as "Current insurance premium: 3,000 yen / month" on the device screen or HMD. The input is the insurance premium notification data received from the server. The output is the insurance premium information displayed to the user.

[1569] 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.

[1570] The automobile insurance fairness calculation system according to the present invention is a system that collects driving data from multiple sensors and GPS mounted on vehicles, preprocesses and analyzes the data, and calculates and notifies the appropriate insurance premium for each driver. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score. Specific embodiments are described below.

[1571] Terminal handling

[1572] Data collection

[1573] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, the brake sensor detects the number of sudden braking attempts, the seat belt sensor checks whether the seat belt is fastened, and the turn signal sensor detects whether the turn signal is used.

[1574] Collecting Emotional Data

[1575] The device is also equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expressions, allowing it to detect the user's emotional state, such as stress or impatience, in real time while driving.

[1576] Data Preprocessing

[1577] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[1578] Data transmission

[1579] The device transmits the pre-processed driving and emotion data to a server via the Internet, either periodically or in real time, using security protocols (e.g., SSL / TLS) to ensure confidentiality and integrity of the data.

[1580] Server Processing

[1581] Data reception

[1582] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database.

[1583] Data analysis

[1584] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors used as evaluation criteria include driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state. The analysis algorithm quantifies each factor and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[1585] Insurance premium calculation

[1586] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[1587] Insurance premium notification

[1588] The server generates data to notify users of calculated insurance premiums in real time and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[1589] User Action

[1590] Receive insurance premium notifications

[1591] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[1592] User Feedback

[1593] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[1594] In this way, this system collects, pre-processes, and analyzes driving and emotional data, and calculates and notifies appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[1595] The processing flow will be explained below.

[1596] Step 1: Data collection

[1597] The device collects driving data from the vehicle's GPS and multiple sensors. For example, GPS records location information, driving route, and driving speed, while a brake sensor detects the number of sudden braking attempts. Furthermore, biometric sensors record heart rate and facial expressions in real time to collect the user's emotional state.

[1598] Step 2: Data Preprocessing

[1599] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. It filters out noise and removes unnecessary data. It also organizes the necessary information, timestamps the data, and classifies it by recording time. For example, it extracts information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data, and compiles it into a database.

[1600] Step 3: Send data

[1601] The device transmits the pre-processed data to a server over the Internet, either in real time or periodically, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[1602] Step 4: Receiving Data

[1603] The server receives the driving data and emotion data sent from the device and stores them in a temporary buffer. The received data is sequentially recorded in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, emotion data, etc. are registered in the database.

[1604] Step 5: Data analysis

[1605] The server uses an analysis algorithm to analyze the received driving data and emotional data. Factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and a high level of stress is detected while driving, an evaluation score is calculated based on that data.

[1606] Step 6: Premium calculation

[1607] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium will be set high.

[1608] Step 7: Premium Notification

[1609] The server generates data to notify the user of the calculated insurance premium in real time and sends it to the terminal. For example, it generates notification data such as "Current insurance premium: 3,000 yen / month" and converts it into a format for visual display.

[1610] Step 8: Receive premium notification

[1611] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[1612] Step 9: User Feedback

[1613] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to lower their insurance premiums by driving more calmly and in a relaxed state.

[1614] Example 2

[1615] 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."

[1616] Conventional automobile insurance calculation systems calculate insurance premiums based solely on the driver's driving data, without taking into account factors such as the driver's emotional state or stress level, limiting the ability to optimize insurance premiums. Furthermore, there is a lack of a system that reflects the driver's emotional data in real time, which creates the problem that the driver's psychological state is not reflected in the insurance premium. The present invention aims to solve these problems and realize more appropriate and fair insurance premium calculations.

[1617] 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.

[1618] In this invention, the server includes means for collecting driving data from multiple sensors and a location information system mounted on the vehicle, means for collecting biometric data such as the user's heart rate and facial expression to detect the emotional state while driving, and means for preprocessing the driving data and emotional data and transmitting the preprocessed data to a central processing unit via a network. This enables fair and appropriate insurance premium calculation based on detailed driving data including the driver's emotional state.

[1619] "Vehicle-mounted sensors" refers to multiple detection devices attached to a vehicle to collect various data while driving.

[1620] "Location information system" refers to a device that records location information such as a vehicle's current location, route, and speed.

[1621] "Driving data" refers to information related to the driving status of a vehicle, including, specifically, driving distance, driving speed, number of sudden braking attempts, whether seat belts are fastened, and whether turn signals are used.

[1622] "Preprocessing" refers to converting the collected raw data into an analyzable format and performing processes such as noise removal and adding timestamps.

[1623] "Central processing unit" refers to a processing unit such as a server that receives, analyzes, and stores data sent from a terminal.

[1624] "Analytics Algorithm" refers to the mathematical models and computational methods used to calculate the assessment score using the collected data.

[1625] The "evaluation score" is a numerical value calculated based on driving data and emotional data, and is an indicator that serves as the basis for calculating insurance premiums.

[1626] "Premium" refers to the amount a driver pays under an automobile insurance policy.

[1627] "Real-time" refers to data being collected, processed, and communicated immediately.

[1628] "Emotional state" refers to the psychological and physiological state of the driver while driving, and specifically includes emotions such as stress and impatience.

[1629] "Biometric data" refers to data that indicates the driver's physical state, such as the driver's heart rate or facial expression.

[1630] The automobile insurance fair amount calculation system of the present invention is a system that calculates and notifies the fair insurance premium for each driver by collecting driving data from multiple sensors and a location information system installed in the vehicle and preprocessing and analyzing the data. Furthermore, this system is characterized by incorporating an emotion engine that recognizes the user's emotions and reflects the emotion data in the evaluation score.

[1631] Terminal handling

[1632] Data collection

[1633] The device collects driving data from the vehicle's GPS and multiple sensors. For example, the GPS records location information, driving route, and driving speed, while the brake sensor detects the number of sudden braking events. The seatbelt sensor also checks whether the seatbelt is fastened, and the turn signal sensor detects whether the turn signal is being used. This data is temporarily stored in the device.

[1634] Collecting Emotional Data

[1635] The device is equipped with a biometric sensor that collects biometric data such as the user's heart rate and facial expression. For example, heart rate data is obtained from a smartwatch, and facial expression data is analyzed using an in-car camera. This allows the system to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data.

[1636] Data Preprocessing

[1637] The device preprocesses the collected driving and emotional data and converts it into an analyzable format. Specifically, it performs noise filtering and removes unnecessary data. It also categorizes the collected data by timestamp and compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database.

[1638] Data transmission

[1639] The device transmits the preprocessed driving and emotion data to a server via the Internet, using security protocols (e.g., SSL / TLS) to ensure data confidentiality and integrity.

[1640] Server Processing

[1641] Data reception

[1642] The server receives the data sent from the device and stores it in a temporary buffer. The received data is then stored in a database, where, for example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotion data are registered.

[1643] Data analysis

[1644] The server then uses an analysis algorithm to analyze the received driving data and emotional data. The analysis algorithm uses software such as TensorFlow to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state as evaluation criteria, and calculates an overall evaluation score. For example, if user A drives 50 km in one day, brakes suddenly five times during that time, and experiences a lot of stress while driving, the server will calculate an evaluation score based on that data.

[1645] Insurance premium calculation

[1646] The server calculates the appropriate insurance premium for each driver based on the analysis results. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and frequently experiences high stress, the insurance premium may be set high.

[1647] Insurance premium notification

[1648] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. The premium notification data for each user is converted into a format suitable for display on the screen and sent.

[1649] User Action

[1650] Receive insurance premium notifications

[1651] The terminal receives the insurance premium notification data sent from the server and displays it to the user. For example, a notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen.

[1652] User Feedback

[1653] Users can review their driving style and emotional management based on the insurance premiums they receive. For example, if a user sees the notification and decides that "my insurance premiums are high because I brake suddenly and am often under stress," they can make an effort to drive more calmly and in a more relaxed state in the future to lower their insurance premiums.

[1654] This will enable the system to collect, pre-process, and analyze driving and emotional data, and then calculate and notify appropriate insurance premiums, which is expected to more fairly reflect the driver's driving behavior and emotional state and promote safe driving.

[1655] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1656] Step 1: Data collection

[1657] The device collects driving data from the vehicle's GPS and multiple sensors. As input, it obtains location information, driving route, and driving speed from the location information system, and obtains the number of sudden braking attempts, seat belt usage status, and turn signal usage status from various sensors. This data is temporarily stored in the device. In concrete terms, the GPS module periodically updates location information, and the sensors collect data in real time.

[1658] Step 2: Collecting emotion data

[1659] The device uses biometric sensors to collect biometric data such as the user's heart rate and facial expressions. It receives input from the smartwatch and in-car camera. This allows the device to detect the user's emotional state, such as stress or impatience, while driving in real time and save the data. Specifically, the smartwatch updates the user's heart rate every second, and the camera analyzes facial expressions in real time.

[1660] Step 3: Data Preprocessing

[1661] The terminal preprocesses the collected driving and emotional data and converts it into an analyzable format. As input, it receives all data collected in the previous step. It performs noise filtering to remove unnecessary data and classifies the collected data by timestamp. As output, it compiles important information such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on biometric data into a database format. Specifically, a filtering algorithm is applied and the data is converted into an appropriate format.

[1662] Step 4: Send data

[1663] The terminal transmits the preprocessed driving data and emotion data to the server via the Internet. As input, it receives the database-formatted data generated in the previous step. During transmission, a security protocol (e.g., SSL / TLS) is used to ensure the confidentiality and integrity of the data. As output, transmission confirmation data is generated. Specifically, the data transmission module encrypts the data and transmits it to the server.

[1664] Step 5: Receiving Data

[1665] The server receives data sent from the terminal and stores it in a temporary buffer. As input, it receives data received via the network. As output, it prepares the data for storage in the database. In concrete terms, the data receiving module authenticates the data and stores it in a temporary buffer.

[1666] Step 6: Data analysis

[1667] The server analyzes the received driving data and emotion data using an analysis algorithm. As input, it receives all data stored in the buffer. It uses the analysis algorithm (e.g., a TensorFlow model) to quantify factors such as driving distance, frequency of sudden braking and sudden acceleration, compliance with traffic rules, vehicle type, and the user's emotional state, and calculates an evaluation score. As output, an individual evaluation score is generated. Specifically, the algorithm processes the data and calculates the evaluation score.

[1668] Step 7: Premium calculation

[1669] The server calculates the appropriate insurance premium for each driver based on the analysis results. As input, it receives the evaluation score generated in the previous step. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. As output, individual insurance premium data is generated. In specific operation, the evaluation score is substituted into the insurance premium calculation formula to calculate the optimal insurance premium.

[1670] Step 8: Premium Notification

[1671] The server generates data to notify the user of the calculated insurance premium and sends it to the terminal. Insurance premium data is received as input. Data converted into a notification format is generated as output. Specifically, the notification data is sent to the terminal and converted into a format that the user can view.

[1672] Step 9: Receive premium notification

[1673] The terminal receives the insurance premium notification data sent from the server and displays it to the user. As input, it receives the notification data sent from the server. As output, it displays it on the screen in a format that the user can check. Specifically, the terminal's screen display module converts the data into a display format and notifies the user.

[1674] Step 10: User feedback

[1675] The user can review their driving style and emotional management based on the notified insurance premium. As input, they check the insurance premium data displayed on the notification screen. As output, improved driving behavior and emotional management methods are implemented. As a specific action, the user changes their driving style or adopts new methods for stress management.

[1676] (Application example 2)

[1677] 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."

[1678] Conventional automobile insurance premium calculation systems do not fully take into account the driver's driving skills and biometric data, making it difficult to accurately calculate insurance premiums based on actual risk.In addition, there is a lack of a mechanism to notify drivers of the appropriateness of their insurance premiums in real time, which limits opportunities for drivers to review their driving style and emotional management.

[1679] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1680] In this invention, the server includes: means for collecting driving data from multiple sensors and a location information system mounted on the vehicle; means for preprocessing the driving data and biometric data and converting them into an analyzable data format; means for transmitting the preprocessed driving data and biometric data to the server via a network; means for storing the transmitted data in a database and calculating an evaluation score using an analysis algorithm; means for calculating insurance premiums for each driver based on the evaluation score; means for notifying the calculated insurance premiums in real time; and means for displaying the appropriate premium on the user's terminal, allowing the user to review their driving style and emotional state. This enables fair calculation of insurance premiums based on the driver's actual driving skills and emotional state, thereby promoting safe driving by drivers.

[1681] "Multiple sensors mounted on a vehicle" refers to various devices installed to measure and record the vehicle's operating status, including brake sensors, seat belt sensors, turn signal sensors, etc.

[1682] A "location information system" is a device that uses satellite navigation technology to measure a vehicle's location and collect data in real time.

[1683] "Driving data" refers to information collected while a vehicle is in operation, such as speed, location, and number of sudden braking attempts.

[1684] "Biometric data" refers to data that indicates the driver's physical and psychological state, such as heart rate and facial expressions.

[1685] "Preprocessing" refers to a series of data preparation procedures, such as data filtering and time stamping, to convert collected data into an analyzable format.

[1686] "Network" refers to a system that transmits and receives data using communication infrastructure such as the Internet.

[1687] The "server" is a computer system that receives driving data and biometric data, processes them using analytical algorithms, and stores them.

[1688] A "database" is a collection of information that systematically organizes and stores received data, making it easy to later analyze and search.

[1689] "Analysis algorithm" refers to the mathematical method or calculation program used to calculate an evaluation score based on collected driving data and biometric data.

[1690] The "evaluation score" is a numerical index calculated based on the analysis results of driving data and biometric data, and is a standard for evaluating the driver's driving skills and emotional state.

[1691] "Premium" means the monetary consideration paid by a driver under an automobile insurance policy.

[1692] "Real-time notification" refers to a means of immediately transmitting information to the driver based on the results of data analysis.

[1693] "User device" refers to a device that can receive and display notifications, such as a driver's smartphone or smart glasses.

[1694] A system for carrying out the present invention utilizes multiple sensors and a location information system mounted on a vehicle to collect and analyze driving data and biological data. A specific embodiment of the system is described below.

[1695] Hardware Configuration

[1696] 1. Vehicle sensors

[1697] GPS location system:

[1698] Record the vehicle's location, route, and speed.

[1699] Brake Sensor:

[1700] Detects the number of sudden braking events.

[1701] Seat belt sensor:

[1702] Check that the seat belt is fastened properly.

[1703] Turn Signal Sensor:

[1704] Detects turn signal usage.

[1705] Biometric Sensors:

[1706] It collects biometric data such as heart rate and facial expressions.

[1707] 2. User's Device

[1708] Smartphone:

[1709] Display and notify data.

[1710] Smart Glasses:

[1711] Display and notify data in real time.

[1712] Software Configuration

[1713] A program is installed on both the server and the terminal, and performs the following processes:

[1714] Terminal handling

[1715] 1. Data Collection

[1716] The device collects driving and biometric data in real time from sensors and location systems installed in the vehicle.

[1717] 2. Data Preprocessing

[1718] The terminal pre-processes the collected data, filtering out noise, time-stamping it and converting it into an organized format.

[1719] 3. Data Transmission

[1720] The pre-processed data is sent to a server over the Internet using security protocols (SSL / TLS).

[1721] Server Processing

[1722] 1. Data Reception

[1723] The server receives data sent from the terminal and stores it in a temporary buffer. The received data is then recorded in a database sequentially.

[1724] 2. Data Analysis

[1725] The server uses an analytical algorithm to analyze the driving data and biometric data, and calculates an overall evaluation score based on evaluation criteria such as driving distance, frequency of sudden braking, whether or not there was a speeding violation, and emotional state based on the biometric data.

[1726] 3. Insurance premium calculation

[1727] Based on the analysis results, the appropriate insurance premium is calculated for each driver. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user.

[1728] 4. Notification of insurance premiums

[1729] The server notifies the user of the calculated insurance premium in real time. The premium notification data for each user is converted into a format suitable for display on the screen and then sent.

[1730] User Action

[1731] 1. Receiving premium notices

[1732] The user's terminal receives the insurance premium notification data sent from the server and displays the insurance premium in real time.

[1733] 2. User Feedback

[1734] The user can review his / her driving style and emotional management based on the notified insurance premium.

[1735] Specific examples

[1736] For example, if an autonomous vehicle repeatedly brakes suddenly while driving on a highway, causing the driver's heart rate to rise, this data will be collected and analyzed and reflected in the insurance premium. A notification such as "Current insurance premium: 3,000 yen / month" will also be displayed on the user's smartphone.

[1737] Example prompts for the generative AI

[1738] "I would like to develop an application that collects driving data from autonomous vehicles and driver biometric data, analyzes it in real time, and calculates and notifies appropriate insurance premiums. Please tell me the types of sensors required, the data preprocessing method, and details of the insurance premium calculation algorithm."

[1739] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1740] Step 1:

[1741] The device collects driving data and biometric data in real time from the vehicle's onboard location information system and multiple sensors. Specifically, it obtains current coordinate data, driving route, and driving speed from the location information system, the number of sudden braking events from the brake sensor, and heart rate and facial expression data from the biometric sensor.

[1742] Step 2:

[1743] The device preprocesses the collected driving data and biometric data. Specifically, it performs noise filtering and removes unnecessary data. It also adds timestamps to organize and classify the data. This preprocessing converts the data into an analyzable format. For example, indicators such as driving distance, frequency of sudden braking, whether or not a speeding violation occurred, and emotional state based on biometric data can be extracted.

[1744] Step 3:

[1745] The terminal transmits the preprocessed driving data and biometric data to a server via the Internet. Security protocols (SSL / TLS) are used for transmission to ensure confidentiality and integrity of the data. The input at this stage is the preprocessed data, and the output is the data transmission to the server.

[1746] Step 4:

[1747] The server receives the data sent from the device and stores it in a temporary buffer. It then records it sequentially in a database. For example, User A's driving distance, number of sudden braking attempts, speeding violation information, and emotional data are registered in the database. The input of this step is the data sent from the device, and the output is the record in the database.

[1748] Step 5:

[1749] The server analyzes the data recorded in the database using an analytical algorithm. Specifically, factors such as driving distance, frequency of sudden braking, compliance with traffic rules, vehicle type, and the user's emotional state are used as evaluation criteria. The analytical algorithm quantifies each factor and calculates an overall evaluation score. The input of this step is the database data, and the output is the evaluation score.

[1750] Step 6:

[1751] The server calculates the insurance premium for each driver based on the evaluation score. The evaluation score is input into the insurance premium calculation algorithm to determine the insurance premium for each user. For example, if user A has a low overall evaluation score and has many high stress states, the insurance premium will be set high. The input of this step is the evaluation score, and the output is the calculated insurance premium.

[1752] Step 7:

[1753] The server notifies the terminal of the calculated insurance premium in real time. The notification data is converted into a format for screen display and sent. For example, a specific notification such as "Current insurance premium: 3,000 yen / month" is displayed on the terminal screen. The input of this step is the calculated insurance premium, and the output is the notification data.

[1754] Step 8:

[1755] The user's terminal receives the insurance premium notification data sent from the server and displays it to the user. This allows the user to review their driving style and emotional management. Based on the notified insurance premium, the user can make efforts to lower the insurance premium by driving safely. The input of this step is the notification data, and the output is the insurance premium displayed on the screen.

[1756] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1757] 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.

[1758] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1759] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1760] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1761] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1762] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1763] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1764] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1765] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1766] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1767] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be di...

Claims

1. A means for collecting driving data from a plurality of sensors and a GPS mounted on a vehicle; means for preprocessing the operational data and converting it into an analyzable data format; means for transmitting the preprocessed data to a server via a network; a means for storing the transmitted data in a database and calculating an evaluation score using an analysis algorithm; A means for calculating an insurance premium for each driver based on the evaluation score; means for notifying the calculated insurance premium in real time; A system including:

2. 2. The system according to claim 1, wherein the means for calculating the evaluation score calculates the evaluation score based on the driving distance, the frequency of sudden braking and sudden acceleration, compliance with traffic rules, and the vehicle type.

3. 2. The system according to claim 1, wherein said notification means displays the insurance premium on a screen of a terminal.

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