System

Abstract

A system is provided.SOLUTION: The system includes means for collecting driving data in real time, means for obtaining historical insurance data, means for analyzing the obtained driving data and historical insurance data, means for generating an optimal insurance plan based on the analysis result, means for notifying a user of the generated insurance plan, means for monitoring data of the user during driving in real time, means for evaluating risks during driving and providing safe driving advice, means for collecting and analyzing driving data and traffic information of an entire city, and means for providing data for city planning and traffic improvement.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 automobile insurance systems make it difficult for users to select the most suitable insurance plan, and premiums are often high. Furthermore, many systems do not fully utilize driving data and location information, resulting in insufficient risk assessment of individual drivers and promotion of safe driving. Furthermore, traffic data for the entire city is not sufficiently collected and analyzed, limiting its contribution to urban planning and traffic improvement. The purpose of this invention is to solve these problems and provide more useful information and services to users and the entire city. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for collecting driving data in real time, a means for acquiring past insurance history data, and a means for analyzing this data. The system also includes a means for generating and notifying the user of an optimal insurance plan based on the analysis results, and a means for monitoring the user's driving data and providing safe driving advice. The system also includes a means for collecting driving data and traffic information from an entire city and providing data for urban planning and traffic improvement. This makes it easier for users to select the optimal insurance plan, reduces insurance premiums, and improves driving safety. Furthermore, the results of analyzing the city's traffic data are useful for urban planning and improving transportation infrastructure.

[0006] "Driving data" refers to information collected while a vehicle is in motion (speed, distance traveled, frequency of sudden braking, accelerator operation, etc.).

[0007] "Insurance history data" refers to information about insurance plans that a user has previously subscribed to and accident history.

[0008] "Analysis" is the process of analyzing collected data using AI algorithms and other techniques to find specific patterns and trends.

[0009] An "insurance plan" is a type of insurance contract offered by an insurance company with different coverages and premiums.

[0010] "Location information" is data that indicates the current location of a particular device or vehicle using GPS or other technologies.

[0011] "Risk assessment" refers to assessing the likelihood of an accident occurring in a particular driving situation or area based on driving data, location information, etc.

[0012] "Safe driving advice" is guidance and warnings about things to be careful of while driving and recommended driving behavior.

[0013] "Urban planning" is the process of formulating long-term plans for the development and renovation of cities and regions, as well as specific measures based on those plans.

[0014] "Traffic improvement" refers to the implementation of measures to smooth traffic flow and improve safety.

[0015] "Traffic data" refers to various information related to traffic, such as road congestion, accident frequency, and vehicle flow. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] MODE FOR CARRYING OUT THE INVENTION

[0038] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[0039] System Overview

[0040] The system's main functions are driving data collection, data analysis, insurance plan generation and notification, real-time monitoring, safe driving advice provision, and city-wide traffic data collection and analysis. Each function is explained in detail below.

[0041] Data collection methods

[0042] Device:

[0043] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. The collected data is sent to a cloud server at regular intervals.

[0044] Data analysis format

[0045] server:

[0046] The server combines the received driving data with insurance history data and analyzes it using AI algorithms, thereby identifying the user's driving patterns (frequency of sudden braking, frequency of high-speed driving, etc.) and the risk of traffic accidents in each region.

[0047] Insurance plan generation and notification forms

[0048] server:

[0049] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history.

[0050] Device:

[0051] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[0052] Real-time monitoring and safe driving advice provision

[0053] Device:

[0054] While the user is driving, driving data is sent to a server in real time.

[0055] server:

[0056] The server analyzes the received driving data in real time and detects high-risk driving situations.

[0057] Device:

[0058] If the risk is deemed high, safe driving advice (for example, "This is an area where sudden braking is common. Please be careful") will be sent to the device, allowing the user to correct their driving on the spot.

[0059] City-wide transportation data collection and analysis

[0060] server:

[0061] Driving data and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[0062] Users (municipalities and transport administrations):

[0063] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[0064] Specific examples

[0065] Insurance plan proposals

[0066] User: The user driving the car has a smartphone inside the car.

[0067] Device: The smartphone collects the user's driving data and sends it to the server.

[0068] Server: Analyzes driving data and insurance history to generate the optimal insurance plan for the user.

[0069] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[0070] Providing safe driving advice

[0071] User: While the user is driving, their smartphone collects data on sudden braking.

[0072] Server: Analyzes data in real time and detects frequent occurrences of sudden braking in specific areas.

[0073] Device: The user is notified with the advice, "Please be careful as there are many sudden brakings in this area." The user can take the advice and drive safely.

[0074] With these functions, the present invention realizes a system that provides users with the most suitable insurance plan, promotes safe driving, and also contributes to improving traffic throughout the city.

[0075] The processing flow will be explained below.

[0076] Step 1:

[0077] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) in real time.

[0078] Step 2:

[0079] Terminal: Sends collected driving data to a cloud server at regular intervals.

[0080] Step 3:

[0081] Server: The cloud server stores the received driving data and integrates it with the user's past insurance history data.

[0082] Step 4:

[0083] Server: Using AI algorithms, it analyzes driving data and insurance history data to identify the user's driving patterns (such as frequency of sudden braking and high speed driving).

[0084] Step 5:

[0085] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[0086] Step 6:

[0087] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[0088] Step 7:

[0089] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[0090] Step 8:

[0091] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[0092] Step 9:

[0093] Device: Collects driving data in real time while the user is driving and transmits it to the server.

[0094] Step 10:

[0095] Server: Monitors real-time driving data and detects high-risk driving situations (e.g., areas with frequent sudden braking or weather-related risks).

[0096] Step 11:

[0097] Server: Generates safe driving advice based on the detected risks.

[0098] Step 12:

[0099] Device: Notify the user of the generated safe driving advice (e.g., "Be careful as there are many sudden brakings in this area").

[0100] Step 13:

[0101] Server: Driving data and traffic accident data collected from users are accumulated across the city and stored as large-scale data.

[0102] Step 14:

[0103] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[0104] Step 15:

[0105] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[0106] Step 16:

[0107] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[0108] Example 1

[0109] 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."

[0110] In modern urban environments, improving driving safety and providing optimal insurance plans are important challenges. Conventional systems do not adequately collect and analyze driving data, making it difficult to provide users with appropriate insurance plans. Furthermore, they are inadequate at predicting traffic accident risks and providing safe driving advice in real time, limiting their ability to improve traffic throughout the city.

[0111] 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.

[0112] In this invention, the server includes: means for collecting driving data in real time; means for acquiring past insurance history data; means for analyzing the acquired driving data and insurance history data; means for generating an optimal insurance plan based on the analysis results; means for notifying the user of the generated insurance plan; means for monitoring the user's driving data in real time; means for assessing driving risks and providing safe driving advice; means for collecting and analyzing driving data and traffic information for the entire city; means for providing data for urban planning and traffic improvement; means for identifying driving patterns using an AI algorithm; means for detecting high-risk driving situations; and means for identifying areas with high traffic accident rates based on the analysis results. This enables the server to provide optimal insurance plans to users and provide safe driving advice in real time. This also contributes to improving traffic conditions throughout the city and reducing the risk of traffic accidents.

[0113] "Driving data" refers to information related to driving a vehicle, such as vehicle speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[0114] "Insurance history data" refers to information about insurance contracts that a user has had in the past.

[0115] "Analyzing means" refers to methods or devices used to process collected data and identify patterns and risks.

[0116] "Insurance Plan" means the terms and conditions of automobile insurance offered to a Driver.

[0117] "Real-time monitoring means" refers to a method or device for real-time monitoring and analysis of vehicle data during operation.

[0118] "Safe driving advice" refers to instructions and advice provided to the driver based on the analysis results to improve driving safety.

[0119] "Traffic information" refers to information related to traffic, such as road conditions, traffic volume, and accident occurrence status.

[0120] An "AI algorithm" is a method for analyzing data using machine learning and artificial intelligence techniques to detect patterns and make predictions.

[0121] A "driving pattern" refers to a statistical feature of data that indicates a driver's driving tendencies and characteristics.

[0122] A "high-risk driving situation" is a driving condition or situation that is prone to causing traffic accidents or other problems.

[0123] A "high traffic accident area" is a location in a specific region or road section where traffic accidents occur frequently.

[0124] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[0125] Collecting and transmitting driving data

[0126] Device: When a user drives a car, their smartphone or in-car device collects real-time driving data such as speed, distance traveled, number of sudden braking attempts, accelerator operation, etc. This data is sent to a cloud server at regular intervals via the smartphone's communication function.

[0127] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration and immediately sends the data to the server.

[0128] Analysis of driving data and insurance history

[0129] Server: The server combines the received driving data with past insurance history data and analyzes it using an AI algorithm based on a generative AI model. This analysis identifies the user's driving patterns (such as frequency of sudden braking and high speed driving) and assesses the risk of traffic accidents in each area.

[0130] How it works: The server uses a deep learning model to classify the number of sudden braking and the frequency of high-speed driving as categorical targets. For example, the server clusters the user's sudden braking data and categorizes them into driving styles such as "safe," "cautious," and "dangerous."

[0131] Generate optimal insurance plans

[0132] Server: Based on the analysis results, the server generates the optimal insurance plan for the user. This plan takes into account the user's driving style and past insurance history.

[0133] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for a user who frequently brakes suddenly.

[0134] Insurance plan notification

[0135] Device: The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[0136] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, a message like "A new insurance plan has been proposed. Please review the details" will appear on the screen.

[0137] Real-time driving data monitoring

[0138] Device: While the user is driving, the smartphone continuously transmits driving data to the server.

[0139] How it works: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server. For example, if a user suddenly accelerates 100 meters in one minute, the data is sent to the server in real time.

[0140] Risk Advice

[0141] Server: The server analyzes driving data in real time and detects high-risk driving situations.

[0142] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in specific areas. For example, the server detects areas where sudden braking occurs frequently and flags them as "high risk."

[0143] Safe driving advice notifications

[0144] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[0145] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when a user enters an area where sudden braking is common, the user will be notified immediately.

[0146] Collecting and analyzing city-wide traffic data

[0147] Server: Centrally manages and analyzes driving data and accident information collected from each user, thereby identifying traffic congestion locations and areas prone to accidents.

[0148] How it works: The server uses a large database to analyze traffic data for the entire city, integrating it with map information. For example, it can detect that accidents frequently occur at a particular intersection and report that area to the local government.

[0149] Providing analysis results

[0150] Users (local governments and transportation administration agencies): The server's analysis results will be provided for urban planning and transportation infrastructure improvement, enabling local governments and transportation administration agencies to make effective improvements to transportation infrastructure.

[0151] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[0152] Example prompt: "Design a system that analyzes users' driving data in real time and provides advice on high-risk areas."

[0153] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0154] Step 1:

[0155] Input: The user starts driving the car.

[0156] Devices: Smartphones and in-car devices collect real-time driving data such as speed, distance traveled, number of hard brakes, and accelerator operation.

[0157] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration.

[0158] Output: Collected driving data.

[0159] Step 2:

[0160] Input: Driving data collected in step 1.

[0161] Terminal: Sends collected driving data to the cloud server at regular intervals.

[0162] Specific operation: Driving data collected by a smartphone is sent to a cloud server at regular intervals, for example, sending data every minute in batches.

[0163] Output: Driving data sent to cloud server.

[0164] Step 3:

[0165] Input: Driving data sent to the cloud server in step 2 and existing insurance history data.

[0166] Server: Integrates received driving data and past insurance history data and analyzes them using AI algorithms based on generative AI models.

[0167] How it works: The server uses a deep learning model to classify the number of sudden braking incidents and the frequency of high-speed driving as categorical targets. For example, it clusters data on sudden braking and categorizes driving styles as "safe," "cautious," or "dangerous."

[0168] Output: Driving patterns and traffic accident risk assessment as analysis results.

[0169] Step 4:

[0170] Input: Analysis results obtained in step 3.

[0171] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[0172] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for users who frequently brake suddenly.

[0173] Output: Optimal insurance plan.

[0174] Step 5:

[0175] Input: The insurance plan generated in step 4.

[0176] Terminal: The generated insurance plan is sent to the user's smartphone.

[0177] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, it might say, "A new insurance plan has been proposed. Please review the details."

[0178] Output: The user receives the insurance plan.

[0179] Step 6:

[0180] Input: Driving data collected continuously while the user is driving.

[0181] Terminal: Sends driving data to the server in real time.

[0182] Specific operation: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server, for example, every minute in real time.

[0183] Output: Real-time driving data.

[0184] Step 7:

[0185] Input: Real-time driving data sent in step 6.

[0186] Server: Analyzes driving data in real time and detects high-risk driving situations.

[0187] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in a specific area. For example, it detects locations where sudden braking frequently occurs.

[0188] Output: Risk notification data.

[0189] Step 8:

[0190] Input: Risk notification data obtained in step 7.

[0191] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[0192] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when entering an area where sudden braking is common, the user will be notified immediately.

[0193] Output: Real-time safe driving advice.

[0194] Step 9:

[0195] Input: Driving data and traffic information collected from each user.

[0196] Server: Centrally manages and analyzes driving data and traffic information for the entire city.

[0197] How it works: The server uses a large database to analyze traffic congestion and accident-prone areas by integrating them with map information. For example, it detects that accidents frequently occur at certain intersections.

[0198] Output: Analysis results for transportation infrastructure improvements.

[0199] Step 10:

[0200] Input: Analysis results obtained in step 9.

[0201] Users (local governments and transportation administration agencies): Provide analysis results for urban planning and transportation infrastructure improvement.

[0202] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[0203] Output: Proposed data for transportation improvements.

[0204] (Application example 1)

[0205] 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."

[0206] Conventional automobile insurance systems can only offer fixed insurance plans, making it difficult to propose optimal plans based on individual drivers' driving styles and driving data. They also lack real-time advice to promote safe driving, and there has been insufficient centralized management of driving data for the entire city to utilize for effective urban planning and traffic improvement. To address these issues, this invention provides a system that collects and analyzes driving data from autonomous vehicles in real time, proposes optimal insurance plans, and provides safe driving advice.

[0207] 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.

[0208] In this invention, the server includes a means for collecting driving data in real time, a means for acquiring past insurance history data, and a means for analyzing the acquired driving data and insurance history data. This makes it possible to analyze the collected driving data and insurance history data and provide optimal insurance plans for individual drivers. Furthermore, the monitoring means and evaluation means can evaluate driving risks in real time and provide safe driving advice, thereby promoting safe driving. Furthermore, by aggregating and analyzing driving data and traffic information for an entire city, it is possible to provide data for traffic improvement and urban planning.

[0209] "Driving data" refers to information related to the driving of an autonomous vehicle, such as its speed, distance traveled, accelerator operation, and braking operation.

[0210] "Insurance history data" refers to information about the contents of insurance contracts that a user has had up to now, as well as past accident and claim history.

[0211] "Analysis results" are information showing statistics and trends generated by analyzing collected driving data and insurance history data using AI and algorithms.

[0212] An "insurance plan" is a proposal of insurance products and contract terms optimized based on the user's driving data and insurance history data.

[0213] "Notifications" are means of informing users of generated insurance plans and safe driving advice, including push notifications to smartphones and in-car devices and emails.

[0214] "Monitoring" refers to the process of collecting and analyzing driving data in real time while the user is driving.

[0215] "Evaluation" is the process of determining driving risks based on collected driving data and providing safe driving advice.

[0216] "Risk" refers to accidents or dangerous driving situations that may occur while driving.

[0217] "Safe driving advice" refers to driving improvement suggestions and warnings provided to users based on real-time driving data.

[0218] An "autonomous vehicle" is a vehicle that operates autonomously using artificial intelligence and sensors.

[0219] "Urban planning" is a plan aimed at developing the entire city and improving transportation infrastructure.

[0220] "Traffic improvement" refers to the review of policies and infrastructure to smooth traffic flow and reduce accidents.

[0221] MODE FOR CARRYING OUT THE INVENTION

[0222] This invention is a system that proposes optimal insurance plans based on driving data from autonomous vehicles and promotes safe driving. This system includes functions such as collecting driving data, analyzing the data, generating and notifying insurance plans, real-time monitoring, and providing safe driving advice.

[0223] System configuration

[0224] 1. Data Collection

[0225] Using built-in sensors in autonomous vehicles and smartphones, driving data such as speed, distance traveled, accelerator operation, and brake operation is collected in real time, and this data is sent to a cloud server at regular intervals.

[0226] 2. Data Analysis

[0227] The server is built on the cloud and collects and analyzes the collected driving data and the user's past insurance history data. It processes the data using AI algorithms (e.g., TensorFlow and Scikit-learn) to identify driving patterns.

[0228] 3. Insurance plan generation and notification

[0229] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history. The generated insurance plan is then notified to the user via push notification or email on their smartphone or in-car display.

[0230] 4. Real-time monitoring and safe driving advice

[0231] The server analyzes driving data in real time to detect high-risk driving situations. If a high risk is detected, the user is notified in real time with advice such as "Please be careful as there are many sudden brakes in this area." This allows the user to correct their driving on the spot. The Geopy library is used to analyze location information and identify risk zones.

[0232] 5. City-wide traffic data analysis

[0233] Driving data and traffic information from across the city will be collected and integrated and analyzed on a cloud server. The analysis results will be provided to local governments and transportation administration agencies, who will be able to identify areas with high traffic congestion and accident rates and use them to improve these areas.

[0234] Specific examples

[0235] 1. Data collection and transmission:

[0236] Data such as the speed of the self-driving vehicle and the number of times it brakes suddenly is collected and sent to a cloud server.

[0237] 2. Data Analysis:

[0238] The collected data is analyzed using an AI model to identify driving patterns and risk areas.

[0239] 3. Insurance plan generation and notification:

[0240] The system generates and notifies the user of the optimal insurance plan based on their driving data and past insurance history.

[0241] 4. Providing safe driving advice:

[0242] It analyzes driving data in real time, and if a danger is detected, it immediately notifies the user with advice.

[0243] Prompt Sentence Examples

[0244] Driving data collection:

[0245] It collects user driving data (speed, number of sudden braking) and sends it to the server in the following format:

[0246] Speed: 60km / h

[0247] Hard braking: 3 times

[0248] Insurance plan suggestions:

[0249] It analyzes the user's driving data and insurance history and proposes insurance plans in the following format:

[0250] (User information)

[0251] Driving data: Speed: '60km / h', Hard braking: '3 times'

[0252] Insurance history: 'No accidents in the last three years'

[0253] (Proposal details)

[0254] Best Insurance Plan: 'Safe Driving Discount Plan', 'Discount Rate: 20%'

[0255] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0256] Step 1:

[0257] Data collection

[0258] The device (built-in sensors in the autonomous vehicle or a smartphone) collects driving data such as speed, distance traveled, accelerator operation, and braking operation in real time. This data is used to record the vehicle's movements in detail while driving. The collected data is temporarily stored in the device.

[0259] Input: Driving data from sensors and smartphones

[0260] Output: Driving data stored in the device

[0261] Step 2:

[0262] Data transmission

[0263] The device periodically transmits the collected driving data to a cloud server. Data transmission requires an internet connection and is encrypted using a secure communication protocol.

[0264] Input: Driving data stored in the device

[0265] Output: Driving data sent to the cloud server

[0266] Step 3:

[0267] Data analysis

[0268] The server combines the received driving data with the user's past insurance history data and analyzes it using AI algorithms (e.g., TensorFlow and Scikit-learn). This analysis identifies driving patterns (such as frequency of sudden braking and high speed driving) and traffic accident risks by region.

[0269] Input: Driving data and insurance history data sent to the cloud server

[0270] Output: Analysis results (driving patterns, traffic accident risk)

[0271] Step 4:

[0272] Generate an insurance plan

[0273] The server then generates the optimal insurance plan for the user based on the analysis results, taking into account the user's driving style and past insurance history. The generated insurance plan is saved in a dedicated format.

[0274] Input: Analysis results (driving patterns, traffic accident risk)

[0275] Output: Generated insurance plan

[0276] Step 5:

[0277] Insurance plan notification

[0278] The server then notifies the user of the generated insurance plan via a smartphone, in-car display, push notification, or email.

[0279] Input: Generated insurance plan

[0280] Output: The insurance plan that the user was notified of

[0281] Step 6:

[0282] Real-time monitoring

[0283] The device monitors driving data in real time and sends it to a server, allowing the driver to constantly monitor their driving situation.

[0284] Input: Data during driving

[0285] Output: Real-time data sent to the server

[0286] Step 7:

[0287] Risk assessment and advice provision

[0288] The server analyzes the driving data received in real time and detects high-risk driving situations. Appropriate safe driving advice is generated for the detected risks and sent to the device, which then notifies the user.

[0289] Input: Driving data received in real time

[0290] Output: Risk assessment results, safe driving advice

[0291] Step 8:

[0292] City-wide traffic data analysis

[0293] The server will integrate and analyze driving data and traffic information collected from across the city, and the analysis results will be provided to local governments and transportation administration agencies to help with urban planning and transportation infrastructure improvements.

[0294] Input: Driving data and traffic information collected from across the city

[0295] Output: Analysis results for urban planning and transportation improvement

[0296] 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.

[0297] MODE FOR CARRYING OUT THE INVENTION

[0298] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described.

[0299] System Overview

[0300] The system has the functions of collecting driving data, analyzing data, generating and notifying insurance plans, real-time monitoring, providing safe driving advice, recognizing user emotions using an emotion engine, and collecting and analyzing traffic data for the entire city. Each function is explained in detail below.

[0301] Data collection methods

[0302] Device:

[0303] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. In addition, cameras, microphones, and various sensors are used to analyze the user's facial expressions and voice, and emotional data is also collected. The collected data is sent to a cloud server at regular intervals.

[0304] Data analysis format

[0305] server:

[0306] The server integrates the received driving data, emotional data, and insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[0307] Insurance plan generation and notification forms

[0308] server:

[0309] Based on the analysis results, the system generates an optimal insurance plan for the user, which takes into account the user's driving style, past insurance history, and even emotional state.

[0310] Device:

[0311] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[0312] Real-time monitoring and safe driving advice provision

[0313] Device:

[0314] While the user is driving, driving data and emotional data are continuously collected in real time and sent to the server.

[0315] server:

[0316] The server analyzes the received driving and emotional data in real time to detect high-risk driving situations and the user's stress level.

[0317] Device:

[0318] If the risk is deemed high, the device will provide safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax"), allowing the user to correct their driving on the spot and regulate their emotional state.

[0319] City-wide transportation data collection and analysis

[0320] server:

[0321] Driving data, emotion data, and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[0322] Users (municipalities and transport administrations):

[0323] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[0324] Specific examples

[0325] Insurance plan proposals

[0326] User: The user driving the car has a smartphone inside the car.

[0327] Device: The smartphone collects the user's driving data and emotional data and sends it to the server.

[0328] Server: Analyzes driving data, emotional data, and insurance history, and generates the optimal insurance plan taking into account the user's stress level, etc.

[0329] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[0330] Providing safe driving advice

[0331] User: While the user is driving, the smartphone collects data on sudden braking, facial expressions, and changes in voice.

[0332] Server: Analyzes data in real time to detect frequent sudden braking in a specific area, and also detects when the user is feeling stressed.

[0333] Device: The device notifies the user, "Please be careful as there are many sudden brakings in this area," along with advice on both safe driving and emotional care, such as, "We recommend taking a deep breath and relaxing." By receiving this advice, the user can continue driving safely and also take care of their emotions.

[0334] With these functions, the present invention realizes a system that provides users with optimal insurance plans, promotes safe driving and emotional care, and also contributes to improving traffic throughout the city.

[0335] The processing flow will be explained below.

[0336] Processing steps of a system that combines emotion engines

[0337] Step 1:

[0338] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) and emotional data (facial expressions, tone of voice, etc.) in real time.

[0339] Step 2:

[0340] Terminal: Collected driving data and emotion data are sent to the cloud server at regular intervals.

[0341] Step 3:

[0342] Server: The cloud server stores the received driving data and emotion data and integrates it with the user's past insurance history data.

[0343] Step 4:

[0344] Server: Using AI algorithms, it analyzes driving data, emotional data, and insurance history data to identify the user's driving patterns (frequency of sudden braking, frequency of high speed driving, etc.) and emotional state.

[0345] Step 5:

[0346] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[0347] Step 6:

[0348] Server: Based on the analysis results, it generates the optimal insurance plan for the user, taking into account the user's emotional state.

[0349] Step 7:

[0350] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[0351] Step 8:

[0352] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[0353] Step 9:

[0354] Device: While the user is driving, it continues to collect driving data and emotional data in real time and transmits it to the server.

[0355] Step 10:

[0356] Server: Monitors real-time driving and emotional data to assess risky driving situations and the user's stress level.

[0357] Step 11:

[0358] Server: Based on the detected risks, it generates safe driving advice (e.g., "Please be careful as there are many sudden brakes in this area") and emotional care advice (e.g., "We recommend that you take a deep breath and relax").

[0359] Step 12:

[0360] On the device: The generated advice is notified to the user via a pop-up notification and / or a sound notification.

[0361] Step 13:

[0362] Server: Driving data, emotion data, and traffic accident data collected from users across the city are accumulated and stored as large-scale data.

[0363] Step 14:

[0364] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[0365] Step 15:

[0366] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[0367] Step 16:

[0368] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[0369] Example 2

[0370] 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."

[0371] Conventional automobile insurance systems propose insurance plans based on driving data and past insurance history, but because they do not take into account the user's emotional state, they have the problem of not being able to provide the optimal plan for each individual driver. They also lack real-time driving advice and emotional care, which means users' safe driving is not adequately supported. Furthermore, because traffic data for the entire city is not collected or analyzed, they are unable to provide useful information for urban planning and transportation infrastructure improvements.

[0372] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting driving data and emotion data in real time, a means for acquiring past insurance history data and emotion history data, and a means for integrating and analyzing the acquired driving data, emotion data, and insurance history data. This makes it possible to accurately grasp the user's driving patterns and emotional state and provide an optimal insurance plan and real-time safe driving advice and emotional care advice. It also becomes possible to analyze driving data, emotion data, and traffic accident information for an entire city and provide useful data for urban planning and transportation infrastructure improvement.

[0373] "Driving data" refers to information collected while the vehicle is traveling, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[0374] "Emotional data" is information that indicates the emotional state of a user, obtained from facial expressions, tone of voice, body movements, etc.

[0375] "Insurance history data" refers to data such as the user's past insurance contract details, accident history, and claim information.

[0376] "Emotion history data" is data that indicates the fluctuations in the user's past emotional state and the results of their analysis.

[0377] "Means for integrating and analyzing" refers to means for combining and analyzing various acquired data to identify the user's driving patterns, emotional state, and traffic risks.

[0378] "Driving patterns" refers to the analysis results of a user's driving style, specific behaviors, habits, etc.

[0379] The "optimal insurance plan" is the most appropriate insurance contract plan proposed to each individual user based on the user's driving data, emotional data, and insurance history data.

[0380] "Real-time monitoring means" refers to a method for continuously collecting data during operation and analyzing it on the spot to always obtain the latest information.

[0381] "Safe driving advice" means providing the user with specific instructions and suggestions for safe driving that are tailored to the driving conditions at the time.

[0382] "Emotional care advice" refers to providing specific instructions or suggestions for relaxation and stress reduction based on the user's emotional state.

[0383] "Data for urban planning and transportation infrastructure improvement" refers to information that is useful for developing plans for traffic management and infrastructure improvement, such as analyzing driving data, emotion data, and traffic accident information from across the city.

[0384] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described in detail.

[0385] System Overview

[0386] The system collects driving and emotional data, proposes insurance plans, and provides real-time advice on safe driving and emotional care. It also collects and analyzes traffic data from across the city to contribute to traffic improvements. Its main components include the user's smartphone or in-car device, a cloud server, a generative AI model, and an emotion engine.

[0387] Hardware and software used

[0388] Hardware:

[0389] Smartphones (e.g. iPhone, Android devices)

[0390] In-car devices (e.g. OBD-II adapter)

[0391] software:

[0392] Cloud server (e.g. AWS, Google Cloud)

[0393] AI algorithms (e.g. TensorFlow, PyTorch)

[0394] Emotion recognition software (e.g., Microsoft Azure Emotion API)

[0395] Data collection

[0396] Device:

[0397] As soon as the vehicle's engine starts, the user's smartphone or in-car device collects driving data such as speed, distance traveled, number of sudden braking attempts, and accelerator operation. The smartphone's built-in camera, microphone, and acceleration sensor are also used to analyze the user's facial expressions and voice, collecting emotional data. This data is then sent to a cloud server at regular intervals.

[0398] Example: A smartphone camera captures facial expressions and analyzes them with emotion recognition software, while a microphone detects the tone of the user's voice and passes the data to an emotion engine.

[0399] Data analysis

[0400] server:

[0401] The cloud server integrates driving data, emotional data, and past insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the area where they drive.

[0402] Example: The server uses an AI model to analyze driving and emotional data to identify a user's tendency to brake frequently and their high stress levels.

[0403] Insurance plan generation and notification

[0404] server:

[0405] The analysis results are used to generate a personalized insurance plan that takes into account your driving style, past insurance history, and emotional state.

[0406] Device:

[0407] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[0408] Examples:

[0409] Prompt: "The user's driving style involves frequent sudden braking, and their past insurance history has categorized them as a high-risk driver. Their emotional data has revealed a state of stress. Please recommend the most appropriate insurance plan."

[0410] Based on the results of the server's analysis, the optimal insurance plan will be notified to the smartphone.

[0411] Real-time monitoring and safe driving advice

[0412] Device:

[0413] Driving data and emotional data are continuously collected while driving and sent to the server.

[0414] server:

[0415] It analyzes data in real time to detect risky driving and the user's stress level.

[0416] Device:

[0417] If the risk is deemed high, the system will notify the user in real time of safe driving advice and emotional care advice.

[0418] Examples:

[0419] Prompt: "The user frequently brakes suddenly in a specific area, and emotional data indicates high stress. Please provide appropriate safe driving advice and emotional management advice."

[0420] The server identifies areas where sudden braking occurs frequently and sends a message to the driver's smartphone saying, "This area is prone to sudden braking, so please be careful." At the same time, it provides emotional care advice such as, "We recommend that you take a deep breath and relax."

[0421] Analysis and provision of city-wide traffic data

[0422] server:

[0423] By centrally managing and analyzing driving data, emotion data, and traffic accident information from across the city, we can identify areas where traffic congestion occurs and where accidents are frequent.

[0424] Users (municipalities and transport administrations):

[0425] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[0426] Example: A server analyzes city-wide data on a weekly basis to identify areas with high traffic congestion, and provides this data to a dedicated portal for the local government.

[0427] This allows the present invention to provide users with optimal insurance plans, promote safe driving and emotional care, and contribute to improving traffic throughout the city.

[0428] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0429] Step 1: Start collecting data

[0430] Device:

[0431] As soon as the vehicle engine starts, the smartphone or in-car device collects driving data (speed, distance traveled, number of hard braking attempts, accelerator operation, etc.) and emotional data (facial expressions, tone of voice, body movements, etc.) Once collected, this data is stored in a buffer at regular intervals.

[0432] input:

[0433] The vehicle engine starts and the terminal begins operation.

[0434] output:

[0435] The collected driving data and emotion data are stored in a buffer.

[0436] Specific behavior:

[0437] The smartphone camera captures the user's facial expressions, the microphone detects the tone of their voice, and sensors record driving data.

[0438] Step 2: Send data to the cloud server

[0439] Device:

[0440] The collected data is compressed at regular intervals and sent to a cloud server using HTTPS.

[0441] input:

[0442] Driving and emotional data collected.

[0443] output:

[0444] The compressed data is sent to a cloud server.

[0445] Specific behavior:

[0446] The device compresses the data and sends it to the cloud server using a secure communication protocol.

[0447] Step 3: Data synthesis and analysis

[0448] server:

[0449] The cloud server combines the received driving data, emotional data, and past insurance history data and analyzes them using AI algorithms (e.g., TensorFlow models). The analysis identifies the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[0450] input:

[0451] Driving data, emotional data, past insurance history data.

[0452] output:

[0453] Analysis results on users' driving patterns, emotional state, and traffic accident risk.

[0454] Specific behavior:

[0455] The server stores the data in a database and then runs an AI model to analyze various data.

[0456] Step 4: Generate your insurance plan

[0457] server:

[0458] Based on the analysis results, the system generates an optimal insurance plan that reflects the user's driving style, past insurance history, and emotional state.

[0459] input:

[0460] Analysis results, driving patterns, emotional state, and traffic accident risk data.

[0461] output:

[0462] The best insurance plan for you.

[0463] Specific behavior:

[0464] Based on the analysis results generated by the AI ​​model, the optimal insurance plan for the user is calculated and stored in a database.

[0465] Step 5: Insurance plan notification

[0466] Device:

[0467] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[0468] input:

[0469] The best insurance plan.

[0470] output:

[0471] A notification message will appear on the user's smartphone.

[0472] Specific behavior:

[0473] Insurance plan data is sent from the cloud server to the device via a notification API, and the UI component on the device displays the plan information.

[0474] Step 6: Real-time monitoring and safe driving advice

[0475] Device:

[0476] While driving, driving data and emotional data continue to be collected and sent to the server.

[0477] server:

[0478] The data transmitted in real time is analyzed to detect risky driving and the user's stress level.

[0479] Device:

[0480] If the risk is deemed high, the system will provide real-time safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax").

[0481] input:

[0482] Driving data, emotional data.

[0483] output:

[0484] Real-time driving advice, emotional care advice.

[0485] Specific behavior:

[0486] The server receives the data and simultaneously performs AI analysis, generating safe driving advice and emotional care advice based on the analysis results, which are then immediately sent to the device, which then displays a notification.

[0487] Step 7: Analyze and provide city-wide traffic data

[0488] server:

[0489] By centrally managing and analyzing driving data, emotion data, and accident information from across the city, traffic congestion locations and areas prone to accidents can be identified.

[0490] Users (municipalities and transport administrations):

[0491] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[0492] input:

[0493] Driving data, emotional data, traffic accident information.

[0494] output:

[0495] Analysis results on traffic congestion locations and areas with high accident rates.

[0496] Specific behavior:

[0497] The server analyzes the data weekly or monthly, automatically generating reports for urban planning and transportation infrastructure improvements, and provides them to local governments via a dedicated portal or by email.

[0498] Through these steps, the system can provide users with the most suitable insurance plans, promote safe driving and emotional care, and also contribute to improving traffic throughout the city.

[0499] (Application example 2)

[0500] 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."

[0501] Conventional automobile insurance systems generate insurance plans based solely on the user's driving data, but lack the ability to provide information on the user's emotional state or real-time safe driving advice. Furthermore, there is an insufficient mechanism for collecting traffic information and driving data from across a city and utilizing it for efficient urban planning and traffic improvement. Furthermore, there is no method for providing appropriate advice in real time when a user's emotional state fluctuates while driving.

[0502] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data in real time, means for acquiring past insurance history data, and means for analyzing the acquired driving data, insurance history data, and emotion data. This makes it possible to monitor the user's driving data and emotion data in real time, provide the most suitable insurance plan, and provide advice on safe driving and emotional care. In addition, traffic information for the entire city can be collected, enabling efficient urban planning and traffic improvements.

[0503] "Driving data" refers to various data related to vehicle driving, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[0504] "Emotional data" refers to data about the user's emotional state analyzed from facial expressions, voice, etc.

[0505] "Insurance history data" refers to information about insurance policies that a user has previously signed up for.

[0506] "Analysis" refers to the act of analyzing collected driving data and emotional data using AI algorithms and other methods to derive meaningful results.

[0507] An "insurance plan" refers to a plan that combines the contents and conditions of vehicle insurance.

[0508] "Notification" refers to the action of informing users of analysis results and insurance plans.

[0509] "Monitoring" refers to the act of monitoring a user's driving data and status in real time.

[0510] "Safe driving advice" refers to instructions or suggestions provided to the user while driving to promote safe driving.

[0511] "Emotional care advice" refers to advice to reduce stress and promote relaxation based on the user's emotional state.

[0512] "City-wide driving data" refers to various driving information collected within a specific city.

[0513] "Traffic information" refers to information related to road traffic, such as traffic volume, congestion, and accidents.

[0514] "Urban planning" refers to plans aimed at developing cities and improving transportation infrastructure.

[0515] "Transportation improvement" refers to policies and practices that aim to achieve more efficient and safer transportation conditions.

[0516] In this invention, the following specific embodiments are implemented as a system for providing insurance plans and safe driving advice while monitoring the emotional state of an autonomous vehicle and its driver.

[0517] System Overview

[0518] Device:

[0519] The smart glasses worn by the driver are equipped with a camera, microphone, and various sensors to collect driving data and user emotional data in real time, including facial expression and voice analysis.

[0520] server:

[0521] The server integrates the collected driving data, emotional data, and past insurance history data and analyzes them using AI algorithms. As a result of the analysis, it generates the optimal insurance plan and notifies the user. It also provides safe driving advice and emotional care advice in real time.

[0522] Hardware and software used:

[0523] Hardware:

[0524] Smart glasses (camera, microphone, various sensors)

[0525] Cloud server (data analysis and AI algorithm execution)

[0526] software:

[0527] EmotionAI (emotion recognition software)

[0528] DataCollector (driving data collection system)

[0529] Notification System

[0530] InsuranceEngine (insurance plan generation engine)

[0531] Specific operations of each method

[0532] Driving and emotion data collection:

[0533] Device: The smart glasses collect driving data such as vehicle speed, mileage, and number of sudden braking. At the same time, they use a camera and microphone to analyze facial expressions and voice to collect driver emotional data.

[0534] Real-time data transmission:

[0535] Terminal: Collected driving data and emotion data are sent to a cloud server at regular intervals.

[0536] Data Analysis and Notification:

[0537] Server: Analyzes the received driving data, emotion data, and insurance history data using AI algorithms to generate the optimal insurance plan.

[0538] Server: Evaluates risks and the user's stress state while driving, and generates advice on safe driving and emotional care in real time.

[0539] Terminal: The generated insurance plan and advice will be displayed on the smart glasses display.

[0540] Specific example explanation

[0541] 1. Insurance plan proposal

[0542] While the user is driving, the smart glasses collect driving data such as speed, distance traveled, and number of sudden braking attempts, and use a camera and microphone to analyze facial expressions and voice changes to collect emotional data.

[0543] The server consolidates and analyzes the data to generate the optimal insurance plan.

[0544] The generated insurance plan will be pushed to the smart glasses, allowing the user to check the plan on the spot.

[0545] 2. Providing safe driving advice

[0546] When a user suddenly brakes, the smart glasses detect driving data and analyze emotional data using a camera and microphone.

[0547] The server analyzes the data in real time and generates notifications such as "This is an area with a lot of sudden braking, please be careful" and "We recommend you take a deep breath and relax."

[0548] These notifications are sent to smart glasses, allowing users to continue driving safely and regulate their emotions.

[0549] Prompt Sentence Examples

[0550] Prompt sentence to input to the generative AI model:

[0551] "When a user suddenly brakes while driving, analyze their emotional and driving data and simultaneously provide advice on safe driving and emotional care."

[0552] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0553] Step 1:

[0554] The device uses smart glasses to collect driving and emotional data in real time. Driving data includes vehicle speed, mileage, and number of sudden braking incidents, while emotional data is the result of analyzing facial expressions and voice using a camera and microphone. Data is acquired as input from the vehicle's sensors and the user's emotional sensors, and is temporarily stored in internal memory. Formatted driving and emotional data is obtained as output.

[0555] Step 2:

[0556] The device transmits the collected driving data and emotion data to the cloud server at regular intervals. The driving data and emotion data collected in step 1 are used as input and are transmitted to the cloud server using the data communication module. As output, the device confirms that the data is transmitted to the cloud server and records it in a log appropriately.

[0557] Step 3:

[0558] The server analyzes the received driving and emotion data. It uses the data sent in step 2 as input and analyzes each data using EmotionAI and DataCollector. It identifies driving patterns and emotional states and assesses their respective risks. The results of this analysis are output and form the basis for generating insurance plans and real-time advice.

[0559] Step 4:

[0560] The server generates the optimal insurance plan based on the analysis results. The analysis results from step 3 and past insurance history data are used as input. InsuranceEngine uses this data to generate an insurance plan suitable for the user. The generated insurance plan is output and stored as a dataset to be notified to the user.

[0561] Step 5:

[0562] The server generates safe driving advice and emotional care advice based on the analysis results. It uses the analysis results from step 3 as input and generates advice based on the risk assessment results. The NotificationSystem creates these advice messages and saves them as messages to be sent to the user as output.

[0563] Step 6:

[0564] The device notifies the user of the generated insurance plan, safe driving advice, and emotional care advice. The data generated in steps 4 and 5 are used as input. Notifications are displayed on the smart glasses display and sent to the user using the vibration function. The output is that the user can check these notifications, which encourages them to select an appropriate insurance plan and drive safely.

[0565] 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.

[0566] 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.

[0567] 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.

[0568] [Second embodiment]

[0569] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0570] 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.

[0571] 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).

[0572] 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.

[0573] 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.

[0574] 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).

[0575] 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.

[0576] 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.

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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."

[0581] MODE FOR CARRYING OUT THE INVENTION

[0582] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[0583] System Overview

[0584] The system's main functions are driving data collection, data analysis, insurance plan generation and notification, real-time monitoring, safe driving advice provision, and city-wide traffic data collection and analysis. Each function is explained in detail below.

[0585] Data collection methods

[0586] Device:

[0587] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. The collected data is sent to a cloud server at regular intervals.

[0588] Data analysis format

[0589] server:

[0590] The server combines the received driving data with insurance history data and analyzes it using AI algorithms, thereby identifying the user's driving patterns (frequency of sudden braking, frequency of high-speed driving, etc.) and the risk of traffic accidents in each region.

[0591] Insurance plan generation and notification forms

[0592] server:

[0593] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history.

[0594] Device:

[0595] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[0596] Real-time monitoring and safe driving advice provision

[0597] Device:

[0598] While the user is driving, driving data is sent to a server in real time.

[0599] server:

[0600] The server analyzes the received driving data in real time and detects high-risk driving situations.

[0601] Device:

[0602] If the risk is deemed high, safe driving advice (for example, "This is an area where sudden braking is common. Please be careful") will be sent to the device, allowing the user to correct their driving on the spot.

[0603] City-wide transportation data collection and analysis

[0604] server:

[0605] Driving data and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[0606] Users (municipalities and transport administrations):

[0607] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[0608] Specific examples

[0609] Insurance plan proposals

[0610] User: The user driving the car has a smartphone inside the car.

[0611] Device: The smartphone collects the user's driving data and sends it to the server.

[0612] Server: Analyzes driving data and insurance history to generate the optimal insurance plan for the user.

[0613] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[0614] Providing safe driving advice

[0615] User: While the user is driving, their smartphone collects data on sudden braking.

[0616] Server: Analyzes data in real time and detects frequent occurrences of sudden braking in specific areas.

[0617] Device: The user is notified with the advice, "Please be careful as there are many sudden brakings in this area." The user can take the advice and drive safely.

[0618] With these functions, the present invention realizes a system that provides users with the most suitable insurance plan, promotes safe driving, and also contributes to improving traffic throughout the city.

[0619] The processing flow will be explained below.

[0620] Step 1:

[0621] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) in real time.

[0622] Step 2:

[0623] Terminal: Sends collected driving data to a cloud server at regular intervals.

[0624] Step 3:

[0625] Server: The cloud server stores the received driving data and integrates it with the user's past insurance history data.

[0626] Step 4:

[0627] Server: Using AI algorithms, it analyzes driving data and insurance history data to identify the user's driving patterns (such as frequency of sudden braking and high speed driving).

[0628] Step 5:

[0629] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[0630] Step 6:

[0631] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[0632] Step 7:

[0633] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[0634] Step 8:

[0635] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[0636] Step 9:

[0637] Device: Collects driving data in real time while the user is driving and transmits it to the server.

[0638] Step 10:

[0639] Server: Monitors real-time driving data and detects high-risk driving situations (e.g., areas with frequent sudden braking or weather-related risks).

[0640] Step 11:

[0641] Server: Generates safe driving advice based on the detected risks.

[0642] Step 12:

[0643] Device: Notify the user of the generated safe driving advice (e.g., "Be careful as there are many sudden brakings in this area").

[0644] Step 13:

[0645] Server: Driving data and traffic accident data collected from users are accumulated across the city and stored as large-scale data.

[0646] Step 14:

[0647] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[0648] Step 15:

[0649] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[0650] Step 16:

[0651] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[0652] Example 1

[0653] 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."

[0654] In modern urban environments, improving driving safety and providing optimal insurance plans are important challenges. Conventional systems do not adequately collect and analyze driving data, making it difficult to provide users with appropriate insurance plans. Furthermore, they are inadequate at predicting traffic accident risks and providing safe driving advice in real time, limiting their ability to improve traffic throughout the city.

[0655] 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.

[0656] In this invention, the server includes: means for collecting driving data in real time; means for acquiring past insurance history data; means for analyzing the acquired driving data and insurance history data; means for generating an optimal insurance plan based on the analysis results; means for notifying the user of the generated insurance plan; means for monitoring the user's driving data in real time; means for assessing driving risks and providing safe driving advice; means for collecting and analyzing driving data and traffic information for the entire city; means for providing data for urban planning and traffic improvement; means for identifying driving patterns using an AI algorithm; means for detecting high-risk driving situations; and means for identifying areas with high traffic accident rates based on the analysis results. This enables the server to provide optimal insurance plans to users and provide safe driving advice in real time. This also contributes to improving traffic conditions throughout the city and reducing the risk of traffic accidents.

[0657] "Driving data" refers to information related to driving a vehicle, such as vehicle speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[0658] "Insurance history data" refers to information about insurance contracts that a user has had in the past.

[0659] "Analyzing means" refers to methods or devices used to process collected data and identify patterns and risks.

[0660] "Insurance Plan" means the terms and conditions of automobile insurance offered to a Driver.

[0661] "Real-time monitoring means" refers to a method or device for real-time monitoring and analysis of vehicle data during operation.

[0662] "Safe driving advice" refers to instructions and advice provided to the driver based on the analysis results to improve driving safety.

[0663] "Traffic information" refers to information related to traffic, such as road conditions, traffic volume, and accident occurrence status.

[0664] An "AI algorithm" is a method for analyzing data using machine learning and artificial intelligence techniques to detect patterns and make predictions.

[0665] A "driving pattern" refers to a statistical feature of data that indicates a driver's driving tendencies and characteristics.

[0666] A "high-risk driving situation" is a driving condition or situation that is prone to causing traffic accidents or other problems.

[0667] A "high traffic accident area" is a location in a specific region or road section where traffic accidents occur frequently.

[0668] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[0669] Collecting and transmitting driving data

[0670] Device: When a user drives a car, their smartphone or in-car device collects real-time driving data such as speed, distance traveled, number of sudden braking attempts, accelerator operation, etc. This data is sent to a cloud server at regular intervals via the smartphone's communication function.

[0671] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration and immediately sends the data to the server.

[0672] Analysis of driving data and insurance history

[0673] Server: The server combines the received driving data with past insurance history data and analyzes it using an AI algorithm based on a generative AI model. This analysis identifies the user's driving patterns (such as frequency of sudden braking and high speed driving) and assesses the risk of traffic accidents in each area.

[0674] How it works: The server uses a deep learning model to classify the number of sudden braking and the frequency of high-speed driving as categorical targets. For example, the server clusters the user's sudden braking data and categorizes them into driving styles such as "safe," "cautious," and "dangerous."

[0675] Generate optimal insurance plans

[0676] Server: Based on the analysis results, the server generates the optimal insurance plan for the user. This plan takes into account the user's driving style and past insurance history.

[0677] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for a user who frequently brakes suddenly.

[0678] Insurance plan notification

[0679] Device: The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[0680] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, a message like "A new insurance plan has been proposed. Please review the details" will appear on the screen.

[0681] Real-time driving data monitoring

[0682] Device: While the user is driving, the smartphone continuously transmits driving data to the server.

[0683] How it works: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server. For example, if a user suddenly accelerates 100 meters in one minute, the data is sent to the server in real time.

[0684] Risk Advice

[0685] Server: The server analyzes driving data in real time and detects high-risk driving situations.

[0686] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in specific areas. For example, the server detects areas where sudden braking occurs frequently and flags them as "high risk."

[0687] Safe driving advice notifications

[0688] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[0689] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when a user enters an area where sudden braking is common, the user will be notified immediately.

[0690] Collecting and analyzing city-wide traffic data

[0691] Server: Centrally manages and analyzes driving data and accident information collected from each user, thereby identifying traffic congestion locations and areas prone to accidents.

[0692] How it works: The server uses a large database to analyze traffic data for the entire city, integrating it with map information. For example, it can detect that accidents frequently occur at a particular intersection and report that area to the local government.

[0693] Providing analysis results

[0694] Users (local governments and transportation administration agencies): The server's analysis results will be provided for urban planning and transportation infrastructure improvement, enabling local governments and transportation administration agencies to make effective improvements to transportation infrastructure.

[0695] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[0696] Example prompt: "Design a system that analyzes users' driving data in real time and provides advice on high-risk areas."

[0697] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0698] Step 1:

[0699] Input: The user starts driving the car.

[0700] Devices: Smartphones and in-car devices collect real-time driving data such as speed, distance traveled, number of hard brakes, and accelerator operation.

[0701] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration.

[0702] Output: Collected driving data.

[0703] Step 2:

[0704] Input: Driving data collected in step 1.

[0705] Terminal: Sends collected driving data to the cloud server at regular intervals.

[0706] Specific operation: Driving data collected by a smartphone is sent to a cloud server at regular intervals, for example, sending data every minute in batches.

[0707] Output: Driving data sent to cloud server.

[0708] Step 3:

[0709] Input: Driving data sent to the cloud server in step 2 and existing insurance history data.

[0710] Server: Integrates received driving data and past insurance history data and analyzes them using AI algorithms based on generative AI models.

[0711] How it works: The server uses a deep learning model to classify the number of sudden braking incidents and the frequency of high-speed driving as categorical targets. For example, it clusters data on sudden braking and categorizes driving styles as "safe," "cautious," or "dangerous."

[0712] Output: Driving patterns and traffic accident risk assessment as analysis results.

[0713] Step 4:

[0714] Input: Analysis results obtained in step 3.

[0715] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[0716] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for users who frequently brake suddenly.

[0717] Output: Optimal insurance plan.

[0718] Step 5:

[0719] Input: The insurance plan generated in step 4.

[0720] Terminal: The generated insurance plan is sent to the user's smartphone.

[0721] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, it might say, "A new insurance plan has been proposed. Please review the details."

[0722] Output: The user receives the insurance plan.

[0723] Step 6:

[0724] Input: Driving data collected continuously while the user is driving.

[0725] Terminal: Sends driving data to the server in real time.

[0726] Specific operation: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server, for example, every minute in real time.

[0727] Output: Real-time driving data.

[0728] Step 7:

[0729] Input: Real-time driving data sent in step 6.

[0730] Server: Analyzes driving data in real time and detects high-risk driving situations.

[0731] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in a specific area. For example, it detects locations where sudden braking frequently occurs.

[0732] Output: Risk notification data.

[0733] Step 8:

[0734] Input: Risk notification data obtained in step 7.

[0735] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[0736] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when entering an area where sudden braking is common, the user will be notified immediately.

[0737] Output: Real-time safe driving advice.

[0738] Step 9:

[0739] Input: Driving data and traffic information collected from each user.

[0740] Server: Centrally manages and analyzes driving data and traffic information for the entire city.

[0741] How it works: The server uses a large database to analyze traffic congestion and accident-prone areas by integrating them with map information. For example, it detects that accidents frequently occur at certain intersections.

[0742] Output: Analysis results for transportation infrastructure improvements.

[0743] Step 10:

[0744] Input: Analysis results obtained in step 9.

[0745] Users (local governments and transportation administration agencies): Provide analysis results for urban planning and transportation infrastructure improvement.

[0746] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[0747] Output: Proposed data for transportation improvements.

[0748] (Application example 1)

[0749] 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."

[0750] Conventional automobile insurance systems can only offer fixed insurance plans, making it difficult to propose optimal plans based on individual drivers' driving styles and driving data. They also lack real-time advice to promote safe driving, and there has been insufficient centralized management of driving data for the entire city to utilize for effective urban planning and traffic improvement. To address these issues, this invention provides a system that collects and analyzes driving data from autonomous vehicles in real time, proposes optimal insurance plans, and provides safe driving advice.

[0751] 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.

[0752] In this invention, the server includes a means for collecting driving data in real time, a means for acquiring past insurance history data, and a means for analyzing the acquired driving data and insurance history data. This makes it possible to analyze the collected driving data and insurance history data and provide optimal insurance plans for individual drivers. Furthermore, the monitoring means and evaluation means can evaluate driving risks in real time and provide safe driving advice, thereby promoting safe driving. Furthermore, by aggregating and analyzing driving data and traffic information for an entire city, it is possible to provide data for traffic improvement and urban planning.

[0753] "Driving data" refers to information related to the driving of an autonomous vehicle, such as its speed, distance traveled, accelerator operation, and braking operation.

[0754] "Insurance history data" refers to information about the contents of insurance contracts that a user has had up to now, as well as past accident and claim history.

[0755] "Analysis results" are information showing statistics and trends generated by analyzing collected driving data and insurance history data using AI and algorithms.

[0756] An "insurance plan" is a proposal of insurance products and contract terms optimized based on the user's driving data and insurance history data.

[0757] "Notifications" are means of informing users of generated insurance plans and safe driving advice, including push notifications to smartphones and in-car devices and emails.

[0758] "Monitoring" refers to the process of collecting and analyzing driving data in real time while the user is driving.

[0759] "Evaluation" is the process of determining driving risks based on collected driving data and providing safe driving advice.

[0760] "Risk" refers to accidents or dangerous driving situations that may occur while driving.

[0761] "Safe driving advice" refers to driving improvement suggestions and warnings provided to users based on real-time driving data.

[0762] An "autonomous vehicle" is a vehicle that operates autonomously using artificial intelligence and sensors.

[0763] "Urban planning" is a plan aimed at developing the entire city and improving transportation infrastructure.

[0764] "Traffic improvement" refers to the review of policies and infrastructure to smooth traffic flow and reduce accidents.

[0765] MODE FOR CARRYING OUT THE INVENTION

[0766] This invention is a system that proposes optimal insurance plans based on driving data from autonomous vehicles and promotes safe driving. This system includes functions such as collecting driving data, analyzing the data, generating and notifying insurance plans, real-time monitoring, and providing safe driving advice.

[0767] System configuration

[0768] 1. Data Collection

[0769] Using built-in sensors in autonomous vehicles and smartphones, driving data such as speed, distance traveled, accelerator operation, and brake operation is collected in real time, and this data is sent to a cloud server at regular intervals.

[0770] 2. Data Analysis

[0771] The server is built on the cloud and collects and analyzes the collected driving data and the user's past insurance history data. It processes the data using AI algorithms (e.g., TensorFlow and Scikit-learn) to identify driving patterns.

[0772] 3. Insurance plan generation and notification

[0773] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history. The generated insurance plan is then notified to the user via push notification or email on their smartphone or in-car display.

[0774] 4. Real-time monitoring and safe driving advice

[0775] The server analyzes driving data in real time to detect high-risk driving situations. If a high risk is detected, the user is notified in real time with advice such as "Please be careful as there are many sudden brakes in this area." This allows the user to correct their driving on the spot. The Geopy library is used to analyze location information and identify risk zones.

[0776] 5. City-wide traffic data analysis

[0777] Driving data and traffic information from across the city will be collected and integrated and analyzed on a cloud server. The analysis results will be provided to local governments and transportation administration agencies, who will be able to identify areas with high traffic congestion and accident rates and use them to improve these areas.

[0778] Specific examples

[0779] 1. Data collection and transmission:

[0780] Data such as the speed of the self-driving vehicle and the number of times it brakes suddenly is collected and sent to a cloud server.

[0781] 2. Data Analysis:

[0782] The collected data is analyzed using an AI model to identify driving patterns and risk areas.

[0783] 3. Insurance plan generation and notification:

[0784] The system generates and notifies the user of the optimal insurance plan based on their driving data and past insurance history.

[0785] 4. Providing safe driving advice:

[0786] It analyzes driving data in real time, and if a danger is detected, it immediately notifies the user with advice.

[0787] Prompt Sentence Examples

[0788] Driving data collection:

[0789] It collects user driving data (speed, number of sudden braking) and sends it to the server in the following format:

[0790] Speed: 60km / h

[0791] Hard braking: 3 times

[0792] Insurance plan suggestions:

[0793] It analyzes the user's driving data and insurance history and proposes insurance plans in the following format:

[0794] (User information)

[0795] Driving data: Speed: '60km / h', Hard braking: '3 times'

[0796] Insurance history: 'No accidents in the last three years'

[0797] (Proposal details)

[0798] Best Insurance Plan: 'Safe Driving Discount Plan', 'Discount Rate: 20%'

[0799] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0800] Step 1:

[0801] Data collection

[0802] The device (built-in sensors in the autonomous vehicle or a smartphone) collects driving data such as speed, distance traveled, accelerator operation, and braking operation in real time. This data is used to record the vehicle's movements in detail while driving. The collected data is temporarily stored in the device.

[0803] Input: Driving data from sensors and smartphones

[0804] Output: Driving data stored in the device

[0805] Step 2:

[0806] Data transmission

[0807] The device periodically transmits the collected driving data to a cloud server. Data transmission requires an internet connection and is encrypted using a secure communication protocol.

[0808] Input: Driving data stored in the device

[0809] Output: Driving data sent to the cloud server

[0810] Step 3:

[0811] Data analysis

[0812] The server combines the received driving data with the user's past insurance history data and analyzes it using AI algorithms (e.g., TensorFlow and Scikit-learn). This analysis identifies driving patterns (such as frequency of sudden braking and high speed driving) and traffic accident risks by region.

[0813] Input: Driving data and insurance history data sent to the cloud server

[0814] Output: Analysis results (driving patterns, traffic accident risk)

[0815] Step 4:

[0816] Generate an insurance plan

[0817] The server then generates the optimal insurance plan for the user based on the analysis results, taking into account the user's driving style and past insurance history. The generated insurance plan is saved in a dedicated format.

[0818] Input: Analysis results (driving patterns, traffic accident risk)

[0819] Output: Generated insurance plan

[0820] Step 5:

[0821] Insurance plan notification

[0822] The server then notifies the user of the generated insurance plan via a smartphone, in-car display, push notification, or email.

[0823] Input: Generated insurance plan

[0824] Output: The insurance plan that the user was notified of

[0825] Step 6:

[0826] Real-time monitoring

[0827] The device monitors driving data in real time and sends it to a server, allowing the driver to constantly monitor their driving situation.

[0828] Input: Data during driving

[0829] Output: Real-time data sent to the server

[0830] Step 7:

[0831] Risk assessment and advice provision

[0832] The server analyzes the driving data received in real time and detects high-risk driving situations. Appropriate safe driving advice is generated for the detected risks and sent to the device, which then notifies the user.

[0833] Input: Driving data received in real time

[0834] Output: Risk assessment results, safe driving advice

[0835] Step 8:

[0836] City-wide traffic data analysis

[0837] The server will integrate and analyze driving data and traffic information collected from across the city, and the analysis results will be provided to local governments and transportation administration agencies to help with urban planning and transportation infrastructure improvements.

[0838] Input: Driving data and traffic information collected from across the city

[0839] Output: Analysis results for urban planning and transportation improvement

[0840] 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.

[0841] MODE FOR CARRYING OUT THE INVENTION

[0842] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described.

[0843] System Overview

[0844] The system has the functions of collecting driving data, analyzing data, generating and notifying insurance plans, real-time monitoring, providing safe driving advice, recognizing user emotions using an emotion engine, and collecting and analyzing traffic data for the entire city. Each function is explained in detail below.

[0845] Data collection methods

[0846] Device:

[0847] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. In addition, cameras, microphones, and various sensors are used to analyze the user's facial expressions and voice, and emotional data is also collected. The collected data is sent to a cloud server at regular intervals.

[0848] Data analysis format

[0849] server:

[0850] The server integrates the received driving data, emotional data, and insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[0851] Insurance plan generation and notification forms

[0852] server:

[0853] Based on the analysis results, the system generates an optimal insurance plan for the user, which takes into account the user's driving style, past insurance history, and even emotional state.

[0854] Device:

[0855] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[0856] Real-time monitoring and safe driving advice provision

[0857] Device:

[0858] While the user is driving, driving data and emotional data are continuously collected in real time and sent to the server.

[0859] server:

[0860] The server analyzes the received driving and emotional data in real time to detect high-risk driving situations and the user's stress level.

[0861] Device:

[0862] If the risk is deemed high, the device will provide safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax"), allowing the user to correct their driving on the spot and regulate their emotional state.

[0863] City-wide transportation data collection and analysis

[0864] server:

[0865] Driving data, emotion data, and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[0866] Users (municipalities and transport administrations):

[0867] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[0868] Specific examples

[0869] Insurance plan proposals

[0870] User: The user driving the car has a smartphone inside the car.

[0871] Device: The smartphone collects the user's driving data and emotional data and sends it to the server.

[0872] Server: Analyzes driving data, emotional data, and insurance history, and generates the optimal insurance plan taking into account the user's stress level, etc.

[0873] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[0874] Providing safe driving advice

[0875] User: While the user is driving, the smartphone collects data on sudden braking, facial expressions, and changes in voice.

[0876] Server: Analyzes data in real time to detect frequent sudden braking in a specific area, and also detects when the user is feeling stressed.

[0877] Device: The device notifies the user, "Please be careful as there are many sudden brakings in this area," along with advice on both safe driving and emotional care, such as, "We recommend taking a deep breath and relaxing." By receiving this advice, the user can continue driving safely and also take care of their emotions.

[0878] With these functions, the present invention realizes a system that provides users with optimal insurance plans, promotes safe driving and emotional care, and also contributes to improving traffic throughout the city.

[0879] The processing flow will be explained below.

[0880] Processing steps of a system that combines emotion engines

[0881] Step 1:

[0882] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) and emotional data (facial expressions, tone of voice, etc.) in real time.

[0883] Step 2:

[0884] Terminal: Collected driving data and emotion data are sent to the cloud server at regular intervals.

[0885] Step 3:

[0886] Server: The cloud server stores the received driving data and emotion data and integrates it with the user's past insurance history data.

[0887] Step 4:

[0888] Server: Using AI algorithms, it analyzes driving data, emotional data, and insurance history data to identify the user's driving patterns (frequency of sudden braking, frequency of high speed driving, etc.) and emotional state.

[0889] Step 5:

[0890] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[0891] Step 6:

[0892] Server: Based on the analysis results, it generates the optimal insurance plan for the user, taking into account the user's emotional state.

[0893] Step 7:

[0894] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[0895] Step 8:

[0896] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[0897] Step 9:

[0898] Device: While the user is driving, it continues to collect driving data and emotional data in real time and transmits it to the server.

[0899] Step 10:

[0900] Server: Monitors real-time driving and emotional data to assess risky driving situations and the user's stress level.

[0901] Step 11:

[0902] Server: Based on the detected risks, it generates safe driving advice (e.g., "Please be careful as there are many sudden brakes in this area") and emotional care advice (e.g., "We recommend that you take a deep breath and relax").

[0903] Step 12:

[0904] On the device: The generated advice is notified to the user via a pop-up notification and / or a sound notification.

[0905] Step 13:

[0906] Server: Driving data, emotion data, and traffic accident data collected from users across the city are accumulated and stored as large-scale data.

[0907] Step 14:

[0908] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[0909] Step 15:

[0910] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[0911] Step 16:

[0912] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[0913] Example 2

[0914] 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."

[0915] Conventional automobile insurance systems propose insurance plans based on driving data and past insurance history, but because they do not take into account the user's emotional state, they have the problem of not being able to provide the optimal plan for each individual driver. They also lack real-time driving advice and emotional care, which means users' safe driving is not adequately supported. Furthermore, because traffic data for the entire city is not collected or analyzed, they are unable to provide useful information for urban planning and transportation infrastructure improvements.

[0916] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting driving data and emotion data in real time, a means for acquiring past insurance history data and emotion history data, and a means for integrating and analyzing the acquired driving data, emotion data, and insurance history data. This makes it possible to accurately grasp the user's driving patterns and emotional state and provide an optimal insurance plan and real-time safe driving advice and emotional care advice. It also becomes possible to analyze driving data, emotion data, and traffic accident information for an entire city and provide useful data for urban planning and transportation infrastructure improvement.

[0917] "Driving data" refers to information collected while the vehicle is traveling, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[0918] "Emotional data" is information that indicates the emotional state of a user, obtained from facial expressions, tone of voice, body movements, etc.

[0919] "Insurance history data" refers to data such as the user's past insurance contract details, accident history, and claim information.

[0920] "Emotion history data" is data that indicates the fluctuations in the user's past emotional state and the results of their analysis.

[0921] "Means for integrating and analyzing" refers to means for combining and analyzing various acquired data to identify the user's driving patterns, emotional state, and traffic risks.

[0922] "Driving patterns" refers to the analysis results of a user's driving style, specific behaviors, habits, etc.

[0923] The "optimal insurance plan" is the most appropriate insurance contract plan proposed to each individual user based on the user's driving data, emotional data, and insurance history data.

[0924] "Real-time monitoring means" refers to a method for continuously collecting data during operation and analyzing it on the spot to always obtain the latest information.

[0925] "Safe driving advice" means providing the user with specific instructions and suggestions for safe driving that are tailored to the driving conditions at the time.

[0926] "Emotional care advice" refers to providing specific instructions or suggestions for relaxation and stress reduction based on the user's emotional state.

[0927] "Data for urban planning and transportation infrastructure improvement" refers to information that is useful for developing plans for traffic management and infrastructure improvement, such as analyzing driving data, emotion data, and traffic accident information from across the city.

[0928] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described in detail.

[0929] System Overview

[0930] The system collects driving and emotional data, proposes insurance plans, and provides real-time advice on safe driving and emotional care. It also collects and analyzes traffic data from across the city to contribute to traffic improvements. Its main components include the user's smartphone or in-car device, a cloud server, a generative AI model, and an emotion engine.

[0931] Hardware and software used

[0932] Hardware:

[0933] Smartphones (e.g. iPhone, Android devices)

[0934] In-car devices (e.g. OBD-II adapter)

[0935] software:

[0936] Cloud server (e.g. AWS, Google Cloud)

[0937] AI algorithms (e.g. TensorFlow, PyTorch)

[0938] Emotion recognition software (e.g., Microsoft Azure Emotion API)

[0939] Data collection

[0940] Device:

[0941] As soon as the vehicle's engine starts, the user's smartphone or in-car device collects driving data such as speed, distance traveled, number of sudden braking attempts, and accelerator operation. The smartphone's built-in camera, microphone, and acceleration sensor are also used to analyze the user's facial expressions and voice, collecting emotional data. This data is then sent to a cloud server at regular intervals.

[0942] Example: A smartphone camera captures facial expressions and analyzes them with emotion recognition software, while a microphone detects the tone of the user's voice and passes the data to an emotion engine.

[0943] Data analysis

[0944] server:

[0945] The cloud server integrates driving data, emotional data, and past insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the area where they drive.

[0946] Example: The server uses an AI model to analyze driving and emotional data to identify a user's tendency to brake frequently and their high stress levels.

[0947] Insurance plan generation and notification

[0948] server:

[0949] The analysis results are used to generate a personalized insurance plan that takes into account your driving style, past insurance history, and emotional state.

[0950] Device:

[0951] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[0952] Examples:

[0953] Prompt: "The user's driving style involves frequent sudden braking, and their past insurance history has categorized them as a high-risk driver. Their emotional data has revealed a state of stress. Please recommend the most appropriate insurance plan."

[0954] Based on the results of the server's analysis, the optimal insurance plan will be notified to the smartphone.

[0955] Real-time monitoring and safe driving advice

[0956] Device:

[0957] Driving data and emotional data are continuously collected while driving and sent to the server.

[0958] server:

[0959] It analyzes data in real time to detect risky driving and the user's stress level.

[0960] Device:

[0961] If the risk is deemed high, the system will notify the user in real time of safe driving advice and emotional care advice.

[0962] Examples:

[0963] Prompt: "The user frequently brakes suddenly in a specific area, and emotional data indicates high stress. Please provide appropriate safe driving advice and emotional management advice."

[0964] The server identifies areas where sudden braking occurs frequently and sends a message to the driver's smartphone saying, "This area is prone to sudden braking, so please be careful." At the same time, it provides emotional care advice such as, "We recommend that you take a deep breath and relax."

[0965] Analysis and provision of city-wide traffic data

[0966] server:

[0967] By centrally managing and analyzing driving data, emotion data, and traffic accident information from across the city, we can identify areas where traffic congestion occurs and where accidents are frequent.

[0968] Users (municipalities and transport administrations):

[0969] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[0970] Example: A server analyzes city-wide data on a weekly basis to identify areas with high traffic congestion, and provides this data to a dedicated portal for the local government.

[0971] This allows the present invention to provide users with optimal insurance plans, promote safe driving and emotional care, and contribute to improving traffic throughout the city.

[0972] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0973] Step 1: Start collecting data

[0974] Device:

[0975] As soon as the vehicle engine starts, the smartphone or in-car device collects driving data (speed, distance traveled, number of hard braking attempts, accelerator operation, etc.) and emotional data (facial expressions, tone of voice, body movements, etc.) Once collected, this data is stored in a buffer at regular intervals.

[0976] input:

[0977] The vehicle engine starts and the terminal begins operation.

[0978] output:

[0979] The collected driving data and emotion data are stored in a buffer.

[0980] Specific behavior:

[0981] The smartphone camera captures the user's facial expressions, the microphone detects the tone of their voice, and sensors record driving data.

[0982] Step 2: Send data to the cloud server

[0983] Device:

[0984] The collected data is compressed at regular intervals and sent to a cloud server using HTTPS.

[0985] input:

[0986] Driving and emotional data collected.

[0987] output:

[0988] The compressed data is sent to a cloud server.

[0989] Specific behavior:

[0990] The device compresses the data and sends it to the cloud server using a secure communication protocol.

[0991] Step 3: Data synthesis and analysis

[0992] server:

[0993] The cloud server combines the received driving data, emotional data, and past insurance history data and analyzes them using AI algorithms (e.g., TensorFlow models). The analysis identifies the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[0994] input:

[0995] Driving data, emotional data, past insurance history data.

[0996] output:

[0997] Analysis results on users' driving patterns, emotional state, and traffic accident risk.

[0998] Specific behavior:

[0999] The server stores the data in a database and then runs an AI model to analyze various data.

[1000] Step 4: Generate your insurance plan

[1001] server:

[1002] Based on the analysis results, the system generates an optimal insurance plan that reflects the user's driving style, past insurance history, and emotional state.

[1003] input:

[1004] Analysis results, driving patterns, emotional state, and traffic accident risk data.

[1005] output:

[1006] The best insurance plan for you.

[1007] Specific behavior:

[1008] Based on the analysis results generated by the AI ​​model, the optimal insurance plan for the user is calculated and stored in a database.

[1009] Step 5: Insurance plan notification

[1010] Device:

[1011] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[1012] input:

[1013] The best insurance plan.

[1014] output:

[1015] A notification message will appear on the user's smartphone.

[1016] Specific behavior:

[1017] Insurance plan data is sent from the cloud server to the device via a notification API, and the UI component on the device displays the plan information.

[1018] Step 6: Real-time monitoring and safe driving advice

[1019] Device:

[1020] While driving, driving data and emotional data continue to be collected and sent to the server.

[1021] server:

[1022] The data transmitted in real time is analyzed to detect risky driving and the user's stress level.

[1023] Device:

[1024] If the risk is deemed high, the system will provide real-time safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax").

[1025] input:

[1026] Driving data, emotional data.

[1027] output:

[1028] Real-time driving advice, emotional care advice.

[1029] Specific behavior:

[1030] The server receives the data and simultaneously performs AI analysis, generating safe driving advice and emotional care advice based on the analysis results, which are then immediately sent to the device, which then displays a notification.

[1031] Step 7: Analyze and provide city-wide traffic data

[1032] server:

[1033] By centrally managing and analyzing driving data, emotion data, and accident information from across the city, traffic congestion locations and areas prone to accidents can be identified.

[1034] Users (municipalities and transport administrations):

[1035] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[1036] input:

[1037] Driving data, emotional data, traffic accident information.

[1038] output:

[1039] Analysis results on traffic congestion locations and areas with high accident rates.

[1040] Specific behavior:

[1041] The server analyzes the data weekly or monthly, automatically generating reports for urban planning and transportation infrastructure improvements, and provides them to local governments via a dedicated portal or by email.

[1042] Through these steps, the system can provide users with the most suitable insurance plans, promote safe driving and emotional care, and also contribute to improving traffic throughout the city.

[1043] (Application example 2)

[1044] 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."

[1045] Conventional automobile insurance systems generate insurance plans based solely on the user's driving data, but lack the ability to provide information on the user's emotional state or real-time safe driving advice. Furthermore, there is an insufficient mechanism for collecting traffic information and driving data from across a city and utilizing it for efficient urban planning and traffic improvement. Furthermore, there is no method for providing appropriate advice in real time when a user's emotional state fluctuates while driving.

[1046] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data in real time, means for acquiring past insurance history data, and means for analyzing the acquired driving data, insurance history data, and emotion data. This makes it possible to monitor the user's driving data and emotion data in real time, provide the most suitable insurance plan, and provide advice on safe driving and emotional care. In addition, traffic information for the entire city can be collected, enabling efficient urban planning and traffic improvements.

[1047] "Driving data" refers to various data related to vehicle driving, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[1048] "Emotional data" refers to data about the user's emotional state analyzed from facial expressions, voice, etc.

[1049] "Insurance history data" refers to information about insurance policies that a user has previously signed up for.

[1050] "Analysis" refers to the act of analyzing collected driving data and emotional data using AI algorithms and other methods to derive meaningful results.

[1051] An "insurance plan" refers to a plan that combines the contents and conditions of vehicle insurance.

[1052] "Notification" refers to the action of informing users of analysis results and insurance plans.

[1053] "Monitoring" refers to the act of monitoring a user's driving data and status in real time.

[1054] "Safe driving advice" refers to instructions or suggestions provided to the user while driving to promote safe driving.

[1055] "Emotional care advice" refers to advice to reduce stress and promote relaxation based on the user's emotional state.

[1056] "City-wide driving data" refers to various driving information collected within a specific city.

[1057] "Traffic information" refers to information related to road traffic, such as traffic volume, congestion, and accidents.

[1058] "Urban planning" refers to plans aimed at developing cities and improving transportation infrastructure.

[1059] "Transportation improvement" refers to policies and practices that aim to achieve more efficient and safer transportation conditions.

[1060] In this invention, the following specific embodiments are implemented as a system for providing insurance plans and safe driving advice while monitoring the emotional state of an autonomous vehicle and its driver.

[1061] System Overview

[1062] Device:

[1063] The smart glasses worn by the driver are equipped with a camera, microphone, and various sensors to collect driving data and user emotional data in real time, including facial expression and voice analysis.

[1064] server:

[1065] The server integrates the collected driving data, emotional data, and past insurance history data and analyzes them using AI algorithms. As a result of the analysis, it generates the optimal insurance plan and notifies the user. It also provides safe driving advice and emotional care advice in real time.

[1066] Hardware and software used:

[1067] Hardware:

[1068] Smart glasses (camera, microphone, various sensors)

[1069] Cloud server (data analysis and AI algorithm execution)

[1070] software:

[1071] EmotionAI (emotion recognition software)

[1072] DataCollector (driving data collection system)

[1073] Notification System

[1074] InsuranceEngine (insurance plan generation engine)

[1075] Specific operations of each method

[1076] Driving and emotion data collection:

[1077] Device: The smart glasses collect driving data such as vehicle speed, mileage, and number of sudden braking. At the same time, they use a camera and microphone to analyze facial expressions and voice to collect driver emotional data.

[1078] Real-time data transmission:

[1079] Terminal: Collected driving data and emotion data are sent to a cloud server at regular intervals.

[1080] Data Analysis and Notification:

[1081] Server: Analyzes the received driving data, emotion data, and insurance history data using AI algorithms to generate the optimal insurance plan.

[1082] Server: Evaluates risks and the user's stress state while driving, and generates advice on safe driving and emotional care in real time.

[1083] Terminal: The generated insurance plan and advice will be displayed on the smart glasses display.

[1084] Specific example explanation

[1085] 1. Insurance plan proposal

[1086] While the user is driving, the smart glasses collect driving data such as speed, distance traveled, and number of sudden braking attempts, and use a camera and microphone to analyze facial expressions and voice changes to collect emotional data.

[1087] The server consolidates and analyzes the data to generate the optimal insurance plan.

[1088] The generated insurance plan will be pushed to the smart glasses, allowing the user to check the plan on the spot.

[1089] 2. Providing safe driving advice

[1090] When a user suddenly brakes, the smart glasses detect driving data and analyze emotional data using a camera and microphone.

[1091] The server analyzes the data in real time and generates notifications such as "This is an area with a lot of sudden braking, please be careful" and "We recommend you take a deep breath and relax."

[1092] These notifications are sent to smart glasses, allowing users to continue driving safely and regulate their emotions.

[1093] Prompt Sentence Examples

[1094] Prompt sentence to input to the generative AI model:

[1095] "When a user suddenly brakes while driving, analyze their emotional and driving data and simultaneously provide advice on safe driving and emotional care."

[1096] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1097] Step 1:

[1098] The device uses smart glasses to collect driving and emotional data in real time. Driving data includes vehicle speed, mileage, and number of sudden braking incidents, while emotional data is the result of analyzing facial expressions and voice using a camera and microphone. Data is acquired as input from the vehicle's sensors and the user's emotional sensors, and is temporarily stored in internal memory. Formatted driving and emotional data is obtained as output.

[1099] Step 2:

[1100] The device transmits the collected driving data and emotion data to the cloud server at regular intervals. The driving data and emotion data collected in step 1 are used as input and are transmitted to the cloud server using the data communication module. As output, the device confirms that the data is transmitted to the cloud server and records it in a log appropriately.

[1101] Step 3:

[1102] The server analyzes the received driving and emotion data. It uses the data sent in step 2 as input and analyzes each data using EmotionAI and DataCollector. It identifies driving patterns and emotional states and assesses their respective risks. The results of this analysis are output and form the basis for generating insurance plans and real-time advice.

[1103] Step 4:

[1104] The server generates the optimal insurance plan based on the analysis results. The analysis results from step 3 and past insurance history data are used as input. InsuranceEngine uses this data to generate an insurance plan suitable for the user. The generated insurance plan is output and stored as a dataset to be notified to the user.

[1105] Step 5:

[1106] The server generates safe driving advice and emotional care advice based on the analysis results. It uses the analysis results from step 3 as input and generates advice based on the risk assessment results. The NotificationSystem creates these advice messages and saves them as messages to be sent to the user as output.

[1107] Step 6:

[1108] The device notifies the user of the generated insurance plan, safe driving advice, and emotional care advice. The data generated in steps 4 and 5 are used as input. Notifications are displayed on the smart glasses display and sent to the user using the vibration function. The output is that the user can check these notifications, which encourages them to select an appropriate insurance plan and drive safely.

[1109] 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.

[1110] 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.

[1111] 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.

[1112] [Third embodiment]

[1113] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1115] 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).

[1116] 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.

[1117] 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.

[1118] 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).

[1119] 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.

[1120] 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.

[1121] 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.

[1122] 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.

[1123] 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.

[1124] 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."

[1125] MODE FOR CARRYING OUT THE INVENTION

[1126] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[1127] System Overview

[1128] The system's main functions are driving data collection, data analysis, insurance plan generation and notification, real-time monitoring, safe driving advice provision, and city-wide traffic data collection and analysis. Each function is explained in detail below.

[1129] Data collection methods

[1130] Device:

[1131] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. The collected data is sent to a cloud server at regular intervals.

[1132] Data analysis format

[1133] server:

[1134] The server combines the received driving data with insurance history data and analyzes it using AI algorithms, thereby identifying the user's driving patterns (frequency of sudden braking, frequency of high-speed driving, etc.) and the risk of traffic accidents in each region.

[1135] Insurance plan generation and notification forms

[1136] server:

[1137] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history.

[1138] Device:

[1139] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[1140] Real-time monitoring and safe driving advice provision

[1141] Device:

[1142] While the user is driving, driving data is sent to a server in real time.

[1143] server:

[1144] The server analyzes the received driving data in real time and detects high-risk driving situations.

[1145] Device:

[1146] If the risk is deemed high, safe driving advice (for example, "This is an area where sudden braking is common. Please be careful") will be sent to the device, allowing the user to correct their driving on the spot.

[1147] City-wide transportation data collection and analysis

[1148] server:

[1149] Driving data and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[1150] Users (municipalities and transport administrations):

[1151] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[1152] Specific examples

[1153] Insurance plan proposals

[1154] User: The user driving the car has a smartphone inside the car.

[1155] Device: The smartphone collects the user's driving data and sends it to the server.

[1156] Server: Analyzes driving data and insurance history to generate the optimal insurance plan for the user.

[1157] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[1158] Providing safe driving advice

[1159] User: While the user is driving, their smartphone collects data on sudden braking.

[1160] Server: Analyzes data in real time and detects frequent occurrences of sudden braking in specific areas.

[1161] Device: The user is notified with the advice, "Please be careful as there are many sudden brakings in this area." The user can take the advice and drive safely.

[1162] With these functions, the present invention realizes a system that provides users with the most suitable insurance plan, promotes safe driving, and also contributes to improving traffic throughout the city.

[1163] The processing flow will be explained below.

[1164] Step 1:

[1165] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) in real time.

[1166] Step 2:

[1167] Terminal: Sends collected driving data to a cloud server at regular intervals.

[1168] Step 3:

[1169] Server: The cloud server stores the received driving data and integrates it with the user's past insurance history data.

[1170] Step 4:

[1171] Server: Using AI algorithms, it analyzes driving data and insurance history data to identify the user's driving patterns (such as frequency of sudden braking and high speed driving).

[1172] Step 5:

[1173] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[1174] Step 6:

[1175] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[1176] Step 7:

[1177] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[1178] Step 8:

[1179] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[1180] Step 9:

[1181] Device: Collects driving data in real time while the user is driving and transmits it to the server.

[1182] Step 10:

[1183] Server: Monitors real-time driving data and detects high-risk driving situations (e.g., areas with frequent sudden braking or weather-related risks).

[1184] Step 11:

[1185] Server: Generates safe driving advice based on the detected risks.

[1186] Step 12:

[1187] Device: Notify the user of the generated safe driving advice (e.g., "Be careful as there are many sudden brakings in this area").

[1188] Step 13:

[1189] Server: Driving data and traffic accident data collected from users are accumulated across the city and stored as large-scale data.

[1190] Step 14:

[1191] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[1192] Step 15:

[1193] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[1194] Step 16:

[1195] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[1196] Example 1

[1197] 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."

[1198] In modern urban environments, improving driving safety and providing optimal insurance plans are important challenges. Conventional systems do not adequately collect and analyze driving data, making it difficult to provide users with appropriate insurance plans. Furthermore, they are inadequate at predicting traffic accident risks and providing safe driving advice in real time, limiting their ability to improve traffic throughout the city.

[1199] 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.

[1200] In this invention, the server includes: means for collecting driving data in real time; means for acquiring past insurance history data; means for analyzing the acquired driving data and insurance history data; means for generating an optimal insurance plan based on the analysis results; means for notifying the user of the generated insurance plan; means for monitoring the user's driving data in real time; means for assessing driving risks and providing safe driving advice; means for collecting and analyzing driving data and traffic information for the entire city; means for providing data for urban planning and traffic improvement; means for identifying driving patterns using an AI algorithm; means for detecting high-risk driving situations; and means for identifying areas with high traffic accident rates based on the analysis results. This enables the server to provide optimal insurance plans to users and provide safe driving advice in real time. This also contributes to improving traffic conditions throughout the city and reducing the risk of traffic accidents.

[1201] "Driving data" refers to information related to driving a vehicle, such as vehicle speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[1202] "Insurance history data" refers to information about insurance contracts that a user has had in the past.

[1203] "Analyzing means" refers to methods or devices used to process collected data and identify patterns and risks.

[1204] "Insurance Plan" means the terms and conditions of automobile insurance offered to a Driver.

[1205] "Real-time monitoring means" refers to a method or device for real-time monitoring and analysis of vehicle data during operation.

[1206] "Safe driving advice" refers to instructions and advice provided to the driver based on the analysis results to improve driving safety.

[1207] "Traffic information" refers to information related to traffic, such as road conditions, traffic volume, and accident occurrence status.

[1208] An "AI algorithm" is a method for analyzing data using machine learning and artificial intelligence techniques to detect patterns and make predictions.

[1209] A "driving pattern" refers to a statistical feature of data that indicates a driver's driving tendencies and characteristics.

[1210] A "high-risk driving situation" is a driving condition or situation that is prone to causing traffic accidents or other problems.

[1211] A "high traffic accident area" is a location in a specific region or road section where traffic accidents occur frequently.

[1212] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[1213] Collecting and transmitting driving data

[1214] Device: When a user drives a car, their smartphone or in-car device collects real-time driving data such as speed, distance traveled, number of sudden braking attempts, accelerator operation, etc. This data is sent to a cloud server at regular intervals via the smartphone's communication function.

[1215] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration and immediately sends the data to the server.

[1216] Analysis of driving data and insurance history

[1217] Server: The server combines the received driving data with past insurance history data and analyzes it using an AI algorithm based on a generative AI model. This analysis identifies the user's driving patterns (such as frequency of sudden braking and high speed driving) and assesses the risk of traffic accidents in each area.

[1218] How it works: The server uses a deep learning model to classify the number of sudden braking and the frequency of high-speed driving as categorical targets. For example, the server clusters the user's sudden braking data and categorizes them into driving styles such as "safe," "cautious," and "dangerous."

[1219] Generate optimal insurance plans

[1220] Server: Based on the analysis results, the server generates the optimal insurance plan for the user. This plan takes into account the user's driving style and past insurance history.

[1221] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for a user who frequently brakes suddenly.

[1222] Insurance plan notification

[1223] Device: The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[1224] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, a message like "A new insurance plan has been proposed. Please review the details" will appear on the screen.

[1225] Real-time driving data monitoring

[1226] Device: While the user is driving, the smartphone continuously transmits driving data to the server.

[1227] How it works: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server. For example, if a user suddenly accelerates 100 meters in one minute, the data is sent to the server in real time.

[1228] Risk Advice

[1229] Server: The server analyzes driving data in real time and detects high-risk driving situations.

[1230] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in specific areas. For example, the server detects areas where sudden braking occurs frequently and flags them as "high risk."

[1231] Safe driving advice notifications

[1232] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[1233] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when a user enters an area where sudden braking is common, the user will be notified immediately.

[1234] Collecting and analyzing city-wide traffic data

[1235] Server: Centrally manages and analyzes driving data and accident information collected from each user, thereby identifying traffic congestion locations and areas prone to accidents.

[1236] How it works: The server uses a large database to analyze traffic data for the entire city, integrating it with map information. For example, it can detect that accidents frequently occur at a particular intersection and report that area to the local government.

[1237] Providing analysis results

[1238] Users (local governments and transportation administration agencies): The server's analysis results will be provided for urban planning and transportation infrastructure improvement, enabling local governments and transportation administration agencies to make effective improvements to transportation infrastructure.

[1239] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[1240] Example prompt: "Design a system that analyzes users' driving data in real time and provides advice on high-risk areas."

[1241] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1242] Step 1:

[1243] Input: The user starts driving the car.

[1244] Devices: Smartphones and in-car devices collect real-time driving data such as speed, distance traveled, number of hard brakes, and accelerator operation.

[1245] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration.

[1246] Output: Collected driving data.

[1247] Step 2:

[1248] Input: Driving data collected in step 1.

[1249] Terminal: Sends collected driving data to the cloud server at regular intervals.

[1250] Specific operation: Driving data collected by a smartphone is sent to a cloud server at regular intervals, for example, sending data every minute in batches.

[1251] Output: Driving data sent to cloud server.

[1252] Step 3:

[1253] Input: Driving data sent to the cloud server in step 2 and existing insurance history data.

[1254] Server: Integrates received driving data and past insurance history data and analyzes them using AI algorithms based on generative AI models.

[1255] How it works: The server uses a deep learning model to classify the number of sudden braking incidents and the frequency of high-speed driving as categorical targets. For example, it clusters data on sudden braking and categorizes driving styles as "safe," "cautious," or "dangerous."

[1256] Output: Driving patterns and traffic accident risk assessment as analysis results.

[1257] Step 4:

[1258] Input: Analysis results obtained in step 3.

[1259] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[1260] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for users who frequently brake suddenly.

[1261] Output: Optimal insurance plan.

[1262] Step 5:

[1263] Input: The insurance plan generated in step 4.

[1264] Terminal: The generated insurance plan is sent to the user's smartphone.

[1265] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, it might say, "A new insurance plan has been proposed. Please review the details."

[1266] Output: The user receives the insurance plan.

[1267] Step 6:

[1268] Input: Driving data collected continuously while the user is driving.

[1269] Terminal: Sends driving data to the server in real time.

[1270] Specific operation: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server, for example, every minute in real time.

[1271] Output: Real-time driving data.

[1272] Step 7:

[1273] Input: Real-time driving data sent in step 6.

[1274] Server: Analyzes driving data in real time and detects high-risk driving situations.

[1275] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in a specific area. For example, it detects locations where sudden braking frequently occurs.

[1276] Output: Risk notification data.

[1277] Step 8:

[1278] Input: Risk notification data obtained in step 7.

[1279] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[1280] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when entering an area where sudden braking is common, the user will be notified immediately.

[1281] Output: Real-time safe driving advice.

[1282] Step 9:

[1283] Input: Driving data and traffic information collected from each user.

[1284] Server: Centrally manages and analyzes driving data and traffic information for the entire city.

[1285] How it works: The server uses a large database to analyze traffic congestion and accident-prone areas by integrating them with map information. For example, it detects that accidents frequently occur at certain intersections.

[1286] Output: Analysis results for transportation infrastructure improvements.

[1287] Step 10:

[1288] Input: Analysis results obtained in step 9.

[1289] Users (local governments and transportation administration agencies): Provide analysis results for urban planning and transportation infrastructure improvement.

[1290] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[1291] Output: Proposed data for transportation improvements.

[1292] (Application example 1)

[1293] 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."

[1294] Conventional automobile insurance systems can only offer fixed insurance plans, making it difficult to propose optimal plans based on individual drivers' driving styles and driving data. They also lack real-time advice to promote safe driving, and there has been insufficient centralized management of driving data for the entire city to utilize for effective urban planning and traffic improvement. To address these issues, this invention provides a system that collects and analyzes driving data from autonomous vehicles in real time, proposes optimal insurance plans, and provides safe driving advice.

[1295] 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.

[1296] In this invention, the server includes a means for collecting driving data in real time, a means for acquiring past insurance history data, and a means for analyzing the acquired driving data and insurance history data. This makes it possible to analyze the collected driving data and insurance history data and provide optimal insurance plans for individual drivers. Furthermore, the monitoring means and evaluation means can evaluate driving risks in real time and provide safe driving advice, thereby promoting safe driving. Furthermore, by aggregating and analyzing driving data and traffic information for an entire city, it is possible to provide data for traffic improvement and urban planning.

[1297] "Driving data" refers to information related to the driving of an autonomous vehicle, such as its speed, distance traveled, accelerator operation, and braking operation.

[1298] "Insurance history data" refers to information about the contents of insurance contracts that a user has had up to now, as well as past accident and claim history.

[1299] "Analysis results" are information showing statistics and trends generated by analyzing collected driving data and insurance history data using AI and algorithms.

[1300] An "insurance plan" is a proposal of insurance products and contract terms optimized based on the user's driving data and insurance history data.

[1301] "Notifications" are means of informing users of generated insurance plans and safe driving advice, including push notifications to smartphones and in-car devices and emails.

[1302] "Monitoring" refers to the process of collecting and analyzing driving data in real time while the user is driving.

[1303] "Evaluation" is the process of determining driving risks based on collected driving data and providing safe driving advice.

[1304] "Risk" refers to accidents or dangerous driving situations that may occur while driving.

[1305] "Safe driving advice" refers to driving improvement suggestions and warnings provided to users based on real-time driving data.

[1306] An "autonomous vehicle" is a vehicle that operates autonomously using artificial intelligence and sensors.

[1307] "Urban planning" is a plan aimed at developing the entire city and improving transportation infrastructure.

[1308] "Traffic improvement" refers to the review of policies and infrastructure to smooth traffic flow and reduce accidents.

[1309] MODE FOR CARRYING OUT THE INVENTION

[1310] This invention is a system that proposes optimal insurance plans based on driving data from autonomous vehicles and promotes safe driving. This system includes functions such as collecting driving data, analyzing the data, generating and notifying insurance plans, real-time monitoring, and providing safe driving advice.

[1311] System configuration

[1312] 1. Data Collection

[1313] Using built-in sensors in autonomous vehicles and smartphones, driving data such as speed, distance traveled, accelerator operation, and brake operation is collected in real time, and this data is sent to a cloud server at regular intervals.

[1314] 2. Data Analysis

[1315] The server is built on the cloud and collects and analyzes the collected driving data and the user's past insurance history data. It processes the data using AI algorithms (e.g., TensorFlow and Scikit-learn) to identify driving patterns.

[1316] 3. Insurance plan generation and notification

[1317] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history. The generated insurance plan is then notified to the user via push notification or email on their smartphone or in-car display.

[1318] 4. Real-time monitoring and safe driving advice

[1319] The server analyzes driving data in real time to detect high-risk driving situations. If a high risk is detected, the user is notified in real time with advice such as "Please be careful as there are many sudden brakes in this area." This allows the user to correct their driving on the spot. The Geopy library is used to analyze location information and identify risk zones.

[1320] 5. City-wide traffic data analysis

[1321] Driving data and traffic information from across the city will be collected and integrated and analyzed on a cloud server. The analysis results will be provided to local governments and transportation administration agencies, who will be able to identify areas with high traffic congestion and accident rates and use them to improve these areas.

[1322] Specific examples

[1323] 1. Data collection and transmission:

[1324] Data such as the speed of the self-driving vehicle and the number of times it brakes suddenly is collected and sent to a cloud server.

[1325] 2. Data Analysis:

[1326] The collected data is analyzed using an AI model to identify driving patterns and risk areas.

[1327] 3. Insurance plan generation and notification:

[1328] The system generates and notifies the user of the optimal insurance plan based on their driving data and past insurance history.

[1329] 4. Providing safe driving advice:

[1330] It analyzes driving data in real time, and if a danger is detected, it immediately notifies the user with advice.

[1331] Prompt Sentence Examples

[1332] Driving data collection:

[1333] It collects user driving data (speed, number of sudden braking) and sends it to the server in the following format:

[1334] Speed: 60km / h

[1335] Hard braking: 3 times

[1336] Insurance plan suggestions:

[1337] It analyzes the user's driving data and insurance history and proposes insurance plans in the following format:

[1338] (User information)

[1339] Driving data: Speed: '60km / h', Hard braking: '3 times'

[1340] Insurance history: 'No accidents in the last three years'

[1341] (Proposal details)

[1342] Best Insurance Plan: 'Safe Driving Discount Plan', 'Discount Rate: 20%'

[1343] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1344] Step 1:

[1345] Data collection

[1346] The device (built-in sensors in the autonomous vehicle or a smartphone) collects driving data such as speed, distance traveled, accelerator operation, and braking operation in real time. This data is used to record the vehicle's movements in detail while driving. The collected data is temporarily stored in the device.

[1347] Input: Driving data from sensors and smartphones

[1348] Output: Driving data stored in the device

[1349] Step 2:

[1350] Data transmission

[1351] The device periodically transmits the collected driving data to a cloud server. Data transmission requires an internet connection and is encrypted using a secure communication protocol.

[1352] Input: Driving data stored in the device

[1353] Output: Driving data sent to the cloud server

[1354] Step 3:

[1355] Data analysis

[1356] The server combines the received driving data with the user's past insurance history data and analyzes it using AI algorithms (e.g., TensorFlow and Scikit-learn). This analysis identifies driving patterns (such as frequency of sudden braking and high speed driving) and traffic accident risks by region.

[1357] Input: Driving data and insurance history data sent to the cloud server

[1358] Output: Analysis results (driving patterns, traffic accident risk)

[1359] Step 4:

[1360] Generate an insurance plan

[1361] The server then generates the optimal insurance plan for the user based on the analysis results, taking into account the user's driving style and past insurance history. The generated insurance plan is saved in a dedicated format.

[1362] Input: Analysis results (driving patterns, traffic accident risk)

[1363] Output: Generated insurance plan

[1364] Step 5:

[1365] Insurance plan notification

[1366] The server then notifies the user of the generated insurance plan via a smartphone, in-car display, push notification, or email.

[1367] Input: Generated insurance plan

[1368] Output: The insurance plan that the user was notified of

[1369] Step 6:

[1370] Real-time monitoring

[1371] The device monitors driving data in real time and sends it to a server, allowing the driver to constantly monitor their driving situation.

[1372] Input: Data during driving

[1373] Output: Real-time data sent to the server

[1374] Step 7:

[1375] Risk assessment and advice provision

[1376] The server analyzes the driving data received in real time and detects high-risk driving situations. Appropriate safe driving advice is generated for the detected risks and sent to the device, which then notifies the user.

[1377] Input: Driving data received in real time

[1378] Output: Risk assessment results, safe driving advice

[1379] Step 8:

[1380] City-wide traffic data analysis

[1381] The server will integrate and analyze driving data and traffic information collected from across the city, and the analysis results will be provided to local governments and transportation administration agencies to help with urban planning and transportation infrastructure improvements.

[1382] Input: Driving data and traffic information collected from across the city

[1383] Output: Analysis results for urban planning and transportation improvement

[1384] 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.

[1385] MODE FOR CARRYING OUT THE INVENTION

[1386] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described.

[1387] System Overview

[1388] The system has the functions of collecting driving data, analyzing data, generating and notifying insurance plans, real-time monitoring, providing safe driving advice, recognizing user emotions using an emotion engine, and collecting and analyzing traffic data for the entire city. Each function is explained in detail below.

[1389] Data collection methods

[1390] Device:

[1391] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. In addition, cameras, microphones, and various sensors are used to analyze the user's facial expressions and voice, and emotional data is also collected. The collected data is sent to a cloud server at regular intervals.

[1392] Data analysis format

[1393] server:

[1394] The server integrates the received driving data, emotional data, and insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[1395] Insurance plan generation and notification forms

[1396] server:

[1397] Based on the analysis results, the system generates an optimal insurance plan for the user, which takes into account the user's driving style, past insurance history, and even emotional state.

[1398] Device:

[1399] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[1400] Real-time monitoring and safe driving advice provision

[1401] Device:

[1402] While the user is driving, driving data and emotional data are continuously collected in real time and sent to the server.

[1403] server:

[1404] The server analyzes the received driving and emotional data in real time to detect high-risk driving situations and the user's stress level.

[1405] Device:

[1406] If the risk is deemed high, the device will provide safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax"), allowing the user to correct their driving on the spot and regulate their emotional state.

[1407] City-wide transportation data collection and analysis

[1408] server:

[1409] Driving data, emotion data, and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[1410] Users (municipalities and transport administrations):

[1411] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[1412] Specific examples

[1413] Insurance plan proposals

[1414] User: The user driving the car has a smartphone inside the car.

[1415] Device: The smartphone collects the user's driving data and emotional data and sends it to the server.

[1416] Server: Analyzes driving data, emotional data, and insurance history, and generates the optimal insurance plan taking into account the user's stress level, etc.

[1417] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[1418] Providing safe driving advice

[1419] User: While the user is driving, the smartphone collects data on sudden braking, facial expressions, and changes in voice.

[1420] Server: Analyzes data in real time to detect frequent sudden braking in a specific area, and also detects when the user is feeling stressed.

[1421] Device: The device notifies the user, "Please be careful as there are many sudden brakings in this area," along with advice on both safe driving and emotional care, such as, "We recommend taking a deep breath and relaxing." By receiving this advice, the user can continue driving safely and also take care of their emotions.

[1422] With these functions, the present invention realizes a system that provides users with optimal insurance plans, promotes safe driving and emotional care, and also contributes to improving traffic throughout the city.

[1423] The processing flow will be explained below.

[1424] Processing steps of a system that combines emotion engines

[1425] Step 1:

[1426] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) and emotional data (facial expressions, tone of voice, etc.) in real time.

[1427] Step 2:

[1428] Terminal: Collected driving data and emotion data are sent to the cloud server at regular intervals.

[1429] Step 3:

[1430] Server: The cloud server stores the received driving data and emotion data and integrates it with the user's past insurance history data.

[1431] Step 4:

[1432] Server: Using AI algorithms, it analyzes driving data, emotional data, and insurance history data to identify the user's driving patterns (frequency of sudden braking, frequency of high speed driving, etc.) and emotional state.

[1433] Step 5:

[1434] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[1435] Step 6:

[1436] Server: Based on the analysis results, it generates the optimal insurance plan for the user, taking into account the user's emotional state.

[1437] Step 7:

[1438] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[1439] Step 8:

[1440] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[1441] Step 9:

[1442] Device: While the user is driving, it continues to collect driving data and emotional data in real time and transmits it to the server.

[1443] Step 10:

[1444] Server: Monitors real-time driving and emotional data to assess risky driving situations and the user's stress level.

[1445] Step 11:

[1446] Server: Based on the detected risks, it generates safe driving advice (e.g., "Please be careful as there are many sudden brakes in this area") and emotional care advice (e.g., "We recommend that you take a deep breath and relax").

[1447] Step 12:

[1448] On the device: The generated advice is notified to the user via a pop-up notification and / or a sound notification.

[1449] Step 13:

[1450] Server: Driving data, emotion data, and traffic accident data collected from users across the city are accumulated and stored as large-scale data.

[1451] Step 14:

[1452] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[1453] Step 15:

[1454] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[1455] Step 16:

[1456] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[1457] Example 2

[1458] 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."

[1459] Conventional automobile insurance systems propose insurance plans based on driving data and past insurance history, but because they do not take into account the user's emotional state, they have the problem of not being able to provide the optimal plan for each individual driver. They also lack real-time driving advice and emotional care, which means users' safe driving is not adequately supported. Furthermore, because traffic data for the entire city is not collected or analyzed, they are unable to provide useful information for urban planning and transportation infrastructure improvements.

[1460] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting driving data and emotion data in real time, a means for acquiring past insurance history data and emotion history data, and a means for integrating and analyzing the acquired driving data, emotion data, and insurance history data. This makes it possible to accurately grasp the user's driving patterns and emotional state and provide an optimal insurance plan and real-time safe driving advice and emotional care advice. It also becomes possible to analyze driving data, emotion data, and traffic accident information for an entire city and provide useful data for urban planning and transportation infrastructure improvement.

[1461] "Driving data" refers to information collected while the vehicle is traveling, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[1462] "Emotional data" is information that indicates the emotional state of a user, obtained from facial expressions, tone of voice, body movements, etc.

[1463] "Insurance history data" refers to data such as the user's past insurance contract details, accident history, and claim information.

[1464] "Emotion history data" is data that indicates the fluctuations in the user's past emotional state and the results of their analysis.

[1465] "Means for integrating and analyzing" refers to means for combining and analyzing various acquired data to identify the user's driving patterns, emotional state, and traffic risks.

[1466] "Driving patterns" refers to the analysis results of a user's driving style, specific behaviors, habits, etc.

[1467] The "optimal insurance plan" is the most appropriate insurance contract plan proposed to each individual user based on the user's driving data, emotional data, and insurance history data.

[1468] "Real-time monitoring means" refers to a method for continuously collecting data during operation and analyzing it on the spot to always obtain the latest information.

[1469] "Safe driving advice" means providing the user with specific instructions and suggestions for safe driving that are tailored to the driving conditions at the time.

[1470] "Emotional care advice" refers to providing specific instructions or suggestions for relaxation and stress reduction based on the user's emotional state.

[1471] "Data for urban planning and transportation infrastructure improvement" refers to information that is useful for developing plans for traffic management and infrastructure improvement, such as analyzing driving data, emotion data, and traffic accident information from across the city.

[1472] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described in detail.

[1473] System Overview

[1474] The system collects driving and emotional data, proposes insurance plans, and provides real-time advice on safe driving and emotional care. It also collects and analyzes traffic data from across the city to contribute to traffic improvements. Its main components include the user's smartphone or in-car device, a cloud server, a generative AI model, and an emotion engine.

[1475] Hardware and software used

[1476] Hardware:

[1477] Smartphones (e.g. iPhone, Android devices)

[1478] In-car devices (e.g. OBD-II adapter)

[1479] software:

[1480] Cloud server (e.g. AWS, Google Cloud)

[1481] AI algorithms (e.g. TensorFlow, PyTorch)

[1482] Emotion recognition software (e.g., Microsoft Azure Emotion API)

[1483] Data collection

[1484] Device:

[1485] As soon as the vehicle's engine starts, the user's smartphone or in-car device collects driving data such as speed, distance traveled, number of sudden braking attempts, and accelerator operation. The smartphone's built-in camera, microphone, and acceleration sensor are also used to analyze the user's facial expressions and voice, collecting emotional data. This data is then sent to a cloud server at regular intervals.

[1486] Example: A smartphone camera captures facial expressions and analyzes them with emotion recognition software, while a microphone detects the tone of the user's voice and passes the data to an emotion engine.

[1487] Data analysis

[1488] server:

[1489] The cloud server integrates driving data, emotional data, and past insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the area where they drive.

[1490] Example: The server uses an AI model to analyze driving and emotional data to identify a user's tendency to brake frequently and their high stress levels.

[1491] Insurance plan generation and notification

[1492] server:

[1493] The analysis results are used to generate a personalized insurance plan that takes into account your driving style, past insurance history, and emotional state.

[1494] Device:

[1495] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[1496] Examples:

[1497] Prompt: "The user's driving style involves frequent sudden braking, and their past insurance history has categorized them as a high-risk driver. Their emotional data has revealed a state of stress. Please recommend the most appropriate insurance plan."

[1498] Based on the results of the server's analysis, the optimal insurance plan will be notified to the smartphone.

[1499] Real-time monitoring and safe driving advice

[1500] Device:

[1501] Driving data and emotional data are continuously collected while driving and sent to the server.

[1502] server:

[1503] It analyzes data in real time to detect risky driving and the user's stress level.

[1504] Device:

[1505] If the risk is deemed high, the system will notify the user in real time of safe driving advice and emotional care advice.

[1506] Examples:

[1507] Prompt: "The user frequently brakes suddenly in a specific area, and emotional data indicates high stress. Please provide appropriate safe driving advice and emotional management advice."

[1508] The server identifies areas where sudden braking occurs frequently and sends a message to the driver's smartphone saying, "This area is prone to sudden braking, so please be careful." At the same time, it provides emotional care advice such as, "We recommend that you take a deep breath and relax."

[1509] Analysis and provision of city-wide traffic data

[1510] server:

[1511] By centrally managing and analyzing driving data, emotion data, and traffic accident information from across the city, we can identify areas where traffic congestion occurs and where accidents are frequent.

[1512] Users (municipalities and transport administrations):

[1513] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[1514] Example: A server analyzes city-wide data on a weekly basis to identify areas with high traffic congestion, and provides this data to a dedicated portal for the local government.

[1515] This allows the present invention to provide users with optimal insurance plans, promote safe driving and emotional care, and contribute to improving traffic throughout the city.

[1516] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1517] Step 1: Start collecting data

[1518] Device:

[1519] As soon as the vehicle engine starts, the smartphone or in-car device collects driving data (speed, distance traveled, number of hard braking attempts, accelerator operation, etc.) and emotional data (facial expressions, tone of voice, body movements, etc.) Once collected, this data is stored in a buffer at regular intervals.

[1520] input:

[1521] The vehicle engine starts and the terminal begins operation.

[1522] output:

[1523] The collected driving data and emotion data are stored in a buffer.

[1524] Specific behavior:

[1525] The smartphone camera captures the user's facial expressions, the microphone detects the tone of their voice, and sensors record driving data.

[1526] Step 2: Send data to the cloud server

[1527] Device:

[1528] The collected data is compressed at regular intervals and sent to a cloud server using HTTPS.

[1529] input:

[1530] Driving and emotional data collected.

[1531] output:

[1532] The compressed data is sent to a cloud server.

[1533] Specific behavior:

[1534] The device compresses the data and sends it to the cloud server using a secure communication protocol.

[1535] Step 3: Data synthesis and analysis

[1536] server:

[1537] The cloud server combines the received driving data, emotional data, and past insurance history data and analyzes them using AI algorithms (e.g., TensorFlow models). The analysis identifies the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[1538] input:

[1539] Driving data, emotional data, past insurance history data.

[1540] output:

[1541] Analysis results on users' driving patterns, emotional state, and traffic accident risk.

[1542] Specific behavior:

[1543] The server stores the data in a database and then runs an AI model to analyze various data.

[1544] Step 4: Generate your insurance plan

[1545] server:

[1546] Based on the analysis results, the system generates an optimal insurance plan that reflects the user's driving style, past insurance history, and emotional state.

[1547] input:

[1548] Analysis results, driving patterns, emotional state, and traffic accident risk data.

[1549] output:

[1550] The best insurance plan for you.

[1551] Specific behavior:

[1552] Based on the analysis results generated by the AI ​​model, the optimal insurance plan for the user is calculated and stored in a database.

[1553] Step 5: Insurance plan notification

[1554] Device:

[1555] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[1556] input:

[1557] The best insurance plan.

[1558] output:

[1559] A notification message will appear on the user's smartphone.

[1560] Specific behavior:

[1561] Insurance plan data is sent from the cloud server to the device via a notification API, and the UI component on the device displays the plan information.

[1562] Step 6: Real-time monitoring and safe driving advice

[1563] Device:

[1564] While driving, driving data and emotional data continue to be collected and sent to the server.

[1565] server:

[1566] The data transmitted in real time is analyzed to detect risky driving and the user's stress level.

[1567] Device:

[1568] If the risk is deemed high, the system will provide real-time safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax").

[1569] input:

[1570] Driving data, emotional data.

[1571] output:

[1572] Real-time driving advice, emotional care advice.

[1573] Specific behavior:

[1574] The server receives the data and simultaneously performs AI analysis, generating safe driving advice and emotional care advice based on the analysis results, which are then immediately sent to the device, which then displays a notification.

[1575] Step 7: Analyze and provide city-wide traffic data

[1576] server:

[1577] By centrally managing and analyzing driving data, emotion data, and accident information from across the city, traffic congestion locations and areas prone to accidents can be identified.

[1578] Users (municipalities and transport administrations):

[1579] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[1580] input:

[1581] Driving data, emotional data, traffic accident information.

[1582] output:

[1583] Analysis results on traffic congestion locations and areas with high accident rates.

[1584] Specific behavior:

[1585] The server analyzes the data weekly or monthly, automatically generating reports for urban planning and transportation infrastructure improvements, and provides them to local governments via a dedicated portal or by email.

[1586] Through these steps, the system can provide users with the most suitable insurance plans, promote safe driving and emotional care, and also contribute to improving traffic throughout the city.

[1587] (Application example 2)

[1588] 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."

[1589] Conventional automobile insurance systems generate insurance plans based solely on the user's driving data, but lack the ability to provide information on the user's emotional state or real-time safe driving advice. Furthermore, there is an insufficient mechanism for collecting traffic information and driving data from across a city and utilizing it for efficient urban planning and traffic improvement. Furthermore, there is no method for providing appropriate advice in real time when a user's emotional state fluctuates while driving.

[1590] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data in real time, means for acquiring past insurance history data, and means for analyzing the acquired driving data, insurance history data, and emotion data. This makes it possible to monitor the user's driving data and emotion data in real time, provide the most suitable insurance plan, and provide advice on safe driving and emotional care. In addition, traffic information for the entire city can be collected, enabling efficient urban planning and traffic improvements.

[1591] "Driving data" refers to various data related to vehicle driving, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[1592] "Emotional data" refers to data about the user's emotional state analyzed from facial expressions, voice, etc.

[1593] "Insurance history data" refers to information about insurance policies that a user has previously signed up for.

[1594] "Analysis" refers to the act of analyzing collected driving data and emotional data using AI algorithms and other methods to derive meaningful results.

[1595] An "insurance plan" refers to a plan that combines the contents and conditions of vehicle insurance.

[1596] "Notification" refers to the action of informing users of analysis results and insurance plans.

[1597] "Monitoring" refers to the act of monitoring a user's driving data and status in real time.

[1598] "Safe driving advice" refers to instructions or suggestions provided to the user while driving to promote safe driving.

[1599] "Emotional care advice" refers to advice to reduce stress and promote relaxation based on the user's emotional state.

[1600] "City-wide driving data" refers to various driving information collected within a specific city.

[1601] "Traffic information" refers to information related to road traffic, such as traffic volume, congestion, and accidents.

[1602] "Urban planning" refers to plans aimed at developing cities and improving transportation infrastructure.

[1603] "Transportation improvement" refers to policies and practices that aim to achieve more efficient and safer transportation conditions.

[1604] In this invention, the following specific embodiments are implemented as a system for providing insurance plans and safe driving advice while monitoring the emotional state of an autonomous vehicle and its driver.

[1605] System Overview

[1606] Device:

[1607] The smart glasses worn by the driver are equipped with a camera, microphone, and various sensors to collect driving data and user emotional data in real time, including facial expression and voice analysis.

[1608] server:

[1609] The server integrates the collected driving data, emotional data, and past insurance history data and analyzes them using AI algorithms. As a result of the analysis, it generates the optimal insurance plan and notifies the user. It also provides safe driving advice and emotional care advice in real time.

[1610] Hardware and software used:

[1611] Hardware:

[1612] Smart glasses (camera, microphone, various sensors)

[1613] Cloud server (data analysis and AI algorithm execution)

[1614] software:

[1615] EmotionAI (emotion recognition software)

[1616] DataCollector (driving data collection system)

[1617] Notification System

[1618] InsuranceEngine (insurance plan generation engine)

[1619] Specific operations of each method

[1620] Driving and emotion data collection:

[1621] Device: The smart glasses collect driving data such as vehicle speed, mileage, and number of sudden braking. At the same time, they use a camera and microphone to analyze facial expressions and voice to collect driver emotional data.

[1622] Real-time data transmission:

[1623] Terminal: Collected driving data and emotion data are sent to a cloud server at regular intervals.

[1624] Data Analysis and Notification:

[1625] Server: Analyzes the received driving data, emotion data, and insurance history data using AI algorithms to generate the optimal insurance plan.

[1626] Server: Evaluates risks and the user's stress state while driving, and generates advice on safe driving and emotional care in real time.

[1627] Terminal: The generated insurance plan and advice will be displayed on the smart glasses display.

[1628] Specific example explanation

[1629] 1. Insurance plan proposal

[1630] While the user is driving, the smart glasses collect driving data such as speed, distance traveled, and number of sudden braking attempts, and use a camera and microphone to analyze facial expressions and voice changes to collect emotional data.

[1631] The server consolidates and analyzes the data to generate the optimal insurance plan.

[1632] The generated insurance plan will be pushed to the smart glasses, allowing the user to check the plan on the spot.

[1633] 2. Providing safe driving advice

[1634] When a user suddenly brakes, the smart glasses detect driving data and analyze emotional data using a camera and microphone.

[1635] The server analyzes the data in real time and generates notifications such as "This is an area with a lot of sudden braking, please be careful" and "We recommend you take a deep breath and relax."

[1636] These notifications are sent to smart glasses, allowing users to continue driving safely and regulate their emotions.

[1637] Prompt Sentence Examples

[1638] Prompt sentence to input to the generative AI model:

[1639] "When a user suddenly brakes while driving, analyze their emotional and driving data and simultaneously provide advice on safe driving and emotional care."

[1640] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1641] Step 1:

[1642] The device uses smart glasses to collect driving and emotional data in real time. Driving data includes vehicle speed, mileage, and number of sudden braking incidents, while emotional data is the result of analyzing facial expressions and voice using a camera and microphone. Data is acquired as input from the vehicle's sensors and the user's emotional sensors, and is temporarily stored in internal memory. Formatted driving and emotional data is obtained as output.

[1643] Step 2:

[1644] The device transmits the collected driving data and emotion data to the cloud server at regular intervals. The driving data and emotion data collected in step 1 are used as input and are transmitted to the cloud server using the data communication module. As output, the device confirms that the data is transmitted to the cloud server and records it in a log appropriately.

[1645] Step 3:

[1646] The server analyzes the received driving and emotion data. It uses the data sent in step 2 as input and analyzes each data using EmotionAI and DataCollector. It identifies driving patterns and emotional states and assesses their respective risks. The results of this analysis are output and form the basis for generating insurance plans and real-time advice.

[1647] Step 4:

[1648] The server generates the optimal insurance plan based on the analysis results. The analysis results from step 3 and past insurance history data are used as input. InsuranceEngine uses this data to generate an insurance plan suitable for the user. The generated insurance plan is output and stored as a dataset to be notified to the user.

[1649] Step 5:

[1650] The server generates safe driving advice and emotional care advice based on the analysis results. It uses the analysis results from step 3 as input and generates advice based on the risk assessment results. The NotificationSystem creates these advice messages and saves them as messages to be sent to the user as output.

[1651] Step 6:

[1652] The device notifies the user of the generated insurance plan, safe driving advice, and emotional care advice. The data generated in steps 4 and 5 are used as input. Notifications are displayed on the smart glasses display and sent to the user using the vibration function. The output is that the user can check these notifications, which encourages them to select an appropriate insurance plan and drive safely.

[1653] 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.

[1654] 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.

[1655] 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.

[1656] [Fourth embodiment]

[1657] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1658] 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.

[1659] 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).

[1660] 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.

[1661] 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.

[1662] 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).

[1663] 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.

[1664] 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.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] 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."

[1670] MODE FOR CARRYING OUT THE INVENTION

[1671] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[1672] System Overview

[1673] The system's main functions are driving data collection, data analysis, insurance plan generation and notification, real-time monitoring, safe driving advice provision, and city-wide traffic data collection and analysis. Each function is explained in detail below.

[1674] Data collection methods

[1675] Device:

[1676] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. The collected data is sent to a cloud server at regular intervals.

[1677] Data analysis format

[1678] server:

[1679] The server combines the received driving data with insurance history data and analyzes it using AI algorithms, thereby identifying the user's driving patterns (frequency of sudden braking, frequency of high-speed driving, etc.) and the risk of traffic accidents in each region.

[1680] Insurance plan generation and notification forms

[1681] server:

[1682] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history.

[1683] Device:

[1684] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[1685] Real-time monitoring and safe driving advice provision

[1686] Device:

[1687] While the user is driving, driving data is sent to a server in real time.

[1688] server:

[1689] The server analyzes the received driving data in real time and detects high-risk driving situations.

[1690] Device:

[1691] If the risk is deemed high, safe driving advice (for example, "This is an area where sudden braking is common. Please be careful") will be sent to the device, allowing the user to correct their driving on the spot.

[1692] City-wide transportation data collection and analysis

[1693] server:

[1694] Driving data and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[1695] Users (municipalities and transport administrations):

[1696] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[1697] Specific examples

[1698] Insurance plan proposals

[1699] User: The user driving the car has a smartphone inside the car.

[1700] Device: The smartphone collects the user's driving data and sends it to the server.

[1701] Server: Analyzes driving data and insurance history to generate the optimal insurance plan for the user.

[1702] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[1703] Providing safe driving advice

[1704] User: While the user is driving, their smartphone collects data on sudden braking.

[1705] Server: Analyzes data in real time and detects frequent occurrences of sudden braking in specific areas.

[1706] Device: The user is notified with the advice, "Please be careful as there are many sudden brakings in this area." The user can take the advice and drive safely.

[1707] With these functions, the present invention realizes a system that provides users with the most suitable insurance plan, promotes safe driving, and also contributes to improving traffic throughout the city.

[1708] The processing flow will be explained below.

[1709] Step 1:

[1710] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) in real time.

[1711] Step 2:

[1712] Terminal: Sends collected driving data to a cloud server at regular intervals.

[1713] Step 3:

[1714] Server: The cloud server stores the received driving data and integrates it with the user's past insurance history data.

[1715] Step 4:

[1716] Server: Using AI algorithms, it analyzes driving data and insurance history data to identify the user's driving patterns (such as frequency of sudden braking and high speed driving).

[1717] Step 5:

[1718] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[1719] Step 6:

[1720] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[1721] Step 7:

[1722] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[1723] Step 8:

[1724] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[1725] Step 9:

[1726] Device: Collects driving data in real time while the user is driving and transmits it to the server.

[1727] Step 10:

[1728] Server: Monitors real-time driving data and detects high-risk driving situations (e.g., areas with frequent sudden braking or weather-related risks).

[1729] Step 11:

[1730] Server: Generates safe driving advice based on the detected risks.

[1731] Step 12:

[1732] Device: Notify the user of the generated safe driving advice (e.g., "Be careful as there are many sudden brakings in this area").

[1733] Step 13:

[1734] Server: Driving data and traffic accident data collected from users are accumulated across the city and stored as large-scale data.

[1735] Step 14:

[1736] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[1737] Step 15:

[1738] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[1739] Step 16:

[1740] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[1741] Example 1

[1742] 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."

[1743] In modern urban environments, improving driving safety and providing optimal insurance plans are important challenges. Conventional systems do not adequately collect and analyze driving data, making it difficult to provide users with appropriate insurance plans. Furthermore, they are inadequate at predicting traffic accident risks and providing safe driving advice in real time, limiting their ability to improve traffic throughout the city.

[1744] 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.

[1745] In this invention, the server includes: means for collecting driving data in real time; means for acquiring past insurance history data; means for analyzing the acquired driving data and insurance history data; means for generating an optimal insurance plan based on the analysis results; means for notifying the user of the generated insurance plan; means for monitoring the user's driving data in real time; means for assessing driving risks and providing safe driving advice; means for collecting and analyzing driving data and traffic information for the entire city; means for providing data for urban planning and traffic improvement; means for identifying driving patterns using an AI algorithm; means for detecting high-risk driving situations; and means for identifying areas with high traffic accident rates based on the analysis results. This enables the server to provide optimal insurance plans to users and provide safe driving advice in real time. This also contributes to improving traffic conditions throughout the city and reducing the risk of traffic accidents.

[1746] "Driving data" refers to information related to driving a vehicle, such as vehicle speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[1747] "Insurance history data" refers to information about insurance contracts that a user has had in the past.

[1748] "Analyzing means" refers to methods or devices used to process collected data and identify patterns and risks.

[1749] "Insurance Plan" means the terms and conditions of automobile insurance offered to a Driver.

[1750] "Real-time monitoring means" refers to a method or device for real-time monitoring and analysis of vehicle data during operation.

[1751] "Safe driving advice" refers to instructions and advice provided to the driver based on the analysis results to improve driving safety.

[1752] "Traffic information" refers to information related to traffic, such as road conditions, traffic volume, and accident occurrence status.

[1753] An "AI algorithm" is a method for analyzing data using machine learning and artificial intelligence techniques to detect patterns and make predictions.

[1754] A "driving pattern" refers to a statistical feature of data that indicates a driver's driving tendencies and characteristics.

[1755] A "high-risk driving situation" is a driving condition or situation that is prone to causing traffic accidents or other problems.

[1756] A "high traffic accident area" is a location in a specific region or road section where traffic accidents occur frequently.

[1757] The present invention is a system that integrates an automobile insurance recommendation system and a location information service to propose an optimal insurance plan to a user and promote safe driving. Hereinafter, an embodiment of the present invention will be described.

[1758] Collecting and transmitting driving data

[1759] Device: When a user drives a car, their smartphone or in-car device collects real-time driving data such as speed, distance traveled, number of sudden braking attempts, accelerator operation, etc. This data is sent to a cloud server at regular intervals via the smartphone's communication function.

[1760] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration and immediately sends the data to the server.

[1761] Analysis of driving data and insurance history

[1762] Server: The server combines the received driving data with past insurance history data and analyzes it using an AI algorithm based on a generative AI model. This analysis identifies the user's driving patterns (such as frequency of sudden braking and high speed driving) and assesses the risk of traffic accidents in each area.

[1763] How it works: The server uses a deep learning model to classify the number of sudden braking and the frequency of high-speed driving as categorical targets. For example, the server clusters the user's sudden braking data and categorizes them into driving styles such as "safe," "cautious," and "dangerous."

[1764] Generate optimal insurance plans

[1765] Server: Based on the analysis results, the server generates the optimal insurance plan for the user. This plan takes into account the user's driving style and past insurance history.

[1766] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for a user who frequently brakes suddenly.

[1767] Insurance plan notification

[1768] Device: The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[1769] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, a message like "A new insurance plan has been proposed. Please review the details" will appear on the screen.

[1770] Real-time driving data monitoring

[1771] Device: While the user is driving, the smartphone continuously transmits driving data to the server.

[1772] How it works: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server. For example, if a user suddenly accelerates 100 meters in one minute, the data is sent to the server in real time.

[1773] Risk Advice

[1774] Server: The server analyzes driving data in real time and detects high-risk driving situations.

[1775] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in specific areas. For example, the server detects areas where sudden braking occurs frequently and flags them as "high risk."

[1776] Safe driving advice notifications

[1777] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[1778] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when a user enters an area where sudden braking is common, the user will be notified immediately.

[1779] Collecting and analyzing city-wide traffic data

[1780] Server: Centrally manages and analyzes driving data and accident information collected from each user, thereby identifying traffic congestion locations and areas prone to accidents.

[1781] How it works: The server uses a large database to analyze traffic data for the entire city, integrating it with map information. For example, it can detect that accidents frequently occur at a particular intersection and report that area to the local government.

[1782] Providing analysis results

[1783] Users (local governments and transportation administration agencies): The server's analysis results will be provided for urban planning and transportation infrastructure improvement, enabling local governments and transportation administration agencies to make effective improvements to transportation infrastructure.

[1784] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[1785] Example prompt: "Design a system that analyzes users' driving data in real time and provides advice on high-risk areas."

[1786] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1787] Step 1:

[1788] Input: The user starts driving the car.

[1789] Devices: Smartphones and in-car devices collect real-time driving data such as speed, distance traveled, number of hard brakes, and accelerator operation.

[1790] How it works: The smartphone uses the GPS sensor to record the current speed, and the acceleration sensor to collect additional data on sudden braking and acceleration. For example, when a user suddenly brakes, the smartphone detects the high negative acceleration.

[1791] Output: Collected driving data.

[1792] Step 2:

[1793] Input: Driving data collected in step 1.

[1794] Terminal: Sends collected driving data to the cloud server at regular intervals.

[1795] Specific operation: Driving data collected by a smartphone is sent to a cloud server at regular intervals, for example, sending data every minute in batches.

[1796] Output: Driving data sent to cloud server.

[1797] Step 3:

[1798] Input: Driving data sent to the cloud server in step 2 and existing insurance history data.

[1799] Server: Integrates received driving data and past insurance history data and analyzes them using AI algorithms based on generative AI models.

[1800] How it works: The server uses a deep learning model to classify the number of sudden braking incidents and the frequency of high-speed driving as categorical targets. For example, it clusters data on sudden braking and categorizes driving styles as "safe," "cautious," or "dangerous."

[1801] Output: Driving patterns and traffic accident risk assessment as analysis results.

[1802] Step 4:

[1803] Input: Analysis results obtained in step 3.

[1804] Server: Based on the analysis results, generate the optimal insurance plan for the user.

[1805] Specific operation: The server uses the user's driving data and insurance history data to generate a risk-based insurance plan. For example, it creates a risk compensation plan for users who frequently brake suddenly.

[1806] Output: Optimal insurance plan.

[1807] Step 5:

[1808] Input: The insurance plan generated in step 4.

[1809] Terminal: The generated insurance plan is sent to the user's smartphone.

[1810] What it does: The smartphone receives a push notification and displays it on the screen, offering the user new insurance plan options. For example, it might say, "A new insurance plan has been proposed. Please review the details."

[1811] Output: The user receives the insurance plan.

[1812] Step 6:

[1813] Input: Driving data collected continuously while the user is driving.

[1814] Terminal: Sends driving data to the server in real time.

[1815] Specific operation: The smartphone captures data from the accelerometer and GPS at regular intervals and sends it to the server, for example, every minute in real time.

[1816] Output: Real-time driving data.

[1817] Step 7:

[1818] Input: Real-time driving data sent in step 6.

[1819] Server: Analyzes driving data in real time and detects high-risk driving situations.

[1820] Specific operation: The server analyzes data on sudden braking and sharp turns to detect frequent risks in a specific area. For example, it detects locations where sudden braking frequently occurs.

[1821] Output: Risk notification data.

[1822] Step 8:

[1823] Input: Risk notification data obtained in step 7.

[1824] Device: If a high-risk driving situation is detected, safe driving advice will be sent to your smartphone.

[1825] Specific operation: The smartphone will send a push notification to warn the user, saying, "Please be careful as there are many sudden braking incidents in this area." For example, when entering an area where sudden braking is common, the user will be notified immediately.

[1826] Output: Real-time safe driving advice.

[1827] Step 9:

[1828] Input: Driving data and traffic information collected from each user.

[1829] Server: Centrally manages and analyzes driving data and traffic information for the entire city.

[1830] How it works: The server uses a large database to analyze traffic congestion and accident-prone areas by integrating them with map information. For example, it detects that accidents frequently occur at certain intersections.

[1831] Output: Analysis results for transportation infrastructure improvements.

[1832] Step 10:

[1833] Input: Analysis results obtained in step 9.

[1834] Users (local governments and transportation administration agencies): Provide analysis results for urban planning and transportation infrastructure improvement.

[1835] Specific operation: The server generates the analysis results in the form of a report and shares them on the local government's official website or dedicated portal. For example, a proposal to change the traffic light settings based on information on intersection X where accidents frequently occur is made.

[1836] Output: Proposed data for transportation improvements.

[1837] (Application example 1)

[1838] 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."

[1839] Conventional automobile insurance systems can only offer fixed insurance plans, making it difficult to propose optimal plans based on individual drivers' driving styles and driving data. They also lack real-time advice to promote safe driving, and there has been insufficient centralized management of driving data for the entire city to utilize for effective urban planning and traffic improvement. To address these issues, this invention provides a system that collects and analyzes driving data from autonomous vehicles in real time, proposes optimal insurance plans, and provides safe driving advice.

[1840] 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.

[1841] In this invention, the server includes a means for collecting driving data in real time, a means for acquiring past insurance history data, and a means for analyzing the acquired driving data and insurance history data. This makes it possible to analyze the collected driving data and insurance history data and provide optimal insurance plans for individual drivers. Furthermore, the monitoring means and evaluation means can evaluate driving risks in real time and provide safe driving advice, thereby promoting safe driving. Furthermore, by aggregating and analyzing driving data and traffic information for an entire city, it is possible to provide data for traffic improvement and urban planning.

[1842] "Driving data" refers to information related to the driving of an autonomous vehicle, such as its speed, distance traveled, accelerator operation, and braking operation.

[1843] "Insurance history data" refers to information about the contents of insurance contracts that a user has had up to now, as well as past accident and claim history.

[1844] "Analysis results" are information showing statistics and trends generated by analyzing collected driving data and insurance history data using AI and algorithms.

[1845] An "insurance plan" is a proposal of insurance products and contract terms optimized based on the user's driving data and insurance history data.

[1846] "Notifications" are means of informing users of generated insurance plans and safe driving advice, including push notifications to smartphones and in-car devices and emails.

[1847] "Monitoring" refers to the process of collecting and analyzing driving data in real time while the user is driving.

[1848] "Evaluation" is the process of determining driving risks based on collected driving data and providing safe driving advice.

[1849] "Risk" refers to accidents or dangerous driving situations that may occur while driving.

[1850] "Safe driving advice" refers to driving improvement suggestions and warnings provided to users based on real-time driving data.

[1851] An "autonomous vehicle" is a vehicle that operates autonomously using artificial intelligence and sensors.

[1852] "Urban planning" is a plan aimed at developing the entire city and improving transportation infrastructure.

[1853] "Traffic improvement" refers to the review of policies and infrastructure to smooth traffic flow and reduce accidents.

[1854] MODE FOR CARRYING OUT THE INVENTION

[1855] This invention is a system that proposes optimal insurance plans based on driving data from autonomous vehicles and promotes safe driving. This system includes functions such as collecting driving data, analyzing the data, generating and notifying insurance plans, real-time monitoring, and providing safe driving advice.

[1856] System configuration

[1857] 1. Data Collection

[1858] Using built-in sensors in autonomous vehicles and smartphones, driving data such as speed, distance traveled, accelerator operation, and brake operation is collected in real time, and this data is sent to a cloud server at regular intervals.

[1859] 2. Data Analysis

[1860] The server is built on the cloud and collects and analyzes the collected driving data and the user's past insurance history data. It processes the data using AI algorithms (e.g., TensorFlow and Scikit-learn) to identify driving patterns.

[1861] 3. Insurance plan generation and notification

[1862] Based on the analysis results, the system generates an optimal insurance plan for the user, taking into account the user's driving style and past insurance history. The generated insurance plan is then notified to the user via push notification or email on their smartphone or in-car display.

[1863] 4. Real-time monitoring and safe driving advice

[1864] The server analyzes driving data in real time to detect high-risk driving situations. If a high risk is detected, the user is notified in real time with advice such as "Please be careful as there are many sudden brakes in this area." This allows the user to correct their driving on the spot. The Geopy library is used to analyze location information and identify risk zones.

[1865] 5. City-wide traffic data analysis

[1866] Driving data and traffic information from across the city will be collected and integrated and analyzed on a cloud server. The analysis results will be provided to local governments and transportation administration agencies, who will be able to identify areas with high traffic congestion and accident rates and use them to improve these areas.

[1867] Specific examples

[1868] 1. Data collection and transmission:

[1869] Data such as the speed of the self-driving vehicle and the number of times it brakes suddenly is collected and sent to a cloud server.

[1870] 2. Data Analysis:

[1871] The collected data is analyzed using an AI model to identify driving patterns and risk areas.

[1872] 3. Insurance plan generation and notification:

[1873] The system generates and notifies the user of the optimal insurance plan based on their driving data and past insurance history.

[1874] 4. Providing safe driving advice:

[1875] It analyzes driving data in real time, and if a danger is detected, it immediately notifies the user with advice.

[1876] Prompt Sentence Examples

[1877] Driving data collection:

[1878] It collects user driving data (speed, number of sudden braking) and sends it to the server in the following format:

[1879] Speed: 60km / h

[1880] Hard braking: 3 times

[1881] Insurance plan suggestions:

[1882] It analyzes the user's driving data and insurance history and proposes insurance plans in the following format:

[1883] (User information)

[1884] Driving data: Speed: '60km / h', Hard braking: '3 times'

[1885] Insurance history: 'No accidents in the last three years'

[1886] (Proposal details)

[1887] Best Insurance Plan: 'Safe Driving Discount Plan', 'Discount Rate: 20%'

[1888] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1889] Step 1:

[1890] Data collection

[1891] The device (built-in sensors in the autonomous vehicle or a smartphone) collects driving data such as speed, distance traveled, accelerator operation, and braking operation in real time. This data is used to record the vehicle's movements in detail while driving. The collected data is temporarily stored in the device.

[1892] Input: Driving data from sensors and smartphones

[1893] Output: Driving data stored in the device

[1894] Step 2:

[1895] Data transmission

[1896] The device periodically transmits the collected driving data to a cloud server. Data transmission requires an internet connection and is encrypted using a secure communication protocol.

[1897] Input: Driving data stored in the device

[1898] Output: Driving data sent to the cloud server

[1899] Step 3:

[1900] Data analysis

[1901] The server combines the received driving data with the user's past insurance history data and analyzes it using AI algorithms (e.g., TensorFlow and Scikit-learn). This analysis identifies driving patterns (such as frequency of sudden braking and high speed driving) and traffic accident risks by region.

[1902] Input: Driving data and insurance history data sent to the cloud server

[1903] Output: Analysis results (driving patterns, traffic accident risk)

[1904] Step 4:

[1905] Generate an insurance plan

[1906] The server then generates the optimal insurance plan for the user based on the analysis results, taking into account the user's driving style and past insurance history. The generated insurance plan is saved in a dedicated format.

[1907] Input: Analysis results (driving patterns, traffic accident risk)

[1908] Output: Generated insurance plan

[1909] Step 5:

[1910] Insurance plan notification

[1911] The server then notifies the user of the generated insurance plan via a smartphone, in-car display, push notification, or email.

[1912] Input: Generated insurance plan

[1913] Output: The insurance plan that the user was notified of

[1914] Step 6:

[1915] Real-time monitoring

[1916] The device monitors driving data in real time and sends it to a server, allowing the driver to constantly monitor their driving situation.

[1917] Input: Data during driving

[1918] Output: Real-time data sent to the server

[1919] Step 7:

[1920] Risk assessment and advice provision

[1921] The server analyzes the driving data received in real time and detects high-risk driving situations. Appropriate safe driving advice is generated for the detected risks and sent to the device, which then notifies the user.

[1922] Input: Driving data received in real time

[1923] Output: Risk assessment results, safe driving advice

[1924] Step 8:

[1925] City-wide traffic data analysis

[1926] The server will integrate and analyze driving data and traffic information collected from across the city, and the analysis results will be provided to local governments and transportation administration agencies to help with urban planning and transportation infrastructure improvements.

[1927] Input: Driving data and traffic information collected from across the city

[1928] Output: Analysis results for urban planning and transportation improvement

[1929] 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.

[1930] MODE FOR CARRYING OUT THE INVENTION

[1931] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described.

[1932] System Overview

[1933] The system has the functions of collecting driving data, analyzing data, generating and notifying insurance plans, real-time monitoring, providing safe driving advice, recognizing user emotions using an emotion engine, and collecting and analyzing traffic data for the entire city. Each function is explained in detail below.

[1934] Data collection methods

[1935] Device:

[1936] The user's smartphone or in-car device collects various driving data (speed, distance traveled, number of sudden braking attempts, accelerator operation, etc.) in real time while driving. In addition, cameras, microphones, and various sensors are used to analyze the user's facial expressions and voice, and emotional data is also collected. The collected data is sent to a cloud server at regular intervals.

[1937] Data analysis format

[1938] server:

[1939] The server integrates the received driving data, emotional data, and insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[1940] Insurance plan generation and notification forms

[1941] server:

[1942] Based on the analysis results, the system generates an optimal insurance plan for the user, which takes into account the user's driving style, past insurance history, and even emotional state.

[1943] Device:

[1944] The generated insurance plan will be sent to the user's smartphone or other device via push notification, email, or the notification function of the dedicated app.

[1945] Real-time monitoring and safe driving advice provision

[1946] Device:

[1947] While the user is driving, driving data and emotional data are continuously collected in real time and sent to the server.

[1948] server:

[1949] The server analyzes the received driving and emotional data in real time to detect high-risk driving situations and the user's stress level.

[1950] Device:

[1951] If the risk is deemed high, the device will provide safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax"), allowing the user to correct their driving on the spot and regulate their emotional state.

[1952] City-wide transportation data collection and analysis

[1953] server:

[1954] Driving data, emotion data, and accident information collected from across the city will be centrally managed and analyzed, allowing for the identification of traffic congestion and accident-prone areas.

[1955] Users (municipalities and transport administrations):

[1956] The analysis results will be provided to local governments and transportation administration agencies for urban planning and transportation infrastructure improvement, enabling effective improvements to transportation infrastructure.

[1957] Specific examples

[1958] Insurance plan proposals

[1959] User: The user driving the car has a smartphone inside the car.

[1960] Device: The smartphone collects the user's driving data and emotional data and sends it to the server.

[1961] Server: Analyzes driving data, emotional data, and insurance history, and generates the optimal insurance plan taking into account the user's stress level, etc.

[1962] Device: The generated insurance plan is sent to the user's smartphone, where the user can review the plans and choose the one that best suits them.

[1963] Providing safe driving advice

[1964] User: While the user is driving, the smartphone collects data on sudden braking, facial expressions, and changes in voice.

[1965] Server: Analyzes data in real time to detect frequent sudden braking in a specific area, and also detects when the user is feeling stressed.

[1966] Device: The device notifies the user, "Please be careful as there are many sudden brakings in this area," along with advice on both safe driving and emotional care, such as, "We recommend taking a deep breath and relaxing." By receiving this advice, the user can continue driving safely and also take care of their emotions.

[1967] With these functions, the present invention realizes a system that provides users with optimal insurance plans, promotes safe driving and emotional care, and also contributes to improving traffic throughout the city.

[1968] The processing flow will be explained below.

[1969] Processing steps of a system that combines emotion engines

[1970] Step 1:

[1971] Devices: Users' smartphones and in-car devices collect driving data (speed, braking frequency, distance traveled, etc.) and emotional data (facial expressions, tone of voice, etc.) in real time.

[1972] Step 2:

[1973] Terminal: Collected driving data and emotion data are sent to the cloud server at regular intervals.

[1974] Step 3:

[1975] Server: The cloud server stores the received driving data and emotion data and integrates it with the user's past insurance history data.

[1976] Step 4:

[1977] Server: Using AI algorithms, it analyzes driving data, emotional data, and insurance history data to identify the user's driving patterns (frequency of sudden braking, frequency of high speed driving, etc.) and emotional state.

[1978] Step 5:

[1979] Server: Uses location information services to analyze traffic accident risks and weather information in the user's driving area.

[1980] Step 6:

[1981] Server: Based on the analysis results, it generates the optimal insurance plan for the user, taking into account the user's emotional state.

[1982] Step 7:

[1983] Server: Selects the generated insurance plan and determines the content to be notified to the user.

[1984] Step 8:

[1985] Device: Notifies users of insurance plan proposals via push notifications, emails, and dedicated app notifications.

[1986] Step 9:

[1987] Device: While the user is driving, it continues to collect driving data and emotional data in real time and transmits it to the server.

[1988] Step 10:

[1989] Server: Monitors real-time driving and emotional data to assess risky driving situations and the user's stress level.

[1990] Step 11:

[1991] Server: Based on the detected risks, it generates safe driving advice (e.g., "Please be careful as there are many sudden brakes in this area") and emotional care advice (e.g., "We recommend that you take a deep breath and relax").

[1992] Step 12:

[1993] On the device: The generated advice is notified to the user via a pop-up notification and / or a sound notification.

[1994] Step 13:

[1995] Server: Driving data, emotion data, and traffic accident data collected from users across the city are accumulated and stored as large-scale data.

[1996] Step 14:

[1997] Server: Analyzes city-wide data to identify traffic patterns and high-risk areas.

[1998] Step 15:

[1999] Server: The analysis results are compiled into a report and provided to local governments and transportation administration agencies.

[2000] Step 16:

[2001] Users (local governments and transportation administration agencies): Based on the reports, they consider and implement measures to improve urban planning and transportation infrastructure.

[2002] Example 2

[2003] 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."

[2004] Conventional automobile insurance systems propose insurance plans based on driving data and past insurance history, but because they do not take into account the user's emotional state, they have the problem of not being able to provide the optimal plan for each individual driver. They also lack real-time driving advice and emotional care, which means users' safe driving is not adequately supported. Furthermore, because traffic data for the entire city is not collected or analyzed, they are unable to provide useful information for urban planning and transportation infrastructure improvements.

[2005] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for collecting driving data and emotion data in real time, a means for acquiring past insurance history data and emotion history data, and a means for integrating and analyzing the acquired driving data, emotion data, and insurance history data. This makes it possible to accurately grasp the user's driving patterns and emotional state and provide an optimal insurance plan and real-time safe driving advice and emotional care advice. It also becomes possible to analyze driving data, emotion data, and traffic accident information for an entire city and provide useful data for urban planning and transportation infrastructure improvement.

[2006] "Driving data" refers to information collected while the vehicle is traveling, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[2007] "Emotional data" is information that indicates the emotional state of a user, obtained from facial expressions, tone of voice, body movements, etc.

[2008] "Insurance history data" refers to data such as the user's past insurance contract details, accident history, and claim information.

[2009] "Emotion history data" is data that indicates the fluctuations in the user's past emotional state and the results of their analysis.

[2010] "Means for integrating and analyzing" refers to means for combining and analyzing various acquired data to identify the user's driving patterns, emotional state, and traffic risks.

[2011] "Driving patterns" refers to the analysis results of a user's driving style, specific behaviors, habits, etc.

[2012] The "optimal insurance plan" is the most appropriate insurance contract plan proposed to each individual user based on the user's driving data, emotional data, and insurance history data.

[2013] "Real-time monitoring means" refers to a method for continuously collecting data during operation and analyzing it on the spot to always obtain the latest information.

[2014] "Safe driving advice" means providing the user with specific instructions and suggestions for safe driving that are tailored to the driving conditions at the time.

[2015] "Emotional care advice" refers to providing specific instructions or suggestions for relaxation and stress reduction based on the user's emotional state.

[2016] "Data for urban planning and transportation infrastructure improvement" refers to information that is useful for developing plans for traffic management and infrastructure improvement, such as analyzing driving data, emotion data, and traffic accident information from across the city.

[2017] The present invention is a system that combines an automobile insurance recommendation system and a location information service with an emotion engine that recognizes user emotions. Hereinafter, an embodiment of the present invention will be described in detail.

[2018] System Overview

[2019] The system collects driving and emotional data, proposes insurance plans, and provides real-time advice on safe driving and emotional care. It also collects and analyzes traffic data from across the city to contribute to traffic improvements. Its main components include the user's smartphone or in-car device, a cloud server, a generative AI model, and an emotion engine.

[2020] Hardware and software used

[2021] Hardware:

[2022] Smartphones (e.g. iPhone, Android devices)

[2023] In-car devices (e.g. OBD-II adapter)

[2024] software:

[2025] Cloud server (e.g. AWS, Google Cloud)

[2026] AI algorithms (e.g. TensorFlow, PyTorch)

[2027] Emotion recognition software (e.g., Microsoft Azure Emotion API)

[2028] Data collection

[2029] Device:

[2030] As soon as the vehicle's engine starts, the user's smartphone or in-car device collects driving data such as speed, distance traveled, number of sudden braking attempts, and accelerator operation. The smartphone's built-in camera, microphone, and acceleration sensor are also used to analyze the user's facial expressions and voice, collecting emotional data. This data is then sent to a cloud server at regular intervals.

[2031] Example: A smartphone camera captures facial expressions and analyzes them with emotion recognition software, while a microphone detects the tone of the user's voice and passes the data to an emotion engine.

[2032] Data analysis

[2033] server:

[2034] The cloud server integrates driving data, emotional data, and past insurance history data and analyzes them using AI algorithms to identify the user's driving patterns, emotional state, and traffic accident risk in the area where they drive.

[2035] Example: The server uses an AI model to analyze driving and emotional data to identify a user's tendency to brake frequently and their high stress levels.

[2036] Insurance plan generation and notification

[2037] server:

[2038] The analysis results are used to generate a personalized insurance plan that takes into account your driving style, past insurance history, and emotional state.

[2039] Device:

[2040] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[2041] Examples:

[2042] Prompt: "The user's driving style involves frequent sudden braking, and their past insurance history has categorized them as a high-risk driver. Their emotional data has revealed a state of stress. Please recommend the most appropriate insurance plan."

[2043] Based on the results of the server's analysis, the optimal insurance plan will be notified to the smartphone.

[2044] Real-time monitoring and safe driving advice

[2045] Device:

[2046] Driving data and emotional data are continuously collected while driving and sent to the server.

[2047] server:

[2048] It analyzes data in real time to detect risky driving and the user's stress level.

[2049] Device:

[2050] If the risk is deemed high, the system will notify the user in real time of safe driving advice and emotional care advice.

[2051] Examples:

[2052] Prompt: "The user frequently brakes suddenly in a specific area, and emotional data indicates high stress. Please provide appropriate safe driving advice and emotional management advice."

[2053] The server identifies areas where sudden braking occurs frequently and sends a message to the driver's smartphone saying, "This area is prone to sudden braking, so please be careful." At the same time, it provides emotional care advice such as, "We recommend that you take a deep breath and relax."

[2054] Analysis and provision of city-wide traffic data

[2055] server:

[2056] By centrally managing and analyzing driving data, emotion data, and traffic accident information from across the city, we can identify areas where traffic congestion occurs and where accidents are frequent.

[2057] Users (municipalities and transport administrations):

[2058] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[2059] Example: A server analyzes city-wide data on a weekly basis to identify areas with high traffic congestion, and provides this data to a dedicated portal for the local government.

[2060] This allows the present invention to provide users with optimal insurance plans, promote safe driving and emotional care, and contribute to improving traffic throughout the city.

[2061] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2062] Step 1: Start collecting data

[2063] Device:

[2064] As soon as the vehicle engine starts, the smartphone or in-car device collects driving data (speed, distance traveled, number of hard braking attempts, accelerator operation, etc.) and emotional data (facial expressions, tone of voice, body movements, etc.) Once collected, this data is stored in a buffer at regular intervals.

[2065] input:

[2066] The vehicle engine starts and the terminal begins operation.

[2067] output:

[2068] The collected driving data and emotion data are stored in a buffer.

[2069] Specific behavior:

[2070] The smartphone camera captures the user's facial expressions, the microphone detects the tone of their voice, and sensors record driving data.

[2071] Step 2: Send data to the cloud server

[2072] Device:

[2073] The collected data is compressed at regular intervals and sent to a cloud server using HTTPS.

[2074] input:

[2075] Driving and emotional data collected.

[2076] output:

[2077] The compressed data is sent to a cloud server.

[2078] Specific behavior:

[2079] The device compresses the data and sends it to the cloud server using a secure communication protocol.

[2080] Step 3: Data synthesis and analysis

[2081] server:

[2082] The cloud server combines the received driving data, emotional data, and past insurance history data and analyzes them using AI algorithms (e.g., TensorFlow models). The analysis identifies the user's driving patterns, emotional state, and traffic accident risk in the driving area.

[2083] input:

[2084] Driving data, emotional data, past insurance history data.

[2085] output:

[2086] Analysis results on users' driving patterns, emotional state, and traffic accident risk.

[2087] Specific behavior:

[2088] The server stores the data in a database and then runs an AI model to analyze various data.

[2089] Step 4: Generate your insurance plan

[2090] server:

[2091] Based on the analysis results, the system generates an optimal insurance plan that reflects the user's driving style, past insurance history, and emotional state.

[2092] input:

[2093] Analysis results, driving patterns, emotional state, and traffic accident risk data.

[2094] output:

[2095] The best insurance plan for you.

[2096] Specific behavior:

[2097] Based on the analysis results generated by the AI ​​model, the optimal insurance plan for the user is calculated and stored in a database.

[2098] Step 5: Insurance plan notification

[2099] Device:

[2100] The generated insurance plan will be sent to the user's smartphone via push notification, email, or the notification function of a dedicated app.

[2101] input:

[2102] The best insurance plan.

[2103] output:

[2104] A notification message will appear on the user's smartphone.

[2105] Specific behavior:

[2106] Insurance plan data is sent from the cloud server to the device via a notification API, and the UI component on the device displays the plan information.

[2107] Step 6: Real-time monitoring and safe driving advice

[2108] Device:

[2109] While driving, driving data and emotional data continue to be collected and sent to the server.

[2110] server:

[2111] The data transmitted in real time is analyzed to detect risky driving and the user's stress level.

[2112] Device:

[2113] If the risk is deemed high, the system will provide real-time safe driving advice (e.g., "Be careful as there are many sudden brakes in this area") or advice based on emotional state (e.g., "We recommend you take a deep breath and relax").

[2114] input:

[2115] Driving data, emotional data.

[2116] output:

[2117] Real-time driving advice, emotional care advice.

[2118] Specific behavior:

[2119] The server receives the data and simultaneously performs AI analysis, generating safe driving advice and emotional care advice based on the analysis results, which are then immediately sent to the device, which then displays a notification.

[2120] Step 7: Analyze and provide city-wide traffic data

[2121] server:

[2122] By centrally managing and analyzing driving data, emotion data, and accident information from across the city, traffic congestion locations and areas prone to accidents can be identified.

[2123] Users (municipalities and transport administrations):

[2124] These analysis results will be provided to local governments and transportation administration agencies to help improve transportation infrastructure and develop new urban plans.

[2125] input:

[2126] Driving data, emotional data, traffic accident information.

[2127] output:

[2128] Analysis results on traffic congestion locations and areas with high accident rates.

[2129] Specific behavior:

[2130] The server analyzes the data weekly or monthly, automatically generating reports for urban planning and transportation infrastructure improvements, and provides them to local governments via a dedicated portal or by email.

[2131] Through these steps, the system can provide users with the most suitable insurance plans, promote safe driving and emotional care, and also contribute to improving traffic throughout the city.

[2132] (Application example 2)

[2133] 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."

[2134] Conventional automobile insurance systems generate insurance plans based solely on the user's driving data, but lack the ability to provide information on the user's emotional state or real-time safe driving advice. Furthermore, there is an insufficient mechanism for collecting traffic information and driving data from across a city and utilizing it for efficient urban planning and traffic improvement. Furthermore, there is no method for providing appropriate advice in real time when a user's emotional state fluctuates while driving.

[2135] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data in real time, means for acquiring past insurance history data, and means for analyzing the acquired driving data, insurance history data, and emotion data. This makes it possible to monitor the user's driving data and emotion data in real time, provide the most suitable insurance plan, and provide advice on safe driving and emotional care. In addition, traffic information for the entire city can be collected, enabling efficient urban planning and traffic improvements.

[2136] "Driving data" refers to various data related to vehicle driving, such as speed, distance traveled, number of sudden braking attempts, and accelerator operation.

[2137] "Emotional data" refers to data about the user's emotional state analyzed from facial expressions, voice, etc.

[2138] "Insurance history data" refers to information about insurance policies that a user has previously signed up for.

[2139] "Analysis" refers to the act of analyzing collected driving data and emotional data using AI algorithms and other methods to derive meaningful results.

[2140] An "insurance plan" refers to a plan that combines the contents and conditions of vehicle insurance.

[2141] "Notification" refers to the action of informing users of analysis results and insurance plans.

[2142] "Monitoring" refers to the act of monitoring a user's driving data and status in real time.

[2143] "Safe driving advice" refers to instructions or suggestions provided to the user while driving to promote safe driving.

[2144] "Emotional care advice" refers to advice to reduce stress and promote relaxation based on the user's emotional state.

[2145] "City-wide driving data" refers to various driving information collected within a specific city.

[2146] "Traffic information" refers to information related to road traffic, such as traffic volume, congestion, and accidents.

[2147] "Urban planning" refers to plans aimed at developing cities and improving transportation infrastructure.

[2148] "Transportation improvement" refers to policies and practices that aim to achieve more efficient and safer transportation conditions.

[2149] In this invention, the following specific embodiments are implemented as a system for providing insurance plans and safe driving advice while monitoring the emotional state of an autonomous vehicle and its driver.

[2150] System Overview

[2151] Device:

[2152] The smart glasses worn by the driver are equipped with a camera, microphone, and various sensors to collect driving data and user emotional data in real time, including facial expression and voice analysis.

[2153] server:

[2154] The server integrates the collected driving data, emotional data, and past insurance history data and analyzes them using AI algorithms. As a result of the analysis, it generates the optimal insurance plan and notifies the user. It also provides safe driving advice and emotional care advice in real time.

[2155] Hardware and software used:

[2156] Hardware:

[2157] Smart glasses (camera, microphone, various sensors)

[2158] Cloud server (data analysis and AI algorithm execution)

[2159] software:

[2160] EmotionAI (emotion recognition software)

[2161] DataCollector (driving data collection system)

[2162] Notification System

[2163] InsuranceEngine (insurance plan generation engine)

[2164] Specific operations of each method

[2165] Driving and emotion data collection:

[2166] Device: The smart glasses collect driving data such as vehicle speed, mileage, and number of sudden braking. At the same time, they use a camera and microphone to analyze facial expressions and voice to collect driver emotional data.

[2167] Real-time data transmission:

[2168] Terminal: Collected driving data and emotion data are sent to a cloud server at regular intervals.

[2169] Data Analysis and Notification:

[2170] Server: Analyzes the received driving data, emotion data, and insurance history data using AI algorithms to generate the optimal insurance plan.

[2171] Server: Evaluates risks and the user's stress state while driving, and generates advice on safe driving and emotional care in real time.

[2172] Terminal: The generated insurance plan and advice will be displayed on the smart glasses display.

[2173] Specific example explanation

[2174] 1. Insurance plan proposal

[2175] While the user is driving, the smart glasses collect driving data such as speed, distance traveled, and number of sudden braking attempts, and use a camera and microphone to analyze facial expressions and voice changes to collect emotional data.

[2176] The server consolidates and analyzes the data to generate the optimal insurance plan.

[2177] The generated insurance plan will be pushed to the smart glasses, allowing the user to check the plan on the spot.

[2178] 2. Providing safe driving advice

[2179] When a user suddenly brakes, the smart glasses detect driving data and analyze emotional data using a camera and microphone.

[2180] The server analyzes the data in real time and generates notifications such as "This is an area with a lot of sudden braking, please be careful" and "We recommend you take a deep breath and relax."

[2181] These notifications are sent to smart glasses, allowing users to continue driving safely and regulate their emotions.

[2182] Prompt Sentence Examples

[2183] Prompt sentence to input to the generative AI model:

[2184] "When a user suddenly brakes while driving, analyze their emotional and driving data and simultaneously provide advice on safe driving and emotional care."

[2185] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2186] Step 1:

[2187] The device uses smart glasses to collect driving and emotional data in real time. Driving data includes vehicle speed, mileage, and number of sudden braking incidents, while emotional data is the result of analyzing facial expressions and voice using a camera and microphone. Data is acquired as input from the vehicle's sensors and the user's emotional sensors, and is temporarily stored in internal memory. Formatted driving and emotional data is obtained as output.

[2188] Step 2:

[2189] The device transmits the collected driving data and emotion data to the cloud server at regular intervals. The driving data and emotion data collected in step 1 are used as input and are transmitted to the cloud server using the data communication module. As output, the device confirms that the data is transmitted to the cloud server and records it in a log appropriately.

[2190] Step 3:

[2191] The server analyzes the received driving and emotion data. It uses the data sent in step 2 as input and analyzes each data using EmotionAI and DataCollector. It identifies driving patterns and emotional states and assesses their respective risks. The results of this analysis are output and form the basis for generating insurance plans and real-time advice.

[2192] Step 4:

[2193] The server generates the optimal insurance plan based on the analysis results. The analysis results from step 3 and past insurance history data are used as input. InsuranceEngine uses this data to generate an insurance plan suitable for the user. The generated insurance plan is output and stored as a dataset to be notified to the user.

[2194] Step 5:

[2195] The server generates safe driving advice and emotional care advice based on the analysis results. It uses the analysis results from step 3 as input and generates advice based on the risk assessment results. The NotificationSystem creates these advice messages and saves them as messages to be sent to the user as output.

[2196] Step 6:

[2197] The device notifies the user of the generated insurance plan, safe driving advice, and emotional care advice. The data generated in steps 4 and 5 are used as input. Notifications are displayed on the smart glasses display and sent to the user using the vibration function. The output is that the user can check these notifications, which encourages them to select an appropriate insurance plan and drive safely.

[2198] 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.

[2199] 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.

[2200] 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.

[2201] 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.

[2202] 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.

[2203] 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.

[2204] 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).

[2205] 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.

[2206] 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."

[2207] 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.

[2208] 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).

[2209] 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 distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2210] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2211] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2212] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2213] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2214] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2215] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2216] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2217] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2218] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2219] The following is further disclosed regarding the above embodiment.

[2220] (Claim 1)

[2221] a means for collecting driving data in real time;

[2222] a means for obtaining historical insurance data;

[2223] means for analyzing the acquired driving data and insurance history data;

[2224] A means for generating an optimal insurance plan based on the analysis results;

[2225] a means for notifying the user of the generated insurance plan;

[2226] A means of monitoring the user's driving data in real time;

[2227] A means of assessing driving risks and providing safe driving advice;

[2228] A means of collecting and analyzing driving data and traffic information across the city,

[2229] a means of providing data for urban planning and transportation improvements;

[2230] A system including:

[2231] (Claim 2)

[2232] The system according to claim 1, which predicts the risk of traffic accidents in the area where the user drives ...

Claims

1. a means for collecting driving data in real time; a means for obtaining historical insurance data; means for analyzing the acquired driving data and insurance history data; A means for generating an optimal insurance plan based on the analysis results; a means for notifying the user of the generated insurance plan; A means of monitoring the user's driving data in real time; A means of assessing driving risks and providing safe driving advice; A means of collecting and analyzing driving data and traffic information across the city, a means of providing data for urban planning and transportation improvements; A system including:

2. 2. The system according to claim 1, which predicts the risk of traffic accidents in the area where the user drives based on the analysis results of the driving data and provides safe driving advice in real time.

3. 3. The system according to claim 1, wherein the system selects and notifies the user of an optimal plan from among the insurance plans generated based on the analysis results.

Images