system

Abstract

We provide the system. [Solution] A device for acquiring operating data, A device that analyzes acquired operating data and performs operational evaluations, A device that adjusts fare information based on operational evaluations, A device that notifies the adjusted fare information, A system that includes this.

Description

Technical Field

[0004] , , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional insurance system, it is difficult to evaluate the individual driving habits and situations of drivers, and insurance premiums are calculated based on a uniform standard, so there is a problem that incentives for promoting safe driving are not provided. In addition, since there is a lack of a mechanism for rewarding safe driving, there is a problem that drivers cannot obtain an opportunity to improve their own driving behavior and improve traffic safety.

Means for Solving the Problems

[0005] This invention provides a device that acquires driving data, analyzes it, and performs a driving evaluation, thereby enabling accurate evaluation of the driver's driving behavior. Furthermore, by adjusting toll information based on this driving evaluation, it provides an economic incentive for safe driving. The adjusted toll information is communicated to the user through a notification device, allowing the driver to obtain direct information to improve their driving behavior. Such a system promotes safe driving and improves the driver's autonomous driving skills.

[0006] "Driving data" refers to information recorded while a driver is driving, including details about driving behavior such as acceleration, speed, and location information.

[0007] "Device" refers to a collection of mechanical or electronic mechanisms for performing a specific function, and in this invention, it refers to hardware or software for collecting, analyzing, adjusting, and notifying driving data.

[0008] "Driving evaluation" refers to the process of evaluating safe driving by quantitatively or qualitatively analyzing a driver's driving style and habits based on collected driving data.

[0009] "Fee information" refers to financial information related to insurance premiums, including the amount of the premium and the discount rate, which are adjusted based on the driver's driving behavior.

[0010] "Notifications" refer to means of communication that convey adjusted fare information and driving evaluation results to drivers, and are conducted via email, SMS, mobile applications, etc. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, the terms used in the following description will be explained.

[0014] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0018] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] As shown in Figure 1, the 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.

[0022] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0023] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0024] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0025] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0026] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0029] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0030] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0031] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0032] To implement this invention, a system is constructed that acquires and analyzes driving data, evaluates safe driving, and adjusts insurance premiums. The processing of this program is described below in natural language.

[0033] A terminal installed in the vehicle collects real-time data on the driver's behavior while driving. This data includes the vehicle's acceleration, speed, location, brake usage, and steering wheel angle. The terminal transmits the collected data to a server at predetermined time intervals. Before transmission, the data is pre-processed to maintain its accuracy.

[0034] The server receives data sent from the terminal and inputs it into a dedicated generative AI model. This model combines past driving history and statistical driving patterns to evaluate the driver's driving behavior. As a result of the evaluation, the driver's level of safe driving is quantified, and each driver's driving style and risk factors can be quantitatively analyzed.

[0035] Based on the analysis results, the server adjusts the driver's insurance premium. Specifically, drivers who are evaluated as practicing safe driving will receive a discount on their next insurance premium. The server notifies the user of the adjusted insurance premium, and the user can check this information through a mobile application or other electronic means.

[0036] For example, suppose a driver brakes very infrequently over a month and always drives within the legal speed limit. This kind of safe driving data is highly rated on the server, resulting in a discount on the following month's insurance premium. This notification is sent to the user, who receives specific feedback in the application such as, "Your insurance premium will be discounted by 15% because you drove safely this month."

[0037] Thus, through this invention, users will have an incentive to drive safely and will be able to proactively work to improve safety in their future driving.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The terminal collects vehicle driving data in real time. Using sensors and GPS, it obtains detailed driving information such as acceleration, speed, location, and brake usage frequency.

[0041] Step 2:

[0042] The operating data collected by the terminal is preprocessed. Unnecessary noise is filtered out, and interrupted data is filled in to prepare the data for analysis.

[0043] Step 3:

[0044] The terminal encrypts pre-processed driving data and sends it to the server at regular intervals. Appropriate security protocols are applied during transmission to ensure the security of the communication.

[0045] Step 4:

[0046] The server receives driving data transmitted from the terminal and stores it in a database. The received data is managed individually for each driver.

[0047] Step 5:

[0048] The server inputs accumulated driving data into an AI model to analyze the driver's driving style. The model quantifies the degree of safe driving and the frequency of risky behaviors, and evaluates the driver's driving performance.

[0049] Step 6:

[0050] The server calculates the driver's insurance premium based on their driving performance. If the driver has a high level of safe driving, a discount rate is calculated and applied to the next bill.

[0051] Step 7:

[0052] The server notifies the user of the calculated insurance premium information. The user receives the notification through a mobile application and can view detailed driving feedback.

[0053] Step 8:

[0054] The system checks notifications sent by users to understand their driving style and whether discounts are applicable. It then uses the feedback to improve future driving behavior.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] There is a need to promote safe driving and to accurately evaluate driving data to set insurance premiums appropriate for individual users, and to make rapid premium adjustments based on that evaluation. However, conventional systems have the problem of insufficient collection and evaluation of driving data, and therefore fail to achieve fair premium adjustments based on individual driving behavior.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for preprocessing driving data, means for performing driving evaluations using a generated AI model, and means for adjusting fare information. This enables accurate and efficient analysis of collected driving data and fair adjustment of insurance premiums based on individual safe driving levels.

[0060] "Driving data" refers to various types of information acquired during driving, such as vehicle acceleration, speed, location information, braking status, and steering angle.

[0061] "Preprocessing" refers to processes performed to maintain the accuracy and consistency of acquired driving data, such as detecting and correcting abnormal values, smoothing data, and compressing data as needed.

[0062] A "generative AI model" is a model constructed using machine learning algorithms and is used to evaluate driving behavior by referring to past driving data and statistical patterns.

[0063] "Driving evaluation" refers to the process of analyzing a driver's driving behavior based on acquired driving data and quantifying their level of safe driving.

[0064] "Charging information" refers to information regarding financial burdens such as insurance premiums that are adjusted based on the driver's driving performance evaluation.

[0065] "Notification" refers to the process or method of communicating adjusted fare information and driving evaluation results to users.

[0066] This system consists of a terminal installed in the vehicle and a server connected via a network. The terminal uses various sensors to collect real-time data on the driver's behavior while driving. This data includes vehicle acceleration, speed, and location information obtained using an accelerometer and GPS module, as well as information on vehicle braking usage and steering angle. This allows for the collection of highly accurate driving data.

[0067] The terminal does not immediately transmit the collected driving data, but first performs data preprocessing. This includes removing outliers, smoothing the data, and compressing it as needed to maintain data quality. Performing this process improves the accuracy of subsequent analysis.

[0068] The pre-processed data is sent from the terminal to the server via a secure communication protocol (e.g., HTTPS). After receiving this data, the server inputs it into the generating AI model. The generating AI model uses a machine learning algorithm and operates based on the prompt message "Evaluate the driver's driving patterns and calculate the degree of safe driving." This model evaluates the driver's driving behavior by referring to past driving history and statistical data on driving patterns.

[0069] Based on the evaluation results, the server quantifies the driver's safe driving performance and adjusts individual insurance premiums. The adjusted premium information is then notified to the user from the server. Users can receive this information via a mobile application or email and check for details about future premium discounts.

[0070] For example, if a driver drives within the legal speed limit for a month and brakes very infrequently, this data will be highly rated on the server. As a result, the user will receive a notification that their insurance premium will be discounted by 15% the following month. This allows users to understand specifically how their safe driving is evaluated and how it is reflected in their insurance premiums. Through this system, an incentive for safe driving is provided, and drivers will strive to drive more safely.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The device uses various sensors to collect real-time data on the driver's behavior while driving. Specifically, it uses an accelerometer and GPS module to acquire data such as vehicle acceleration, speed, location, brake usage, and steering angle. This input data reflects the driving situation in detail.

[0074] Step 2:

[0075] The terminal performs preprocessing on the collected raw data. This step involves detecting and correcting data anomalies, denoising, and smoothing the data. Compression may also be performed to reduce data size as needed. As a result, it outputs high-quality, consistent data.

[0076] Step 3:

[0077] The terminal sends the prepared data to the server at predetermined time intervals. To ensure secure communication, the data is transmitted via the HTTPS protocol. The output is secure operational data.

[0078] Step 4:

[0079] The server receives data sent from the terminal. This data is input into the generating AI model. The server analyzes the driving data using the generating AI model, while referring to past driving data and statistical models. The evaluation is performed based on the prompt message "Evaluate the driver's driving pattern and calculate the degree of safe driving." The output after the analysis is data that quantifies the driver's degree of safe driving.

[0080] Step 5:

[0081] The server calculates and adjusts the driver's insurance premium based on numerical data on safe driving performance. Specifically, it applies a discount to the insurance premium if the driver's driving behavior is safe, based on pre-set evaluation criteria. The output is the adjusted insurance premium information.

[0082] Step 6:

[0083] The server notifies the user of the adjusted insurance premium information. This notification is delivered via a mobile application, email, or other means. It is crucial in this step to provide the user with specific feedback on the adjustment results. The output to the user is a notification message detailing the discount.

[0084] Throughout each step of the process, the terminal and server work together to scrutinize driving data, enabling fair evaluation and fare adjustments based on the actions of individual drivers.

[0085] (Application Example 1)

[0086] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0087] Conventional systems for evaluating operational information only analyze the acquired data, making it difficult to conduct detailed evaluations based on individual machine operations or propose incentives. This results in problems such as insufficient promotion of safe operation and inadequate feedback to operators.

[0088] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0089] In this invention, the server includes means for acquiring operational information, means for analyzing the acquired operational information and performing operational evaluations, means for adjusting fare information based on the operational evaluations, means for notifying the adjusted fare information, means for evaluating machine operations in real time during operation, and means for proposing a reward system based on the evaluation results. This makes it possible to specifically evaluate safe operation and provide a reward system tailored to individual machine operations.

[0090] "Operational information" refers to all data acquired when a vehicle or automobile is in motion, and specifically includes acceleration, speed, and location information.

[0091] "Analysis" refers to the process of evaluating specific machine operations and operating patterns in detail based on acquired operational information.

[0092] "Operational evaluation" refers to quantifying or assigning numerical values ​​to the safety and efficiency of vehicle operations based on analyzed operational information.

[0093] "Fee information" refers to information on insurance premiums and other operational costs determined based on the vehicle's operational evaluation.

[0094] "Machine operation" refers to the specific operations and actions performed by the vehicle while it is in operation, and is subject to real-time evaluation.

[0095] A "reward system" refers to a system that includes incentives and discounts proposed based on evaluation results, with the aim of encouraging drivers to operate safely.

[0096] In order to implement this invention, it is necessary to construct a system that acquires and analyzes operational information and proposes adjustments to fare information and a compensation system based on operational evaluations.

[0097] First, a terminal installed in the vehicle collects operational information in real time from vehicles and cars in motion. This operational information includes acceleration, speed, location information, and data obtained from various sensors within the vehicle. The terminal then transmits the data to a server at predetermined time intervals. Before transmitting the data, it undergoes preprocessing such as outlier removal and noise filtering.

[0098] The server analyzes the received operational information and evaluates it using a generated AI model. This AI model uses past operational history data and statistical operational patterns for its evaluation. The evaluation results are provided as a numerical safety score indicating how safe the operation is.

[0099] Based on the evaluation results, the server proposes a reward system. This proposal includes information on insurance premium discounts as an incentive for safe driving, and the server notifies the user. Users can check this information via their smartphone or in-car display.

[0100] For example, suppose a user has been driving safely for a month, staying within the legal speed limit and not using sudden braking. This safe driving data will be highly rated, and as a result, their insurance premium will be discounted the following month. The user will receive a notification stating, "Your current driving style is likely to qualify for a 5% discount on your insurance premium."

[0101] A concrete example of a prompt message would be: "Maintain acceleration of 0.05 m / s² or less, driving speed: 50 km / h, steering angle change: stable — Perform a safe driving assessment." In this way, the system can provide users with incentives for safe driving and promote improvements in driving safety.

[0102] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0103] Step 1:

[0104] The terminal acquires real-time operational information from various sensors mounted on the vehicle. This operational information includes acceleration, speed, and location data. The acquired data is filtered to remove outliers, improving its accuracy. The input is raw data from the sensors, and the output is refined operational information.

[0105] Step 2:

[0106] The terminal transmits refined operational information to the server at predetermined time intervals. During transmission, the data is aggregated into a predetermined format and transferred using a communication protocol. The input is the refined operational information, and the output is the operational information packet sent to the server.

[0107] Step 3:

[0108] The server receives operational information transmitted from the terminal and inputs it into the generated AI model. This model evaluates operations using past operational history data and statistical operational patterns. The input is operational information transmitted to the server, and the output is a numerical representation of the evaluated level of safe operation.

[0109] Step 4:

[0110] The server adjusts the fare information based on the evaluated safe driving performance. Specifically, if the safe driving performance is high, it calculates a discount on the insurance premium as a reward system and generates the adjusted fare information. The input is the evaluated safe driving performance, and the output is the adjusted fare information.

[0111] Step 5:

[0112] The server then notifies the user of the adjusted fare information. This notification is sent as a push message to the user's smartphone or in-car display. The input is the adjusted fare information, and the output is the notification message to the user.

[0113] Step 6:

[0114] Through the notification messages they receive, users can check how safe their operations are and what reward system applies as a result. This provides users with an incentive to proactively improve their operations. The input is the notification message, and the output is feedback that leads to improved user behavior.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention is a safe driving evaluation system that combines driving data with an emotion engine that analyzes the driver's emotional state. By adding an emotional element to the evaluation of driving behavior, this system aims to perform a more precise driving evaluation and promote safe driving.

[0117] The terminal installed in the vehicle has the function of collecting normal driving data such as acceleration, speed, and location information in real time, as well as sensors to capture the driver's voice and facial expressions. As a result, emotional data of the user while driving is collected on the terminal. The emotion engine analyzes this voice and facial expression data to evaluate the emotional state of the user, such as whether they are feeling stressed or angry.

[0118] The terminal transmits this data to the server at predetermined time intervals. On the server, the driving data and emotional data are analyzed by a generating AI model. This analysis comprehensively evaluates the driver's driving behavior and emotional state, and quantifies the degree of safe driving. In some cases, the degree of safe driving may be adjusted to take into account the influence of emotional state on driving behavior.

[0119] For example, if a driver is assessed as being under high stress due to sudden changes in speed, their safe driving score will be calculated more strictly. Based on this assessment, the insurance premium is adjusted on the server according to the safe driving score. If a driver is assessed as emotionally stable and driving safely, a discount on the insurance premium will be applied.

[0120] Users can receive detailed insurance premium information based on analysis results via a mobile application. The notifications include feedback on driving behavior and emotional state, allowing users to understand and improve their driving style. In this way, the present invention supports drivers' driving behavior from an emotional perspective as well, providing a safer driving environment.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The device collects driving data such as acceleration, speed, and location information in real time within the vehicle. It also uses voice and camera sensors to capture the user's voice and facial expressions, collecting emotional data.

[0124] Step 2:

[0125] The terminal preprocesses driving data and emotional data. Driving data is formatted and noise is removed. Emotional data is analyzed using voice and facial expression data to identify emotional states such as anger, stress, and relaxation.

[0126] Step 3:

[0127] The terminal sends pre-processed data to the server at specified time intervals. Encryption technology is applied to the data transmission to ensure the security of the communication.

[0128] Step 4:

[0129] The server receives driving and emotional data transmitted from the terminal. Each piece of data is recorded in a database and organized by driver.

[0130] Step 5:

[0131] The server uses a generated AI model to analyze the received data. Driving style is evaluated from driving data, and emotional state is evaluated from emotional data. Based on these, the degree of safe driving is quantified.

[0132] Step 6:

[0133] The server calculates insurance premiums based on driving and emotional assessments. The impact of emotions on driving risk is also considered, and a discount rate is calculated based on the degree of safe driving.

[0134] Step 7:

[0135] The server notifies the user of the calculation results. Notifications are delivered via a mobile application, providing driving and emotional feedback.

[0136] Step 8:

[0137] Users check notifications in the application and receive feedback on their driving style and emotional state. This encourages users to reflect on their driving and become more aware of the need for improvement.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] When evaluating safe driving, there is a need to consider not only driving data but also the driver's emotional state to perform more accurate driving assessments. However, conventional systems have difficulty incorporating emotional elements into the evaluation, and there is a challenge in that they cannot mitigate the impact of temporary emotional changes in the driver on the driving assessment.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for acquiring driving data and emotional data in real time, means for using a generative AI model for analyzing the acquired driving data and emotional data to perform a driving evaluation, and means for adjusting the driving evaluation based on the emotional analysis and quantifying the degree of safe driving. This enables a highly accurate safe driving evaluation that takes into account the driver's emotional state.

[0143] "Driving data" refers to data necessary to indicate the operating status of a vehicle, and includes travel speed, environmental acceleration, and location information.

[0144] "Emotional data" refers to voice and facial expression data used to evaluate the driver's emotional state.

[0145] A "generative AI model" is an artificial intelligence model that analyzes collected driving data and emotional data to perform driving evaluations.

[0146] "Driving evaluation" is a process that evaluates a driver's driving behavior based on driving data and emotional data, and calculates their level of safe driving.

[0147] "Safe driving score" is a numerical value obtained from driving evaluations and is an indicator of the safety of a driver's driving behavior.

[0148] "Charging information" refers to information related to financial burdens such as insurance premiums that are adjusted based on driving performance evaluations.

[0149] "Feedback information" refers to information provided to drivers regarding their driving behavior and emotional state, intended to help them improve these aspects.

[0150] This invention is a system for evaluating safe driving, consisting of a terminal and a server. The terminal is mounted in the vehicle and is equipped with various sensors to acquire driving data and emotional data in real time. Specifically, it uses an accelerometer, a GPS module for acquiring location information, and a camera and microphone for analyzing voice and facial expressions. Using these sensors, the terminal measures driving data such as speed, environmental acceleration, and location information, and also records the driver's voice tone and changes in facial expressions, acquiring them as emotional data.

[0151] The server receives driving and emotional data transmitted from the terminal. This data is input into a generative AI model and analyzed. The generative AI model detects abnormal driving patterns and comprehensively evaluates the associated emotional states to quantify the degree of safe driving. Based on the analysis, it generates specific feedback based on driving behavior and emotional states, and adjusts insurance premiums as needed.

[0152] Users can receive information transmitted from a server via their mobile devices and get detailed feedback on their driving behavior. For example, by inputting a prompt into an AI model such as, "Analyze the signs of stress in the driver's voice when sudden speed changes occur," it becomes clear how the driver's emotional state influenced their driving behavior.

[0153] This invention aims to provide drivers with a safer and more comfortable driving environment by conducting a safe driving evaluation that takes emotional states into account.

[0154] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0155] Step 1:

[0156] The device uses sensors mounted on the vehicle to acquire driving data (acceleration, speed, location) and emotional data (voice, facial expressions) in real time. It receives raw data from each sensor as input and generates an integrated dataset as output. This dataset contains various parameters sampled at specific time intervals.

[0157] Step 2:

[0158] The emotion engine within the device analyzes the acquired emotion data. Inputs include voice and facial expression data, which are converted into emotional states (stress, anger, joy, etc.). Outputs are labels and scores indicating the driver's emotional state. This process utilizes voice waveform pattern recognition and facial recognition algorithms.

[0159] Step 3:

[0160] The terminal transmits driving data and sentiment analysis results to the server at predetermined time intervals. The input is the analyzed dataset, and the output is the transmission of data to the server over the network. The data is compressed in real time, and the protocol is optimized to improve communication efficiency.

[0161] Step 4:

[0162] The server inputs the received data into a generating AI model to perform a comprehensive driving evaluation. The input consists of driving data and emotional data, while the output is an evaluation result representing the degree of safe driving. Here, abnormal driving patterns are identified and their impact is analyzed, and the influence of emotional states on driving behavior is calculated.

[0163] Step 5:

[0164] The server adjusts the fare information based on the safe driving level and generates feedback information for the user. The input is the safe driving level, and the output is the adjusted fare information and feedback message. Specifically, the insurance premium is calculated based on the evaluation results obtained from the generating AI model, and the content notified to the user is determined.

[0165] Step 6:

[0166] Users receive notifications sent from the server on their mobile devices and view feedback on their driving behavior and emotional state. Input is the server notification, and output is the information displayed via the user interface. Specifically, users can open the app, review the notified feedback, and identify areas for improvement in their driving style.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] Conventional driving evaluation systems relied solely on driving data, making it difficult to accurately assess safe driving. In particular, they failed to consider the impact of a driver's emotional state on driving behavior, resulting in a lack of appropriate feedback to drivers. Furthermore, the absence of real-time driving assistance meant drivers lacked opportunities to improve their driving behavior on the spot.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for analyzing driving data and performing driving evaluations, means for analyzing the driver's emotional state, and means for integrating the analyzed emotional state and driving evaluations to calculate the degree of safe driving. This enables a comprehensive driving evaluation that takes into account the driver's emotional state. Furthermore, by providing driving assistance information in real time through a smart device, the driver can improve their driving behavior on the spot.

[0172] "Driving data" refers to information about the vehicle's operation, including acceleration, speed, and location information.

[0173] "Driving evaluation" involves analyzing acquired driving data and evaluating the quality of driving behavior numerically or qualitatively.

[0174] "Emotional state" refers to the driver's psychological and emotional state, and is evaluated based on voice and facial expression information.

[0175] "Safe driving score" is an indicator of driving safety, calculated by integrating driving evaluation and emotional state.

[0176] "Charging information" refers to information regarding insurance premiums and the financial burden associated with driving.

[0177] A "smart device" is an electronic device equipped with communication capabilities that provides information to the driver in real time.

[0178] "Driving support information" refers to information provided to promote safe driving, including suggestions for improving driving style and relaxation techniques.

[0179] The system implementing this invention evaluates the degree of safe driving using the driver's emotional state and driving data. The terminal is equipped with various sensors for acquiring driving data, and uses an accelerometer and GPS module to acquire acceleration, speed, and location information in real time. Furthermore, a microphone and camera are provided to detect the driver's voice and facial expressions using speech recognition and image processing technology. This data is collected in real time via smart devices such as smart glasses and analyzed via a cloud server.

[0180] The server uses a generative AI model to analyze driving data and emotional state. This analysis not only calculates the level of safe driving but also generates relaxation suggestions and suggestions for improving the driver's driving style. Drivers can receive these suggestions through the display of their smart device. For example, if the driver's stress level is high, a suggestion such as "Take a deep breath and relax" will be displayed.

[0181] The hardware used here includes smart glasses, in-car cameras, and microphones, while cloud computing platforms (e.g., AWS®, Google® Cloud) are used for data processing and computation. On the software side, generative AI models (e.g., GPT-4®) are used for analysis.

[0182] A concrete example of a prompt message might be, "Analyze the emotional state of the driver currently at the wheel. If stress or anger levels are high, generate specific suggestions for relaxation." Such prompts are used to provide the driver with appropriate feedback in real time.

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The terminal acquires driving data. The device uses accelerometers, speed sensors, GPS modules, etc., within the vehicle to collect acceleration, speed, and position information. Inputs are data from each sensor, and outputs are recordings of driving data within the terminal.

[0186] Step 2:

[0187] The device acquires the driver's emotional data. It uses a microphone to collect the driver's voice and a camera to capture facial expression data. The input is audio and video data, and the output is emotional data stored on the device.

[0188] Step 3:

[0189] The device transmits acquired driving data and emotional data to a cloud server. The data is sent in real time via an encrypted communication channel. The input is driving data and emotional data, and the output is the transmission of data to the cloud server.

[0190] Step 4:

[0191] The server analyzes driving data and emotional data. Using a generative AI model, it analyzes the input data and calculates the safe driving score. The input is driving data and emotional data, and the output is a numerical value representing the safe driving score, which is the result of the analysis.

[0192] Step 5:

[0193] The server generates feedback for the driver based on the analysis results. Based on the prompt messages, it generates relaxation suggestions and suggestions for improving driving style. The input is numerical data from the analysis, and the output is a specific feedback message.

[0194] Step 6:

[0195] The server sends the generated feedback to the smart device. The driver can receive and confirm the feedback on the smart glasses display. The input is the feedback message, and the output is the display on the smart device.

[0196] Step 7:

[0197] The system adjusts the driving style based on user feedback. Users read the feedback messages and use them to improve their driving safety. The input is the content of the feedback, and the output is the change in the user's behavior.

[0198] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0199] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0200] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0201] [Second Embodiment]

[0202] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0203] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0204] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0205] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0206] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0207] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0208] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0209] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0210] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0211] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0212] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0213] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0214] To implement this invention, a system is constructed that acquires and analyzes driving data, evaluates safe driving, and adjusts insurance premiums. The processing of this program is described below in natural language.

[0215] A terminal installed in the vehicle collects real-time data on the driver's behavior while driving. This data includes the vehicle's acceleration, speed, location, brake usage, and steering wheel angle. The terminal transmits the collected data to a server at predetermined time intervals. Before transmission, the data is pre-processed to maintain its accuracy.

[0216] The server receives data sent from the terminal and inputs it into a dedicated generative AI model. This model combines past driving history and statistical driving patterns to evaluate the driver's driving behavior. As a result of the evaluation, the driver's level of safe driving is quantified, and each driver's driving style and risk factors can be quantitatively analyzed.

[0217] Based on the analysis results, the server adjusts the driver's insurance premium. Specifically, drivers who are evaluated as practicing safe driving will receive a discount on their next insurance premium. The server notifies the user of the adjusted insurance premium, and the user can check this information through a mobile application or other electronic means.

[0218] For example, suppose a driver brakes very infrequently over a month and always drives within the legal speed limit. This kind of safe driving data is highly rated on the server, resulting in a discount on the following month's insurance premium. This notification is sent to the user, who receives specific feedback in the application such as, "Your insurance premium will be discounted by 15% because you drove safely this month."

[0219] Thus, through this invention, users will have an incentive to drive safely and will be able to proactively work to improve safety in their future driving.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The terminal collects vehicle driving data in real time. Using sensors and GPS, it obtains detailed driving information such as acceleration, speed, location, and brake usage frequency.

[0223] Step 2:

[0224] The operating data collected by the terminal is preprocessed. Unnecessary noise is filtered out, and interrupted data is filled in to prepare the data for analysis.

[0225] Step 3:

[0226] The terminal encrypts pre-processed driving data and sends it to the server at regular intervals. Appropriate security protocols are applied during transmission to ensure the security of the communication.

[0227] Step 4:

[0228] The server receives driving data transmitted from the terminal and stores it in a database. The received data is managed individually for each driver.

[0229] Step 5:

[0230] The server inputs accumulated driving data into an AI model to analyze the driver's driving style. The model quantifies the degree of safe driving and the frequency of risky behaviors, and evaluates the driver's driving performance.

[0231] Step 6:

[0232] The server calculates the driver's insurance premium based on their driving performance. If the driver has a high level of safe driving, a discount rate is calculated and applied to the next bill.

[0233] Step 7:

[0234] The server notifies the user of the calculated insurance premium information. The user receives the notification through a mobile application and can view detailed driving feedback.

[0235] Step 8:

[0236] The system checks notifications sent by users to understand their driving style and whether discounts are applicable. It then uses the feedback to improve future driving behavior.

[0237] (Example 1)

[0238] Next, we will describe Example 1. 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."

[0239] There is a need to promote safe driving and to accurately evaluate driving data to set insurance premiums appropriate for individual users, and to make rapid premium adjustments based on that evaluation. However, conventional systems have the problem of insufficient collection and evaluation of driving data, and therefore fail to achieve fair premium adjustments based on individual driving behavior.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes means for preprocessing driving data, means for performing driving evaluations using a generated AI model, and means for adjusting fare information. This enables accurate and efficient analysis of collected driving data and fair adjustment of insurance premiums based on individual safe driving levels.

[0242] "Driving data" refers to various types of information acquired during driving, such as vehicle acceleration, speed, location information, braking status, and steering angle.

[0243] "Preprocessing" refers to processes performed to maintain the accuracy and consistency of acquired driving data, such as detecting and correcting abnormal values, smoothing data, and compressing data as needed.

[0244] A "generative AI model" is a model constructed using machine learning algorithms and is used to evaluate driving behavior by referring to past driving data and statistical patterns.

[0245] "Driving evaluation" refers to the process of analyzing a driver's driving behavior based on acquired driving data and quantifying their level of safe driving.

[0246] "Charging information" refers to information regarding financial burdens such as insurance premiums that are adjusted based on the driver's driving performance evaluation.

[0247] "Notification" refers to the process or method of communicating adjusted fare information and driving evaluation results to users.

[0248] This system consists of a terminal installed in the vehicle and a server connected via a network. The terminal uses various sensors to collect real-time data on the driver's behavior while driving. This data includes vehicle acceleration, speed, and location information obtained using an accelerometer and GPS module, as well as information on vehicle braking usage and steering angle. This allows for the collection of highly accurate driving data.

[0249] The terminal does not immediately transmit the collected driving data, but first performs data preprocessing. This includes removing outliers, smoothing the data, and compressing it as needed to maintain data quality. Performing this process improves the accuracy of subsequent analysis.

[0250] The pre-processed data is sent from the terminal to the server via a secure communication protocol (e.g., HTTPS). After receiving this data, the server inputs it into the generating AI model. The generating AI model uses a machine learning algorithm and operates based on the prompt message "Evaluate the driver's driving patterns and calculate the degree of safe driving." This model evaluates the driver's driving behavior by referring to past driving history and statistical data on driving patterns.

[0251] Based on the evaluation results, the server quantifies the driver's safe driving performance and adjusts individual insurance premiums. The adjusted premium information is then notified to the user from the server. Users can receive this information via a mobile application or email and check for details about future premium discounts.

[0252] For example, if a driver drives within the legal speed limit for a month and brakes very infrequently, this data will be highly rated on the server. As a result, the user will receive a notification that their insurance premium will be discounted by 15% the following month. This allows users to understand specifically how their safe driving is evaluated and how it is reflected in their insurance premiums. Through this system, an incentive for safe driving is provided, and drivers will strive to drive more safely.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The device uses various sensors to collect real-time data on the driver's behavior while driving. Specifically, it uses an accelerometer and GPS module to acquire data such as vehicle acceleration, speed, location, brake usage, and steering angle. This input data reflects the driving situation in detail.

[0256] Step 2:

[0257] The terminal performs preprocessing on the collected raw data. This step involves detecting and correcting data anomalies, denoising, and smoothing the data. Compression may also be performed to reduce data size as needed. As a result, it outputs high-quality, consistent data.

[0258] Step 3:

[0259] The terminal sends the prepared data to the server at predetermined time intervals. To ensure secure communication, the data is transmitted via the HTTPS protocol. The output is secure operational data.

[0260] Step 4:

[0261] The server receives data sent from the terminal. This data is input into the generating AI model. The server analyzes the driving data using the generating AI model, while referring to past driving data and statistical models. The evaluation is performed based on the prompt message "Evaluate the driver's driving pattern and calculate the degree of safe driving." The output after the analysis is data that quantifies the driver's degree of safe driving.

[0262] Step 5:

[0263] The server calculates and adjusts the driver's insurance premium based on numerical data on safe driving performance. Specifically, it applies a discount to the insurance premium if the driver's driving behavior is safe, based on pre-set evaluation criteria. The output is the adjusted insurance premium information.

[0264] Step 6:

[0265] The server notifies the user of the adjusted insurance premium information. This notification is delivered via a mobile application, email, or other means. It is crucial in this step to provide the user with specific feedback on the adjustment results. The output to the user is a notification message detailing the discount.

[0266] Throughout each step of the process, the terminal and server work together to scrutinize driving data, enabling fair evaluation and fare adjustments based on the actions of individual drivers.

[0267] (Application Example 1)

[0268] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0269] Conventional systems for evaluating operational information only analyze the acquired data, making it difficult to conduct detailed evaluations based on individual machine operations or propose incentives. This results in problems such as insufficient promotion of safe operation and inadequate feedback to operators.

[0270] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0271] In this invention, the server includes means for acquiring operational information, means for analyzing the acquired operational information and performing operational evaluations, means for adjusting fare information based on the operational evaluations, means for notifying the adjusted fare information, means for evaluating machine operations in real time during operation, and means for proposing a reward system based on the evaluation results. This makes it possible to specifically evaluate safe operation and provide a reward system tailored to individual machine operations.

[0272] "Operational information" refers to all data acquired when a vehicle or automobile is in motion, and specifically includes acceleration, speed, and location information.

[0273] "Analysis" refers to the process of evaluating specific machine operations and operating patterns in detail based on acquired operational information.

[0274] "Operational evaluation" refers to quantifying or assigning numerical values ​​to the safety and efficiency of vehicle operations based on analyzed operational information.

[0275] "Fee information" refers to information on insurance premiums and other operational costs determined based on the vehicle's operational evaluation.

[0276] "Machine operation" refers to the specific operations and actions performed by the vehicle while it is in operation, and is subject to real-time evaluation.

[0277] A "reward system" refers to a system that includes incentives and discounts proposed based on evaluation results, with the aim of encouraging drivers to operate safely.

[0278] In order to implement this invention, it is necessary to construct a system that acquires and analyzes operational information and proposes adjustments to fare information and a compensation system based on operational evaluations.

[0279] First, the terminal installed in the vehicle collects real-time operation information from the running vehicle or automobile. This operation information includes acceleration, speed, position information, and data obtained from various sensors inside the vehicle. Subsequently, the terminal transmits the data to the server at defined time intervals. Before transmitting the data, preprocessing of the data is performed, such as removing outliers and noise filtering.

[0280] The server analyzes the received operation information and evaluates the information using a generated AI model. This AI model performs the evaluation using past operation history data and statistical operation patterns. The result of the evaluation is provided as a quantified safe operation level indicating the degree of safety of the operation.

[0281] Based on the evaluation result, the server proposes a reward system. This proposal includes discount information on insurance premiums as an incentive for safe operation and is notified from the server to the user. The user can confirm this information through a smartphone or an in-vehicle display.

[0282] For example, assume that a certain user has been performing safe operation for one month, driving within the legal speed, and not using sudden braking. The data of this safe operation is highly evaluated, and as a result, the insurance premium for the next month is discounted. The user is notified that "There is a possibility that the insurance premium will be discounted by 5% for the current driving."

[0283] As an example of a specific prompt sentence, it is in the form of "Acceleration: Maintain at 0.05 m / s² or less, driving speed: 50 km / h, steering angle change: stable —— Execute the evaluation of the safe operation level." In this way, the system can provide an incentive for safe operation to the user and promote the improvement of operation safety.

[0284] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0285] Step 1:

[0286] The terminal obtains real-time operation information from various sensors installed in the vehicle. This operation information includes acceleration, speed, position information, etc. The acquired data is processed by filtering to remove outliers to improve the accuracy of the data. The input is the raw data from the sensors, and the output is the refined operation information.

[0287] Step 2:

[0288] The terminal transmits the refined operation information to the server at a predetermined time interval. When transmitting, the data is aggregated into a predetermined format and transferred using a communication protocol. The input is the refined operation information, and the output is the operation information packet transmitted to the server.

[0289] Step 3:

[0290] The server receives the operation information transmitted from the terminal and inputs it into the generated AI model. This model evaluates the operation using past operation history data and statistical operation patterns. The input is the operation information transmitted to the server, and the output is quantified as the evaluated safe operation level.

[0291] Step 4:

[0292] The server adjusts the fee information based on the evaluated safe operation level. Specifically, when the safe operation level is high, an insurance premium discount is calculated as a reward system, and the adjusted fee information is generated. The input is the evaluated safe operation level, and the output is the adjusted fee information.

[0293] Step 5:

[0294] The server further notifies the user of the adjusted fee information. The notification is made by sending a push message to the user's smartphone or in-vehicle display. The input is the adjusted fee information, and the output is the notification message to the user.

[0295] Step 6:

[0296] Through the notification messages they receive, users can check how safe their operations are and what reward system applies as a result. This provides users with an incentive to proactively improve their operations. The input is the notification message, and the output is feedback that leads to improved user behavior.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] This invention is a safe driving evaluation system that combines driving data with an emotion engine that analyzes the driver's emotional state. By adding an emotional element to the evaluation of driving behavior, this system aims to perform a more precise driving evaluation and promote safe driving.

[0299] The terminal installed in the vehicle has the function of collecting normal driving data such as acceleration, speed, and location information in real time, as well as sensors to capture the driver's voice and facial expressions. As a result, emotional data of the user while driving is collected on the terminal. The emotion engine analyzes this voice and facial expression data to evaluate the emotional state of the user, such as whether they are feeling stressed or angry.

[0300] The terminal transmits this data to the server at predetermined time intervals. On the server, the driving data and emotional data are analyzed by a generating AI model. This analysis comprehensively evaluates the driver's driving behavior and emotional state, and quantifies the degree of safe driving. In some cases, the degree of safe driving may be adjusted to take into account the influence of emotional state on driving behavior.

[0301] For example, if a driver is evaluated to be in a high-stress state with a sudden speed change, the safe driving level is calculated strictly. Based on this evaluation result, the insurance premium is adjusted on the server according to the safe driving level. When the driver's emotion is stable and they are evaluated to be driving safely, an insurance premium discount is applied.

[0302] The user can receive the details of the insurance premium based on the analysis results through a mobile application. The notification includes feedback on driving behavior and emotional state, enabling the user to understand and improve their driving style. Thus, the present invention supports the driver's driving behavior from an emotional aspect and provides a safer driving environment.

[0303] The following describes the process flow.

[0304] Step 1:

[0305] The terminal collects driving data such as acceleration, speed, and position information in real time inside the vehicle. Also, using voice and camera sensors, the user's voice and expression are acquired to collect emotion data.

[0306] Step 2:

[0307] The terminal preprocesses the driving data and emotion data. The driving data is formatted and noise is removed. Regarding the emotion data, voice and expression data are analyzed to identify emotional states such as anger, stress, and relaxation.

[0308] Step 3:

[0309] The terminal sends the preprocessed data to the server at a specified time interval. Encryption technology is applied to data transmission to ensure communication security.

[0310] Step 4:

[0311] The server receives driving and emotional data transmitted from the terminal. Each piece of data is recorded in a database and organized by driver.

[0312] Step 5:

[0313] The server uses a generated AI model to analyze the received data. Driving style is evaluated from driving data, and emotional state is evaluated from emotional data. Based on these, the degree of safe driving is quantified.

[0314] Step 6:

[0315] The server calculates insurance premiums based on driving and emotional assessments. The impact of emotions on driving risk is also considered, and a discount rate is calculated based on the degree of safe driving.

[0316] Step 7:

[0317] The server notifies the user of the calculation results. Notifications are delivered via a mobile application, providing driving and emotional feedback.

[0318] Step 8:

[0319] Users check notifications in the application and receive feedback on their driving style and emotional state. This encourages users to reflect on their driving and become more aware of the need for improvement.

[0320] (Example 2)

[0321] Next, we will describe Example 2. 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".

[0322] When evaluating safe driving, there is a need to consider not only driving data but also the driver's emotional state to perform more accurate driving assessments. However, conventional systems have difficulty incorporating emotional elements into the evaluation, and there is a challenge in that they cannot mitigate the impact of temporary emotional changes in the driver on the driving assessment.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for acquiring driving data and emotional data in real time, means for using a generative AI model for analyzing the acquired driving data and emotional data to perform a driving evaluation, and means for adjusting the driving evaluation based on the emotional analysis and quantifying the degree of safe driving. This enables a highly accurate safe driving evaluation that takes into account the driver's emotional state.

[0325] "Driving data" refers to data necessary to indicate the operating status of a vehicle, and includes travel speed, environmental acceleration, and location information.

[0326] "Emotional data" refers to voice and facial expression data used to evaluate the driver's emotional state.

[0327] A "generative AI model" is an artificial intelligence model that analyzes collected driving data and emotional data to perform driving evaluations.

[0328] "Driving evaluation" is a process that evaluates a driver's driving behavior based on driving data and emotional data, and calculates their level of safe driving.

[0329] "Safe driving score" is a numerical value obtained from driving evaluations and is an indicator of the safety of a driver's driving behavior.

[0330] "Charging information" refers to information related to financial burdens such as insurance premiums that are adjusted based on driving performance evaluations.

[0331] "Feedback information" refers to information provided to drivers regarding their driving behavior and emotional state, intended to help them improve these aspects.

[0332] This invention is a system for evaluating safe driving, consisting of a terminal and a server. The terminal is mounted in the vehicle and is equipped with various sensors to acquire driving data and emotional data in real time. Specifically, it uses an accelerometer, a GPS module for acquiring location information, and a camera and microphone for analyzing voice and facial expressions. Using these sensors, the terminal measures driving data such as speed, environmental acceleration, and location information, and also records the driver's voice tone and changes in facial expressions, acquiring them as emotional data.

[0333] The server receives driving and emotional data transmitted from the terminal. This data is input into a generative AI model and analyzed. The generative AI model detects abnormal driving patterns and comprehensively evaluates the associated emotional states to quantify the degree of safe driving. Based on the analysis, it generates specific feedback based on driving behavior and emotional states, and adjusts insurance premiums as needed.

[0334] Users can receive information transmitted from a server via their mobile devices and get detailed feedback on their driving behavior. For example, by inputting a prompt into an AI model such as, "Analyze the signs of stress in the driver's voice when sudden speed changes occur," it becomes clear how the driver's emotional state influenced their driving behavior.

[0335] This invention aims to provide drivers with a safer and more comfortable driving environment by conducting a safe driving evaluation that takes emotional states into account.

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] The device uses sensors mounted on the vehicle to acquire driving data (acceleration, speed, location) and emotional data (voice, facial expressions) in real time. It receives raw data from each sensor as input and generates an integrated dataset as output. This dataset contains various parameters sampled at specific time intervals.

[0339] Step 2:

[0340] The emotion engine within the device analyzes the acquired emotion data. Inputs include voice and facial expression data, which are converted into emotional states (stress, anger, joy, etc.). Outputs are labels and scores indicating the driver's emotional state. This process utilizes voice waveform pattern recognition and facial recognition algorithms.

[0341] Step 3:

[0342] The terminal transmits driving data and sentiment analysis results to the server at predetermined time intervals. The input is the analyzed dataset, and the output is the transmission of data to the server over the network. The data is compressed in real time, and the protocol is optimized to improve communication efficiency.

[0343] Step 4:

[0344] The server inputs the received data into a generating AI model to perform a comprehensive driving evaluation. The input consists of driving data and emotional data, while the output is an evaluation result representing the degree of safe driving. Here, abnormal driving patterns are identified and their impact is analyzed, and the influence of emotional states on driving behavior is calculated.

[0345] Step 5:

[0346] The server adjusts the fare information based on the safe driving level and generates feedback information for the user. The input is the safe driving level, and the output is the adjusted fare information and feedback message. Specifically, the insurance premium is calculated based on the evaluation results obtained from the generating AI model, and the content notified to the user is determined.

[0347] Step 6:

[0348] Users receive notifications sent from the server on their mobile devices and view feedback on their driving behavior and emotional state. Input is the server notification, and output is the information displayed via the user interface. Specifically, users can open the app, review the notified feedback, and identify areas for improvement in their driving style.

[0349] (Application Example 2)

[0350] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0351] Conventional driving evaluation systems relied solely on driving data, making it difficult to accurately assess safe driving. In particular, they failed to consider the impact of a driver's emotional state on driving behavior, resulting in a lack of appropriate feedback to drivers. Furthermore, the absence of real-time driving assistance meant drivers lacked opportunities to improve their driving behavior on the spot.

[0352] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0353] In this invention, the server includes means for analyzing driving data and performing driving evaluations, means for analyzing the driver's emotional state, and means for integrating the analyzed emotional state and driving evaluations to calculate the degree of safe driving. This enables a comprehensive driving evaluation that takes into account the driver's emotional state. Furthermore, by providing driving assistance information in real time through a smart device, the driver can improve their driving behavior on the spot.

[0354] "Driving data" refers to information about the vehicle's operation, including acceleration, speed, and location information.

[0355] "Driving evaluation" involves analyzing acquired driving data and evaluating the quality of driving behavior numerically or qualitatively.

[0356] "Emotional state" refers to the driver's psychological and emotional state, and is evaluated based on voice and facial expression information.

[0357] "Safe driving score" is an indicator of driving safety, calculated by integrating driving evaluation and emotional state.

[0358] "Charging information" refers to information regarding insurance premiums and the financial burden associated with driving.

[0359] A "smart device" is an electronic device equipped with communication capabilities that provides information to the driver in real time.

[0360] "Driving support information" refers to information provided to promote safe driving, including suggestions for improving driving style and relaxation techniques.

[0361] The system implementing this invention evaluates the degree of safe driving using the driver's emotional state and driving data. The terminal is equipped with various sensors for acquiring driving data, and uses an accelerometer and GPS module to acquire acceleration, speed, and location information in real time. Furthermore, a microphone and camera are provided to detect the driver's voice and facial expressions using speech recognition and image processing technology. This data is collected in real time via smart devices such as smart glasses and analyzed via a cloud server.

[0362] The server uses a generative AI model to analyze driving data and emotional state. This analysis not only calculates the level of safe driving but also generates relaxation suggestions and suggestions for improving the driver's driving style. Drivers can receive these suggestions through the display of their smart device. For example, if the driver's stress level is high, a suggestion such as "Take a deep breath and relax" will be displayed.

[0363] The hardware used here includes smart glasses, in-car cameras, and microphones, while cloud computing platforms (e.g., AWS, Google Cloud) are utilized for data processing and computation. On the software side, generative AI models (e.g., GPT-4) are used for analysis.

[0364] A concrete example of a prompt message might be, "Analyze the emotional state of the driver currently at the wheel. If stress or anger levels are high, generate specific suggestions for relaxation." Such prompts are used to provide the driver with appropriate feedback in real time.

[0365] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0366] Step 1:

[0367] The terminal acquires driving data. The device uses accelerometers, speed sensors, GPS modules, etc., within the vehicle to collect acceleration, speed, and position information. Inputs are data from each sensor, and outputs are recordings of driving data within the terminal.

[0368] Step 2:

[0369] The device acquires the driver's emotional data. It uses a microphone to collect the driver's voice and a camera to capture facial expression data. The input is audio and video data, and the output is emotional data stored on the device.

[0370] Step 3:

[0371] The device transmits acquired driving data and emotional data to a cloud server. The data is sent in real time via an encrypted communication channel. The input is driving data and emotional data, and the output is the transmission of data to the cloud server.

[0372] Step 4:

[0373] The server analyzes driving data and emotional data. Using a generative AI model, it analyzes the input data and calculates the safe driving score. The input is driving data and emotional data, and the output is a numerical value representing the safe driving score, which is the result of the analysis.

[0374] Step 5:

[0375] The server generates feedback for the driver based on the analysis results. Based on the prompt messages, it generates relaxation suggestions and suggestions for improving driving style. The input is numerical data from the analysis, and the output is a specific feedback message.

[0376] Step 6:

[0377] The server sends the generated feedback to the smart device. The driver can receive and confirm the feedback on the smart glasses display. The input is the feedback message, and the output is the display on the smart device.

[0378] Step 7:

[0379] The system adjusts the driving style based on user feedback. Users read the feedback messages and use them to improve their driving safety. The input is the content of the feedback, and the output is the change in the user's behavior.

[0380] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0381] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0382] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0386] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0387] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0388] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0389] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0390] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0391] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0392] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0393] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0394] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0395] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0396] To implement this invention, a system is constructed that acquires and analyzes driving data, evaluates safe driving, and adjusts insurance premiums. The processing of this program is described below in natural language.

[0397] A terminal installed in the vehicle collects real-time data on the driver's behavior while driving. This data includes the vehicle's acceleration, speed, location, brake usage, and steering wheel angle. The terminal transmits the collected data to a server at predetermined time intervals. Before transmission, the data is pre-processed to maintain its accuracy.

[0398] The server receives data sent from the terminal and inputs it into a dedicated generative AI model. This model combines past driving history and statistical driving patterns to evaluate the driver's driving behavior. As a result of the evaluation, the driver's level of safe driving is quantified, and each driver's driving style and risk factors can be quantitatively analyzed.

[0399] Based on the analysis results, the server adjusts the driver's insurance premium. Specifically, drivers who are evaluated as practicing safe driving will receive a discount on their next insurance premium. The server notifies the user of the adjusted insurance premium, and the user can check this information through a mobile application or other electronic means.

[0400] For example, suppose a driver brakes very infrequently over a month and always drives within the legal speed limit. This kind of safe driving data is highly rated on the server, resulting in a discount on the following month's insurance premium. This notification is sent to the user, who receives specific feedback in the application such as, "Your insurance premium will be discounted by 15% because you drove safely this month."

[0401] Thus, through this invention, users will have an incentive to drive safely and will be able to proactively work to improve safety in their future driving.

[0402] The following describes the processing flow.

[0403] Step 1:

[0404] The terminal collects vehicle driving data in real time. Using sensors and GPS, it obtains detailed driving information such as acceleration, speed, location, and brake usage frequency.

[0405] Step 2:

[0406] The operating data collected by the terminal is preprocessed. Unnecessary noise is filtered out, and interrupted data is filled in to prepare the data for analysis.

[0407] Step 3:

[0408] The terminal encrypts pre-processed driving data and sends it to the server at regular intervals. Appropriate security protocols are applied during transmission to ensure the security of the communication.

[0409] Step 4:

[0410] The server receives driving data transmitted from the terminal and stores it in a database. The received data is managed individually for each driver.

[0411] Step 5:

[0412] The server inputs accumulated driving data into an AI model to analyze the driver's driving style. The model quantifies the degree of safe driving and the frequency of risky behaviors, and evaluates the driver's driving performance.

[0413] Step 6:

[0414] The server calculates the driver's insurance premium based on their driving performance. If the driver has a high level of safe driving, a discount rate is calculated and applied to the next bill.

[0415] Step 7:

[0416] The server notifies the user of the calculated insurance premium information. The user receives the notification through a mobile application and can view detailed driving feedback.

[0417] Step 8:

[0418] The system checks notifications sent by users to understand their driving style and whether discounts are applicable. It then uses the feedback to improve future driving behavior.

[0419] (Example 1)

[0420] Next, we will describe Example 1. 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."

[0421] There is a need to promote safe driving and to accurately evaluate driving data to set insurance premiums appropriate for individual users, and to make rapid premium adjustments based on that evaluation. However, conventional systems have the problem of insufficient collection and evaluation of driving data, and therefore fail to achieve fair premium adjustments based on individual driving behavior.

[0422] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0423] In this invention, the server includes means for preprocessing driving data, means for performing driving evaluations using a generated AI model, and means for adjusting fare information. This enables accurate and efficient analysis of collected driving data and fair adjustment of insurance premiums based on individual safe driving levels.

[0424] "Driving data" refers to various types of information acquired during driving, such as vehicle acceleration, speed, location information, braking status, and steering angle.

[0425] "Preprocessing" refers to processes performed to maintain the accuracy and consistency of acquired driving data, such as detecting and correcting abnormal values, smoothing data, and compressing data as needed.

[0426] A "generative AI model" is a model constructed using machine learning algorithms and is used to evaluate driving behavior by referring to past driving data and statistical patterns.

[0427] "Driving evaluation" refers to the process of analyzing a driver's driving behavior based on acquired driving data and quantifying their level of safe driving.

[0428] "Charging information" refers to information regarding financial burdens such as insurance premiums that are adjusted based on the driver's driving performance evaluation.

[0429] "Notification" refers to the process or method of communicating adjusted fare information and driving evaluation results to users.

[0430] This system consists of a terminal installed in the vehicle and a server connected via a network. The terminal uses various sensors to collect real-time data on the driver's behavior while driving. This data includes vehicle acceleration, speed, and location information obtained using an accelerometer and GPS module, as well as information on vehicle braking usage and steering angle. This allows for the collection of highly accurate driving data.

[0431] The terminal does not immediately transmit the collected driving data, but first performs data preprocessing. This includes removing outliers, smoothing the data, and compressing it as needed to maintain data quality. Performing this process improves the accuracy of subsequent analysis.

[0432] The pre-processed data is sent from the terminal to the server via a secure communication protocol (e.g., HTTPS). After receiving this data, the server inputs it into the generating AI model. The generating AI model uses a machine learning algorithm and operates based on the prompt message "Evaluate the driver's driving patterns and calculate the degree of safe driving." This model evaluates the driver's driving behavior by referring to past driving history and statistical data on driving patterns.

[0433] Based on the evaluation results, the server quantifies the driver's safe driving performance and adjusts individual insurance premiums. The adjusted premium information is then notified to the user from the server. Users can receive this information via a mobile application or email and check for details about future premium discounts.

[0434] For example, if a driver drives within the legal speed limit for a month and brakes very infrequently, this data will be highly rated on the server. As a result, the user will receive a notification that their insurance premium will be discounted by 15% the following month. This allows users to understand specifically how their safe driving is evaluated and how it is reflected in their insurance premiums. Through this system, an incentive for safe driving is provided, and drivers will strive to drive more safely.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The device uses various sensors to collect real-time data on the driver's behavior while driving. Specifically, it uses an accelerometer and GPS module to acquire data such as vehicle acceleration, speed, location, brake usage, and steering angle. This input data reflects the driving situation in detail.

[0438] Step 2:

[0439] The terminal performs preprocessing on the collected raw data. This step involves detecting and correcting data anomalies, denoising, and smoothing the data. Compression may also be performed to reduce data size as needed. As a result, it outputs high-quality, consistent data.

[0440] Step 3:

[0441] The terminal sends the prepared data to the server at predetermined time intervals. To ensure secure communication, the data is transmitted via the HTTPS protocol. The output is secure operational data.

[0442] Step 4:

[0443] The server receives data sent from the terminal. This data is input into the generating AI model. The server analyzes the driving data using the generating AI model, while referring to past driving data and statistical models. The evaluation is performed based on the prompt message "Evaluate the driver's driving pattern and calculate the degree of safe driving." The output after the analysis is data that quantifies the driver's degree of safe driving.

[0444] Step 5:

[0445] The server calculates and adjusts the driver's insurance premium based on numerical data on safe driving performance. Specifically, it applies a discount to the insurance premium if the driver's driving behavior is safe, based on pre-set evaluation criteria. The output is the adjusted insurance premium information.

[0446] Step 6:

[0447] The server notifies the user of the adjusted insurance premium information. This notification is delivered via a mobile application, email, or other means. It is crucial in this step to provide the user with specific feedback on the adjustment results. The output to the user is a notification message detailing the discount.

[0448] Throughout each step of the process, the terminal and server work together to scrutinize driving data, enabling fair evaluation and fare adjustments based on the actions of individual drivers.

[0449] (Application Example 1)

[0450] Next, we will explain Application Example 1. In the following explanation, 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."

[0451] Conventional systems for evaluating operational information only analyze the acquired data, making it difficult to conduct detailed evaluations based on individual machine operations or propose incentives. This results in problems such as insufficient promotion of safe operation and inadequate feedback to operators.

[0452] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0453] In this invention, the server includes means for acquiring operational information, means for analyzing the acquired operational information and performing operational evaluations, means for adjusting fare information based on the operational evaluations, means for notifying the adjusted fare information, means for evaluating machine operations in real time during operation, and means for proposing a reward system based on the evaluation results. This makes it possible to specifically evaluate safe operation and provide a reward system tailored to individual machine operations.

[0454] "Operational information" refers to all data acquired when a vehicle or automobile is in motion, and specifically includes acceleration, speed, and location information.

[0455] "Analysis" refers to the process of evaluating specific machine operations and operating patterns in detail based on acquired operational information.

[0456] "Operational evaluation" refers to quantifying or assigning numerical values ​​to the safety and efficiency of vehicle operations based on analyzed operational information.

[0457] "Fee information" refers to information on insurance premiums and other operational costs determined based on the vehicle's operational evaluation.

[0458] "Machine operation" refers to the specific operations and actions performed by the vehicle while it is in operation, and is subject to real-time evaluation.

[0459] A "reward system" refers to a system that includes incentives and discounts proposed based on evaluation results, with the aim of encouraging drivers to operate safely.

[0460] In order to implement this invention, it is necessary to construct a system that acquires and analyzes operational information and proposes adjustments to fare information and a compensation system based on operational evaluations.

[0461] First, a terminal installed in the vehicle collects operational information in real time from vehicles and cars in motion. This operational information includes acceleration, speed, location information, and data obtained from various sensors within the vehicle. The terminal then transmits the data to a server at predetermined time intervals. Before transmitting the data, it undergoes preprocessing such as outlier removal and noise filtering.

[0462] The server analyzes the received operational information and evaluates it using a generated AI model. This AI model uses past operational history data and statistical operational patterns for its evaluation. The evaluation results are provided as a numerical safety score indicating how safe the operation is.

[0463] Based on the evaluation results, the server proposes a reward system. This proposal includes information on insurance premium discounts as an incentive for safe driving, and the server notifies the user. Users can check this information via their smartphone or in-car display.

[0464] For example, suppose a user has been driving safely for a month, staying within the legal speed limit and not using sudden braking. This safe driving data will be highly rated, and as a result, their insurance premium will be discounted the following month. The user will receive a notification stating, "Your current driving style is likely to qualify for a 5% discount on your insurance premium."

[0465] A concrete example of a prompt message would be: "Maintain acceleration of 0.05 m / s² or less, driving speed: 50 km / h, steering angle change: stable — Perform a safe driving assessment." In this way, the system can provide users with incentives for safe driving and promote improvements in driving safety.

[0466] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0467] Step 1:

[0468] The terminal acquires real-time operational information from various sensors mounted on the vehicle. This operational information includes acceleration, speed, and location data. The acquired data is filtered to remove outliers, improving its accuracy. The input is raw data from the sensors, and the output is refined operational information.

[0469] Step 2:

[0470] The terminal transmits refined operational information to the server at predetermined time intervals. During transmission, the data is aggregated into a predetermined format and transferred using a communication protocol. The input is the refined operational information, and the output is the operational information packet sent to the server.

[0471] Step 3:

[0472] The server receives operational information transmitted from the terminal and inputs it into the generated AI model. This model evaluates operations using past operational history data and statistical operational patterns. The input is operational information transmitted to the server, and the output is a numerical representation of the evaluated level of safe operation.

[0473] Step 4:

[0474] The server adjusts the fare information based on the evaluated safe driving performance. Specifically, if the safe driving performance is high, it calculates a discount on the insurance premium as a reward system and generates the adjusted fare information. The input is the evaluated safe driving performance, and the output is the adjusted fare information.

[0475] Step 5:

[0476] The server then notifies the user of the adjusted fare information. This notification is sent as a push message to the user's smartphone or in-car display. The input is the adjusted fare information, and the output is the notification message to the user.

[0477] Step 6:

[0478] Through the notification messages they receive, users can check how safe their operations are and what reward system applies as a result. This provides users with an incentive to proactively improve their operations. The input is the notification message, and the output is feedback that leads to improved user behavior.

[0479] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0480] This invention is a safe driving evaluation system that combines driving data with an emotion engine that analyzes the driver's emotional state. By adding an emotional element to the evaluation of driving behavior, this system aims to perform a more precise driving evaluation and promote safe driving.

[0481] The terminal installed in the vehicle has the function of collecting normal driving data such as acceleration, speed, and location information in real time, as well as sensors to capture the driver's voice and facial expressions. As a result, emotional data of the user while driving is collected on the terminal. The emotion engine analyzes this voice and facial expression data to evaluate the emotional state of the user, such as whether they are feeling stressed or angry.

[0482] The terminal transmits this data to the server at predetermined time intervals. On the server, the driving data and emotional data are analyzed by a generating AI model. This analysis comprehensively evaluates the driver's driving behavior and emotional state, and quantifies the degree of safe driving. In some cases, the degree of safe driving may be adjusted to take into account the influence of emotional state on driving behavior.

[0483] For example, if a driver is assessed as being under high stress due to sudden changes in speed, their safe driving score will be calculated more strictly. Based on this assessment, the insurance premium is adjusted on the server according to the safe driving score. If a driver is assessed as emotionally stable and driving safely, a discount on the insurance premium will be applied.

[0484] Users can receive detailed insurance premium information based on analysis results via a mobile application. The notifications include feedback on driving behavior and emotional state, allowing users to understand and improve their driving style. In this way, the present invention supports drivers' driving behavior from an emotional perspective as well, providing a safer driving environment.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] The device collects driving data such as acceleration, speed, and location information in real time within the vehicle. It also uses voice and camera sensors to capture the user's voice and facial expressions, collecting emotional data.

[0488] Step 2:

[0489] The terminal preprocesses driving data and emotional data. Driving data is formatted and noise is removed. Emotional data is analyzed using voice and facial expression data to identify emotional states such as anger, stress, and relaxation.

[0490] Step 3:

[0491] The terminal sends pre-processed data to the server at specified time intervals. Encryption technology is applied to the data transmission to ensure the security of the communication.

[0492] Step 4:

[0493] The server receives driving and emotional data transmitted from the terminal. Each piece of data is recorded in a database and organized by driver.

[0494] Step 5:

[0495] The server uses a generated AI model to analyze the received data. Driving style is evaluated from driving data, and emotional state is evaluated from emotional data. Based on these, the degree of safe driving is quantified.

[0496] Step 6:

[0497] The server calculates insurance premiums based on driving and emotional assessments. The impact of emotions on driving risk is also considered, and a discount rate is calculated based on the degree of safe driving.

[0498] Step 7:

[0499] The server notifies the user of the calculation results. Notifications are delivered via a mobile application, providing driving and emotional feedback.

[0500] Step 8:

[0501] Users check notifications in the application and receive feedback on their driving style and emotional state. This encourages users to reflect on their driving and become more aware of the need for improvement.

[0502] (Example 2)

[0503] Next, we will describe Example 2. 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."

[0504] When evaluating safe driving, there is a need to consider not only driving data but also the driver's emotional state to perform more accurate driving assessments. However, conventional systems have difficulty incorporating emotional elements into the evaluation, and there is a challenge in that they cannot mitigate the impact of temporary emotional changes in the driver on the driving assessment.

[0505] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0506] In this invention, the server includes means for acquiring driving data and emotional data in real time, means for using a generative AI model for analyzing the acquired driving data and emotional data to perform a driving evaluation, and means for adjusting the driving evaluation based on the emotional analysis and quantifying the degree of safe driving. This enables a highly accurate safe driving evaluation that takes into account the driver's emotional state.

[0507] "Driving data" refers to data necessary to indicate the operating status of a vehicle, and includes travel speed, environmental acceleration, and location information.

[0508] "Emotional data" refers to voice and facial expression data used to evaluate the driver's emotional state.

[0509] A "generative AI model" is an artificial intelligence model that analyzes collected driving data and emotional data to perform driving evaluations.

[0510] "Driving evaluation" is a process that evaluates a driver's driving behavior based on driving data and emotional data, and calculates their level of safe driving.

[0511] "Safe driving score" is a numerical value obtained from driving evaluations and is an indicator of the safety of a driver's driving behavior.

[0512] "Charging information" refers to information related to financial burdens such as insurance premiums that are adjusted based on driving performance evaluations.

[0513] "Feedback information" refers to information provided to drivers regarding their driving behavior and emotional state, intended to help them improve these aspects.

[0514] This invention is a system for evaluating safe driving, consisting of a terminal and a server. The terminal is mounted in the vehicle and is equipped with various sensors to acquire driving data and emotional data in real time. Specifically, it uses an accelerometer, a GPS module for acquiring location information, and a camera and microphone for analyzing voice and facial expressions. Using these sensors, the terminal measures driving data such as speed, environmental acceleration, and location information, and also records the driver's voice tone and changes in facial expressions, acquiring them as emotional data.

[0515] The server receives driving and emotional data transmitted from the terminal. This data is input into a generative AI model and analyzed. The generative AI model detects abnormal driving patterns and comprehensively evaluates the associated emotional states to quantify the degree of safe driving. Based on the analysis, it generates specific feedback based on driving behavior and emotional states, and adjusts insurance premiums as needed.

[0516] Users can receive information transmitted from a server via their mobile devices and get detailed feedback on their driving behavior. For example, by inputting a prompt into an AI model such as, "Analyze the signs of stress in the driver's voice when sudden speed changes occur," it becomes clear how the driver's emotional state influenced their driving behavior.

[0517] This invention aims to provide drivers with a safer and more comfortable driving environment by conducting a safe driving evaluation that takes emotional states into account.

[0518] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0519] Step 1:

[0520] The device uses sensors mounted on the vehicle to acquire driving data (acceleration, speed, location) and emotional data (voice, facial expressions) in real time. It receives raw data from each sensor as input and generates an integrated dataset as output. This dataset contains various parameters sampled at specific time intervals.

[0521] Step 2:

[0522] The emotion engine within the device analyzes the acquired emotion data. Inputs include voice and facial expression data, which are converted into emotional states (stress, anger, joy, etc.). Outputs are labels and scores indicating the driver's emotional state. This process utilizes voice waveform pattern recognition and facial recognition algorithms.

[0523] Step 3:

[0524] The terminal transmits driving data and sentiment analysis results to the server at predetermined time intervals. The input is the analyzed dataset, and the output is the transmission of data to the server over the network. The data is compressed in real time, and the protocol is optimized to improve communication efficiency.

[0525] Step 4:

[0526] The server inputs the received data into a generating AI model to perform a comprehensive driving evaluation. The input consists of driving data and emotional data, while the output is an evaluation result representing the degree of safe driving. Here, abnormal driving patterns are identified and their impact is analyzed, and the influence of emotional states on driving behavior is calculated.

[0527] Step 5:

[0528] The server adjusts the fare information based on the safe driving level and generates feedback information for the user. The input is the safe driving level, and the output is the adjusted fare information and feedback message. Specifically, the insurance premium is calculated based on the evaluation results obtained from the generating AI model, and the content notified to the user is determined.

[0529] Step 6:

[0530] Users receive notifications sent from the server on their mobile devices and view feedback on their driving behavior and emotional state. Input is the server notification, and output is the information displayed via the user interface. Specifically, users can open the app, review the notified feedback, and identify areas for improvement in their driving style.

[0531] (Application Example 2)

[0532] Next, we will explain application example 2. In the following explanation, 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."

[0533] Conventional driving evaluation systems relied solely on driving data, making it difficult to accurately assess safe driving. In particular, they failed to consider the impact of a driver's emotional state on driving behavior, resulting in a lack of appropriate feedback to drivers. Furthermore, the absence of real-time driving assistance meant drivers lacked opportunities to improve their driving behavior on the spot.

[0534] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0535] In this invention, the server includes means for analyzing driving data and performing driving evaluations, means for analyzing the driver's emotional state, and means for integrating the analyzed emotional state and driving evaluations to calculate the degree of safe driving. This enables a comprehensive driving evaluation that takes into account the driver's emotional state. Furthermore, by providing driving assistance information in real time through a smart device, the driver can improve their driving behavior on the spot.

[0536] "Driving data" refers to information about the vehicle's operation, including acceleration, speed, and location information.

[0537] "Driving evaluation" involves analyzing acquired driving data and evaluating the quality of driving behavior numerically or qualitatively.

[0538] "Emotional state" refers to the driver's psychological and emotional state, and is evaluated based on voice and facial expression information.

[0539] "Safe driving score" is an indicator of driving safety, calculated by integrating driving evaluation and emotional state.

[0540] "Charging information" refers to information regarding insurance premiums and the financial burden associated with driving.

[0541] A "smart device" is an electronic device equipped with communication capabilities that provides information to the driver in real time.

[0542] "Driving support information" refers to information provided to promote safe driving, including suggestions for improving driving style and relaxation techniques.

[0543] The system implementing this invention evaluates the degree of safe driving using the driver's emotional state and driving data. The terminal is equipped with various sensors for acquiring driving data, and uses an accelerometer and GPS module to acquire acceleration, speed, and location information in real time. Furthermore, a microphone and camera are provided to detect the driver's voice and facial expressions using speech recognition and image processing technology. This data is collected in real time via smart devices such as smart glasses and analyzed via a cloud server.

[0544] The server uses a generative AI model to analyze driving data and emotional state. This analysis not only calculates the level of safe driving but also generates relaxation suggestions and suggestions for improving the driver's driving style. Drivers can receive these suggestions through the display of their smart device. For example, if the driver's stress level is high, a suggestion such as "Take a deep breath and relax" will be displayed.

[0545] The hardware used here includes smart glasses, in-car cameras, and microphones, while cloud computing platforms (e.g., AWS, Google Cloud) are utilized for data processing and computation. On the software side, generative AI models (e.g., GPT-4) are used for analysis.

[0546] A concrete example of a prompt message might be, "Analyze the emotional state of the driver currently at the wheel. If stress or anger levels are high, generate specific suggestions for relaxation." Such prompts are used to provide the driver with appropriate feedback in real time.

[0547] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0548] Step 1:

[0549] The terminal acquires driving data. The device uses accelerometers, speed sensors, GPS modules, etc., within the vehicle to collect acceleration, speed, and position information. Inputs are data from each sensor, and outputs are recordings of driving data within the terminal.

[0550] Step 2:

[0551] The device acquires the driver's emotional data. It uses a microphone to collect the driver's voice and a camera to capture facial expression data. The input is audio and video data, and the output is emotional data stored on the device.

[0552] Step 3:

[0553] The device transmits acquired driving data and emotional data to a cloud server. The data is sent in real time via an encrypted communication channel. The input is driving data and emotional data, and the output is the transmission of data to the cloud server.

[0554] Step 4:

[0555] The server analyzes driving data and emotional data. Using a generative AI model, it analyzes the input data and calculates the safe driving score. The input is driving data and emotional data, and the output is a numerical value representing the safe driving score, which is the result of the analysis.

[0556] Step 5:

[0557] The server generates feedback for the driver based on the analysis results. Based on the prompt messages, it generates relaxation suggestions and suggestions for improving driving style. The input is numerical data from the analysis, and the output is a specific feedback message.

[0558] Step 6:

[0559] The server sends the generated feedback to the smart device. The driver can receive and confirm the feedback on the smart glasses display. The input is the feedback message, and the output is the display on the smart device.

[0560] Step 7:

[0561] The system adjusts the driving style based on user feedback. Users read the feedback messages and use them to improve their driving safety. The input is the content of the feedback, and the output is the change in the user's behavior.

[0562] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0563] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0564] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0565] [Fourth Embodiment]

[0566] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0567] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0568] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0569] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0570] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0571] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0572] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0573] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0574] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0575] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0576] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0577] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0578] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0579] To implement this invention, a system is constructed that acquires and analyzes driving data, evaluates safe driving, and adjusts insurance premiums. The processing of this program is described below in natural language.

[0580] A terminal installed in the vehicle collects real-time data on the driver's behavior while driving. This data includes the vehicle's acceleration, speed, location, brake usage, and steering wheel angle. The terminal transmits the collected data to a server at predetermined time intervals. Before transmission, the data is pre-processed to maintain its accuracy.

[0581] The server receives data sent from the terminal and inputs it into a dedicated generative AI model. This model combines past driving history and statistical driving patterns to evaluate the driver's driving behavior. As a result of the evaluation, the driver's level of safe driving is quantified, and each driver's driving style and risk factors can be quantitatively analyzed.

[0582] Based on the analysis results, the server adjusts the driver's insurance premium. Specifically, drivers who are evaluated as practicing safe driving will receive a discount on their next insurance premium. The server notifies the user of the adjusted insurance premium, and the user can check this information through a mobile application or other electronic means.

[0583] For example, suppose a driver brakes very infrequently over a month and always drives within the legal speed limit. This kind of safe driving data is highly rated on the server, resulting in a discount on the following month's insurance premium. This notification is sent to the user, who receives specific feedback in the application such as, "Your insurance premium will be discounted by 15% because you drove safely this month."

[0584] Thus, through this invention, users will have an incentive to drive safely and will be able to proactively work to improve safety in their future driving.

[0585] The following describes the processing flow.

[0586] Step 1:

[0587] The terminal collects vehicle driving data in real time. Using sensors and GPS, it obtains detailed driving information such as acceleration, speed, location, and brake usage frequency.

[0588] Step 2:

[0589] The operating data collected by the terminal is preprocessed. Unnecessary noise is filtered out, and interrupted data is filled in to prepare the data for analysis.

[0590] Step 3:

[0591] The terminal encrypts pre-processed driving data and sends it to the server at regular intervals. Appropriate security protocols are applied during transmission to ensure the security of the communication.

[0592] Step 4:

[0593] The server receives driving data transmitted from the terminal and stores it in a database. The received data is managed individually for each driver.

[0594] Step 5:

[0595] The server inputs accumulated driving data into an AI model to analyze the driver's driving style. The model quantifies the degree of safe driving and the frequency of risky behaviors, and evaluates the driver's driving performance.

[0596] Step 6:

[0597] The server calculates the driver's insurance premium based on their driving performance. If the driver has a high level of safe driving, a discount rate is calculated and applied to the next bill.

[0598] Step 7:

[0599] The server notifies the user of the calculated insurance premium information. The user receives the notification through a mobile application and can view detailed driving feedback.

[0600] Step 8:

[0601] The system checks notifications sent by users to understand their driving style and whether discounts are applicable. It then uses the feedback to improve future driving behavior.

[0602] (Example 1)

[0603] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0604] There is a need to promote safe driving and to accurately evaluate driving data to set insurance premiums appropriate for individual users, and to make rapid premium adjustments based on that evaluation. However, conventional systems have the problem of insufficient collection and evaluation of driving data, and therefore fail to achieve fair premium adjustments based on individual driving behavior.

[0605] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0606] In this invention, the server includes means for preprocessing driving data, means for performing driving evaluations using a generated AI model, and means for adjusting fare information. This enables accurate and efficient analysis of collected driving data and fair adjustment of insurance premiums based on individual safe driving levels.

[0607] "Driving data" refers to various types of information acquired during driving, such as vehicle acceleration, speed, location information, braking status, and steering angle.

[0608] "Preprocessing" refers to processes performed to maintain the accuracy and consistency of acquired driving data, such as detecting and correcting abnormal values, smoothing data, and compressing data as needed.

[0609] A "generative AI model" is a model constructed using machine learning algorithms and is used to evaluate driving behavior by referring to past driving data and statistical patterns.

[0610] "Driving evaluation" refers to the process of analyzing a driver's driving behavior based on acquired driving data and quantifying their level of safe driving.

[0611] "Charging information" refers to information regarding financial burdens such as insurance premiums that are adjusted based on the driver's driving performance evaluation.

[0612] "Notification" refers to the process or method of communicating adjusted fare information and driving evaluation results to users.

[0613] This system consists of a terminal installed in the vehicle and a server connected via a network. The terminal uses various sensors to collect real-time data on the driver's behavior while driving. This data includes vehicle acceleration, speed, and location information obtained using an accelerometer and GPS module, as well as information on vehicle braking usage and steering angle. This allows for the collection of highly accurate driving data.

[0614] The terminal does not immediately transmit the collected driving data, but first performs data preprocessing. This includes removing outliers, smoothing the data, and compressing it as needed to maintain data quality. Performing this process improves the accuracy of subsequent analysis.

[0615] The pre-processed data is sent from the terminal to the server via a secure communication protocol (e.g., HTTPS). After receiving this data, the server inputs it into the generating AI model. The generating AI model uses a machine learning algorithm and operates based on the prompt message "Evaluate the driver's driving patterns and calculate the degree of safe driving." This model evaluates the driver's driving behavior by referring to past driving history and statistical data on driving patterns.

[0616] Based on the evaluation results, the server quantifies the driver's safe driving performance and adjusts individual insurance premiums. The adjusted premium information is then notified to the user from the server. Users can receive this information via a mobile application or email and check for details about future premium discounts.

[0617] For example, if a driver drives within the legal speed limit for a month and brakes very infrequently, this data will be highly rated on the server. As a result, the user will receive a notification that their insurance premium will be discounted by 15% the following month. This allows users to understand specifically how their safe driving is evaluated and how it is reflected in their insurance premiums. Through this system, an incentive for safe driving is provided, and drivers will strive to drive more safely.

[0618] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0619] Step 1:

[0620] The device uses various sensors to collect real-time data on the driver's behavior while driving. Specifically, it uses an accelerometer and GPS module to acquire data such as vehicle acceleration, speed, location, brake usage, and steering angle. This input data reflects the driving situation in detail.

[0621] Step 2:

[0622] The terminal performs preprocessing on the collected raw data. This step involves detecting and correcting data anomalies, denoising, and smoothing the data. Compression may also be performed to reduce data size as needed. As a result, it outputs high-quality, consistent data.

[0623] Step 3:

[0624] The terminal sends the prepared data to the server at predetermined time intervals. To ensure secure communication, the data is transmitted via the HTTPS protocol. The output is secure operational data.

[0625] Step 4:

[0626] The server receives data sent from the terminal. This data is input into the generating AI model. The server analyzes the driving data using the generating AI model, while referring to past driving data and statistical models. The evaluation is performed based on the prompt message "Evaluate the driver's driving pattern and calculate the degree of safe driving." The output after the analysis is data that quantifies the driver's degree of safe driving.

[0627] Step 5:

[0628] The server calculates and adjusts the driver's insurance premium based on numerical data on safe driving performance. Specifically, it applies a discount to the insurance premium if the driver's driving behavior is safe, based on pre-set evaluation criteria. The output is the adjusted insurance premium information.

[0629] Step 6:

[0630] The server notifies the user of the adjusted insurance premium information. This notification is delivered via a mobile application, email, or other means. It is crucial in this step to provide the user with specific feedback on the adjustment results. The output to the user is a notification message detailing the discount.

[0631] Throughout each step of the process, the terminal and server work together to scrutinize driving data, enabling fair evaluation and fare adjustments based on the actions of individual drivers.

[0632] (Application Example 1)

[0633] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] Conventional systems for evaluating operational information only analyze the acquired data, making it difficult to conduct detailed evaluations based on individual machine operations or propose incentives. This results in problems such as insufficient promotion of safe operation and inadequate feedback to operators.

[0635] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0636] In this invention, the server includes means for acquiring operational information, means for analyzing the acquired operational information and performing operational evaluations, means for adjusting fare information based on the operational evaluations, means for notifying the adjusted fare information, means for evaluating machine operations in real time during operation, and means for proposing a reward system based on the evaluation results. This makes it possible to specifically evaluate safe operation and provide a reward system tailored to individual machine operations.

[0637] "Operational information" refers to all data acquired when a vehicle or automobile is in motion, and specifically includes acceleration, speed, and location information.

[0638] "Analysis" refers to the process of evaluating specific machine operations and operating patterns in detail based on acquired operational information.

[0639] "Operational evaluation" refers to quantifying or assigning numerical values ​​to the safety and efficiency of vehicle operations based on analyzed operational information.

[0640] "Fee information" refers to information on insurance premiums and other operational costs determined based on the vehicle's operational evaluation.

[0641] "Machine operation" refers to the specific operations and actions performed by the vehicle while it is in operation, and is subject to real-time evaluation.

[0642] A "reward system" refers to a system that includes incentives and discounts proposed based on evaluation results, with the aim of encouraging drivers to operate safely.

[0643] In order to implement this invention, it is necessary to construct a system that acquires and analyzes operational information and proposes adjustments to fare information and a compensation system based on operational evaluations.

[0644] First, a terminal installed in the vehicle collects operational information in real time from vehicles and cars in motion. This operational information includes acceleration, speed, location information, and data obtained from various sensors within the vehicle. The terminal then transmits the data to a server at predetermined time intervals. Before transmitting the data, it undergoes preprocessing such as outlier removal and noise filtering.

[0645] The server analyzes the received operational information and evaluates it using a generated AI model. This AI model uses past operational history data and statistical operational patterns for its evaluation. The evaluation results are provided as a numerical safety score indicating how safe the operation is.

[0646] Based on the evaluation results, the server proposes a reward system. This proposal includes information on insurance premium discounts as an incentive for safe driving, and the server notifies the user. Users can check this information via their smartphone or in-car display.

[0647] For example, suppose a user has been driving safely for a month, staying within the legal speed limit and not using sudden braking. This safe driving data will be highly rated, and as a result, their insurance premium will be discounted the following month. The user will receive a notification stating, "Your current driving style is likely to qualify for a 5% discount on your insurance premium."

[0648] A concrete example of a prompt message would be: "Maintain acceleration of 0.05 m / s² or less, driving speed: 50 km / h, steering angle change: stable — Perform a safe driving assessment." In this way, the system can provide users with incentives for safe driving and promote improvements in driving safety.

[0649] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0650] Step 1:

[0651] The terminal acquires real-time operational information from various sensors mounted on the vehicle. This operational information includes acceleration, speed, and location data. The acquired data is filtered to remove outliers, improving its accuracy. The input is raw data from the sensors, and the output is refined operational information.

[0652] Step 2:

[0653] The terminal transmits refined operational information to the server at predetermined time intervals. During transmission, the data is aggregated into a predetermined format and transferred using a communication protocol. The input is the refined operational information, and the output is the operational information packet sent to the server.

[0654] Step 3:

[0655] The server receives operational information transmitted from the terminal and inputs it into the generated AI model. This model evaluates operations using past operational history data and statistical operational patterns. The input is operational information transmitted to the server, and the output is a numerical representation of the evaluated level of safe operation.

[0656] Step 4:

[0657] The server adjusts the fare information based on the evaluated safe driving performance. Specifically, if the safe driving performance is high, it calculates a discount on the insurance premium as a reward system and generates the adjusted fare information. The input is the evaluated safe driving performance, and the output is the adjusted fare information.

[0658] Step 5:

[0659] The server then notifies the user of the adjusted fare information. This notification is sent as a push message to the user's smartphone or in-car display. The input is the adjusted fare information, and the output is the notification message to the user.

[0660] Step 6:

[0661] Through the notification messages they receive, users can check how safe their operations are and what reward system applies as a result. This provides users with an incentive to proactively improve their operations. The input is the notification message, and the output is feedback that leads to improved user behavior.

[0662] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0663] This invention is a safe driving evaluation system that combines driving data with an emotion engine that analyzes the driver's emotional state. By adding an emotional element to the evaluation of driving behavior, this system aims to perform a more precise driving evaluation and promote safe driving.

[0664] The terminal installed in the vehicle has the function of collecting normal driving data such as acceleration, speed, and location information in real time, as well as sensors to capture the driver's voice and facial expressions. As a result, emotional data of the user while driving is collected on the terminal. The emotion engine analyzes this voice and facial expression data to evaluate the emotional state of the user, such as whether they are feeling stressed or angry.

[0665] The terminal transmits this data to the server at predetermined time intervals. On the server, the driving data and emotional data are analyzed by a generating AI model. This analysis comprehensively evaluates the driver's driving behavior and emotional state, and quantifies the degree of safe driving. In some cases, the degree of safe driving may be adjusted to take into account the influence of emotional state on driving behavior.

[0666] For example, if a driver is assessed as being under high stress due to sudden changes in speed, their safe driving score will be calculated more strictly. Based on this assessment, the insurance premium is adjusted on the server according to the safe driving score. If a driver is assessed as emotionally stable and driving safely, a discount on the insurance premium will be applied.

[0667] Users can receive detailed insurance premium information based on analysis results via a mobile application. The notifications include feedback on driving behavior and emotional state, allowing users to understand and improve their driving style. In this way, the present invention supports drivers' driving behavior from an emotional perspective as well, providing a safer driving environment.

[0668] The following describes the processing flow.

[0669] Step 1:

[0670] The device collects driving data such as acceleration, speed, and location information in real time within the vehicle. It also uses voice and camera sensors to capture the user's voice and facial expressions, collecting emotional data.

[0671] Step 2:

[0672] The terminal preprocesses driving data and emotional data. Driving data is formatted and noise is removed. Emotional data is analyzed using voice and facial expression data to identify emotional states such as anger, stress, and relaxation.

[0673] Step 3:

[0674] The terminal sends pre-processed data to the server at specified time intervals. Encryption technology is applied to the data transmission to ensure the security of the communication.

[0675] Step 4:

[0676] The server receives driving and emotional data transmitted from the terminal. Each piece of data is recorded in a database and organized by driver.

[0677] Step 5:

[0678] The server uses a generated AI model to analyze the received data. Driving style is evaluated from driving data, and emotional state is evaluated from emotional data. Based on these, the degree of safe driving is quantified.

[0679] Step 6:

[0680] The server calculates insurance premiums based on driving and emotional assessments. The impact of emotions on driving risk is also considered, and a discount rate is calculated based on the degree of safe driving.

[0681] Step 7:

[0682] The server notifies the user of the calculation results. Notifications are delivered via a mobile application, providing driving and emotional feedback.

[0683] Step 8:

[0684] Users check notifications in the application and receive feedback on their driving style and emotional state. This encourages users to reflect on their driving and become more aware of the need for improvement.

[0685] (Example 2)

[0686] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] When evaluating safe driving, there is a need to consider not only driving data but also the driver's emotional state to perform more accurate driving assessments. However, conventional systems have difficulty incorporating emotional elements into the evaluation, and there is a challenge in that they cannot mitigate the impact of temporary emotional changes in the driver on the driving assessment.

[0688] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0689] In this invention, the server includes means for acquiring driving data and emotional data in real time, means for using a generative AI model for analyzing the acquired driving data and emotional data to perform a driving evaluation, and means for adjusting the driving evaluation based on the emotional analysis and quantifying the degree of safe driving. This enables a highly accurate safe driving evaluation that takes into account the driver's emotional state.

[0690] "Driving data" refers to data necessary to indicate the operating status of a vehicle, and includes travel speed, environmental acceleration, and location information.

[0691] "Emotional data" refers to voice and facial expression data used to evaluate the driver's emotional state.

[0692] A "generative AI model" is an artificial intelligence model that analyzes collected driving data and emotional data to perform driving evaluations.

[0693] "Driving evaluation" is a process that evaluates a driver's driving behavior based on driving data and emotional data, and calculates their level of safe driving.

[0694] "Safe driving score" is a numerical value obtained from driving evaluations and is an indicator of the safety of a driver's driving behavior.

[0695] "Charging information" refers to information related to financial burdens such as insurance premiums that are adjusted based on driving performance evaluations.

[0696] "Feedback information" refers to information provided to drivers regarding their driving behavior and emotional state, intended to help them improve these aspects.

[0697] This invention is a system for evaluating safe driving, consisting of a terminal and a server. The terminal is mounted in the vehicle and is equipped with various sensors to acquire driving data and emotional data in real time. Specifically, it uses an accelerometer, a GPS module for acquiring location information, and a camera and microphone for analyzing voice and facial expressions. Using these sensors, the terminal measures driving data such as speed, environmental acceleration, and location information, and also records the driver's voice tone and changes in facial expressions, acquiring them as emotional data.

[0698] The server receives driving and emotional data transmitted from the terminal. This data is input into a generative AI model and analyzed. The generative AI model detects abnormal driving patterns and comprehensively evaluates the associated emotional states to quantify the degree of safe driving. Based on the analysis, it generates specific feedback based on driving behavior and emotional states, and adjusts insurance premiums as needed.

[0699] Users can receive information transmitted from a server via their mobile devices and get detailed feedback on their driving behavior. For example, by inputting a prompt into an AI model such as, "Analyze the signs of stress in the driver's voice when sudden speed changes occur," it becomes clear how the driver's emotional state influenced their driving behavior.

[0700] This invention aims to provide drivers with a safer and more comfortable driving environment by conducting a safe driving evaluation that takes emotional states into account.

[0701] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0702] Step 1:

[0703] The device uses sensors mounted on the vehicle to acquire driving data (acceleration, speed, location) and emotional data (voice, facial expressions) in real time. It receives raw data from each sensor as input and generates an integrated dataset as output. This dataset contains various parameters sampled at specific time intervals.

[0704] Step 2:

[0705] The emotion engine within the device analyzes the acquired emotion data. Inputs include voice and facial expression data, which are converted into emotional states (stress, anger, joy, etc.). Outputs are labels and scores indicating the driver's emotional state. This process utilizes voice waveform pattern recognition and facial recognition algorithms.

[0706] Step 3:

[0707] The terminal transmits driving data and sentiment analysis results to the server at predetermined time intervals. The input is the analyzed dataset, and the output is the transmission of data to the server over the network. The data is compressed in real time, and the protocol is optimized to improve communication efficiency.

[0708] Step 4:

[0709] The server inputs the received data into a generating AI model to perform a comprehensive driving evaluation. The input consists of driving data and emotional data, while the output is an evaluation result representing the degree of safe driving. Here, abnormal driving patterns are identified and their impact is analyzed, and the influence of emotional states on driving behavior is calculated.

[0710] Step 5:

[0711] The server adjusts the fare information based on the safe driving level and generates feedback information for the user. The input is the safe driving level, and the output is the adjusted fare information and feedback message. Specifically, the insurance premium is calculated based on the evaluation results obtained from the generating AI model, and the content notified to the user is determined.

[0712] Step 6:

[0713] Users receive notifications sent from the server on their mobile devices and view feedback on their driving behavior and emotional state. Input is the server notification, and output is the information displayed via the user interface. Specifically, users can open the app, review the notified feedback, and identify areas for improvement in their driving style.

[0714] (Application Example 2)

[0715] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0716] Conventional driving evaluation systems relied solely on driving data, making it difficult to accurately assess safe driving. In particular, they failed to consider the impact of a driver's emotional state on driving behavior, resulting in a lack of appropriate feedback to drivers. Furthermore, the absence of real-time driving assistance meant drivers lacked opportunities to improve their driving behavior on the spot.

[0717] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0718] In this invention, the server includes means for analyzing driving data and performing driving evaluations, means for analyzing the driver's emotional state, and means for integrating the analyzed emotional state and driving evaluations to calculate the degree of safe driving. This enables a comprehensive driving evaluation that takes into account the driver's emotional state. Furthermore, by providing driving assistance information in real time through a smart device, the driver can improve their driving behavior on the spot.

[0719] "Driving data" refers to information about the vehicle's operation, including acceleration, speed, and location information.

[0720] "Driving evaluation" involves analyzing acquired driving data and evaluating the quality of driving behavior numerically or qualitatively.

[0721] "Emotional state" refers to the driver's psychological and emotional state, and is evaluated based on voice and facial expression information.

[0722] "Safe driving score" is an indicator of driving safety, calculated by integrating driving evaluation and emotional state.

[0723] "Charging information" refers to information regarding insurance premiums and the financial burden associated with driving.

[0724] A "smart device" is an electronic device equipped with communication capabilities that provides information to the driver in real time.

[0725] "Driving support information" refers to information provided to promote safe driving, including suggestions for improving driving style and relaxation techniques.

[0726] The system implementing this invention evaluates the degree of safe driving using the driver's emotional state and driving data. The terminal is equipped with various sensors for acquiring driving data, and uses an accelerometer and GPS module to acquire acceleration, speed, and location information in real time. Furthermore, a microphone and camera are provided to detect the driver's voice and facial expressions using speech recognition and image processing technology. This data is collected in real time via smart devices such as smart glasses and analyzed via a cloud server.

[0727] The server uses a generative AI model to analyze driving data and emotional state. This analysis not only calculates the level of safe driving but also generates relaxation suggestions and suggestions for improving the driver's driving style. Drivers can receive these suggestions through the display of their smart device. For example, if the driver's stress level is high, a suggestion such as "Take a deep breath and relax" will be displayed.

[0728] The hardware used here includes smart glasses, in-car cameras, and microphones, while cloud computing platforms (e.g., AWS, Google Cloud) are utilized for data processing and computation. On the software side, generative AI models (e.g., GPT-4) are used for analysis.

[0729] A concrete example of a prompt message might be, "Analyze the emotional state of the driver currently at the wheel. If stress or anger levels are high, generate specific suggestions for relaxation." Such prompts are used to provide the driver with appropriate feedback in real time.

[0730] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0731] Step 1:

[0732] The terminal acquires driving data. The device uses accelerometers, speed sensors, GPS modules, etc., within the vehicle to collect acceleration, speed, and position information. Inputs are data from each sensor, and outputs are recordings of driving data within the terminal.

[0733] Step 2:

[0734] The device acquires the driver's emotional data. It uses a microphone to collect the driver's voice and a camera to capture facial expression data. The input is audio and video data, and the output is emotional data stored on the device.

[0735] Step 3:

[0736] The device transmits acquired driving data and emotional data to a cloud server. The data is sent in real time via an encrypted communication channel. The input is driving data and emotional data, and the output is the transmission of data to the cloud server.

[0737] Step 4:

[0738] The server analyzes driving data and emotional data. Using a generative AI model, it analyzes the input data and calculates the safe driving score. The input is driving data and emotional data, and the output is a numerical value representing the safe driving score, which is the result of the analysis.

[0739] Step 5:

[0740] The server generates feedback for the driver based on the analysis results. Based on the prompt messages, it generates relaxation suggestions and suggestions for improving driving style. The input is numerical data from the analysis, and the output is a specific feedback message.

[0741] Step 6:

[0742] The server sends the generated feedback to the smart device. The driver can receive and confirm the feedback on the smart glasses display. The input is the feedback message, and the output is the display on the smart device.

[0743] Step 7:

[0744] The system adjusts the driving style based on user feedback. Users read the feedback messages and use them to improve their driving safety. The input is the content of the feedback, and the output is the change in the user's behavior.

[0745] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0746] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0747] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0748] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0749] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0750] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0751] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0752] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0753] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0754] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0755] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0756] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0757] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0758] 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.

[0759] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0760] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0761] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0762] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0763] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0764] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0765] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0766] The following is further disclosed regarding the embodiments described above.

[0767] (Claim 1)

[0768] A device for acquiring operating data,

[0769] A device that analyzes acquired operating data and performs operational evaluations,

[0770] A device that adjusts fare information based on operational evaluations,

[0771] A device that notifies the adjusted fare information,

[0772] A system that includes this.

[0773] (Claim 2)

[0774] The system according to claim 1, comprising means for quantifying the degree of safe driving based on driving evaluation.

[0775] (Claim 3)

[0776] The system according to claim 1, wherein the acquired driving data includes acceleration, velocity, and position information.

[0777] "Example 1"

[0778] (Claim 1)

[0779] Means for acquiring driving data,

[0780] A means for preprocessing the acquired driving data,

[0781] A means of performing operational evaluation using an AI model generated by analyzing pre-processed operational data,

[0782] A means of adjusting fare information based on driving evaluations,

[0783] A means of notifying users of the adjusted pricing information,

[0784] A system that includes this.

[0785] (Claim 2)

[0786] The system according to claim 1, comprising means for quantifying the degree of safe driving based on driving evaluation and providing feedback to the user.

[0787] (Claim 3)

[0788] The system according to claim 1, wherein the acquired driving data includes acceleration, speed, position information, braking usage, and steering angle.

[0789] "Application Example 1"

[0790] (Claim 1)

[0791] Means of obtaining operational information,

[0792] A means of analyzing acquired operational information and performing operational evaluations,

[0793] A means of adjusting fare information based on operational evaluations,

[0794] A means of notifying adjusted pricing information,

[0795] A means of evaluating machine operation in real time during operation,

[0796] A means of proposing a compensation system based on evaluation results,

[0797] A system that includes this.

[0798] (Claim 2)

[0799] The system according to claim 1, comprising means for quantifying the degree of safe operation based on operational evaluation.

[0800] (Claim 3)

[0801] The system according to claim 1, wherein the operational information to be acquired includes acceleration, speed, and location information.

[0802] "Example 2 of combining an emotion engine"

[0803] (Claim 1)

[0804] A means of acquiring driving data and emotional data in real time,

[0805] A means of using a generative AI model to perform driving evaluation by analyzing acquired driving data and emotional data,

[0806] A means of adjusting driving evaluations based on emotion analysis and quantifying the degree of safe driving,

[0807] A means of adjusting toll information based on a quantified level of safe driving,

[0808] A means of notifying users of adjusted pricing information and feedback information,

[0809] A system that includes this.

[0810] (Claim 2)

[0811] The system according to claim 1, wherein the driving data collected includes speed, environmental acceleration, and location information.

[0812] (Claim 3)

[0813] The system according to claim 1, which uses voice and facial expression analysis to evaluate the emotional state of the user.

[0814] "Application example 2 when combining with an emotional engine"

[0815] (Claim 1)

[0816] Means for acquiring driving data,

[0817] A means of analyzing acquired driving data and performing driving evaluations,

[0818] A means of analyzing the driver's emotional state,

[0819] A means for integrating analyzed emotional states and driving evaluations to calculate the degree of safe driving,

[0820] A means of adjusting toll information based on the degree of safe driving,

[0821] A means of notifying adjusted pricing information,

[0822] A means of providing drivers with real-time driving assistance information using smart devices,

[0823] A system that includes this.

[0824] (Claim 2)

[0825] The system according to claim 1, comprising means for generating relaxation suggestions according to an emotional state.

[0826] (Claim 3)

[0827] The system according to claim 1, wherein the acquired driving data includes acceleration, speed, location information, voice, and facial expression information. [Explanation of Symbols]

[0828] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A device for acquiring operating data, A device that analyzes acquired operating data and performs operational evaluation, A device that adjusts fare information based on operational evaluations, A device that notifies the adjusted fare information, A system that includes this.

2. The system according to claim 1, comprising means for quantifying the degree of safe driving based on driving evaluation.

3. The system according to claim 1, wherein the acquired driving data includes acceleration, velocity, and position information.

Images