system

Abstract

We provide the system. [Solution] A location acquisition means for acquiring highly accurate location information, A communication means for transmitting collected driving behavior data, A data analysis means for analyzing received data and evaluating driving ability, An evaluation method for diagnosing driving ability based on analysis results, A means for generating reports that provide diagnostic results to the user, A reporting method for sending reports, A driving ability diagnostic system including...

Description

Technical Field

[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, and includes steps of 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 in 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 an aging society, the increase in traffic accidents caused by elderly people driving has become a social problem. In particular, the decline in the cognitive ability and reaction speed of the elderly reduces their driving ability and increases the risk of accidents. However, it is difficult for the elderly to accurately grasp their own driving ability, and there is a lack of objective criteria for judging whether to voluntarily return their driver's license. As a result, there is a problem that the safety is impaired when the elderly continue to drive unnecessarily.

Means for Solving the Problems

[0005] To solve this problem, the present invention provides a location acquisition means for acquiring high-precision location information, a communication means for collecting and transmitting driving behavior data, and a data analysis means for analyzing the received data and evaluating driving ability. Furthermore, it includes an evaluation means for diagnosing driving ability based on the analysis results, a report generation means for providing the diagnosis results to the user, and a reporting means for transmitting the report. This configuration realizes a system that visualizes the driving ability of elderly people and supports voluntary license surrender through safety evaluation.

[0006] "Position acquisition means" refers to devices and technologies for acquiring highly accurate positional information, and has the function of determining the precise location of a vehicle while it is in motion.

[0007] "Communication means" refers to technology or equipment that has the function of transmitting collected driving behavior data to an external system such as a server, and that ensures the secure transfer of data.

[0008] "Data analysis means" refers to algorithms and processing systems for evaluating driving ability based on received driving data, and aims to clarify the characteristics of driving behavior through data analysis.

[0009] "Evaluation means" refers to systems and technologies used to diagnose driving ability based on results obtained through data analysis, and have the function of determining the driver's safety and driving suitability.

[0010] "Report generation means" refers to software or a process for automatically creating a diagnostic report based on the results of the driving ability evaluation, and provides information to the user in an easy-to-understand manner.

[0011] "Reporting means" refers to technology or equipment equipped with the function of transmitting generated reports to users or relevant organizations, and is intended to accurately deliver necessary information. [Brief explanation of the drawing]

[0012] [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]

[0013] 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.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a labeled 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.

[0016] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a labeled 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.

[0018] In the following embodiments, a labeled 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.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] The driving ability diagnostic system of the present invention aims to improve traffic safety by monitoring and analyzing the driving behavior of elderly people in real time and appropriately evaluating their abilities. This system consists of a terminal installed in the vehicle, a central server, and a user terminal.

[0034] First, the terminal is integrated into the vehicle and uses various sensors such as GPS and accelerometers to acquire highly accurate location information and driving data (speed, sudden acceleration, sudden deceleration, lane changes, etc.). This information is initially processed by the vehicle's computer and then transmitted to a central server via the internet.

[0035] The central server stores received driving data and uses an analysis engine to evaluate the characteristics of driving behavior. By utilizing generative AI and machine learning algorithms, it extracts features related to reaction speed and cognitive ability, and scores safe driving ability. This evaluation criterion incorporates comparisons based on existing traffic databases and average driving characteristics of people of the same age and gender.

[0036] The evaluation results are automatically generated as a diagnostic report, and the details are sent to the user's terminal. Through this report, the user can understand the current state of their driving ability and consult with family or local government as needed. In addition, this system has a function to issue appropriate alerts when it detects data indicating a decline in driving ability, providing the user with an opportunity to consider changing their mindset or surrendering their license.

[0037] As a concrete example, data is collected from an elderly person's daily commute. From the transmitted data, the server analyzes detailed information such as changes in entry speed at intersections and braking timing, and notifies the user in a report if a decline in attention during recent driving has been observed. The user and their family can then consider this information to develop safer driving habits or prepare for surrendering their driver's license.

[0038] In this way, the system of the present invention visualizes the driving ability of elderly people and contributes to improving traffic safety and reducing risks.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device uses various sensors installed in the vehicle to collect data in real time while driving. The data acquired includes location information (GPS), speed, acceleration, brake usage frequency, and whether or not lane changes occurred.

[0042] Step 2:

[0043] The terminal processes the collected data, bundles it into data packets, and sends them to the server via the internet. During this process, data compression and encryption are performed to ensure communication efficiency and security.

[0044] Step 3:

[0045] The server saves the received data to a database and immediately starts data analysis using the analysis engine. The purpose of the analysis is to identify the driver's driving behavior and detect trends and anomalies by comparing it with past data.

[0046] Step 4:

[0047] The server uses AI generation to calculate indicators of reaction speed and attention. This algorithm considers factors such as the frequency of lane departures, sudden braking, and speed fluctuations to generate a safe driving score.

[0048] Step 5:

[0049] The server generates a diagnostic report based on the analysis results. The report includes driving safety levels, alerts for specific driving behaviors, and overall driving trends, all presented in an easy-to-understand format.

[0050] Step 6:

[0051] The server sends the generated report to the user's terminal. This allows the driver and related parties to view the results and take necessary corrective actions.

[0052] Step 7:

[0053] Based on the reports received, users will evaluate their own driving abilities and consult with their families and local governments to consider future options, including surrendering their licenses. This information will be used to improve their future driving behavior.

[0054] (Example 1)

[0055] 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."

[0056] The increase in human error by elderly drivers has become a social problem, raising the risk of traffic accidents. However, there are insufficient means for drivers themselves to objectively assess their own driving abilities and take appropriate action. There is a need for concrete approaches that allow elderly drivers to visualize their own driving abilities, raise their awareness of safe driving, and, when necessary, encourage them to surrender their licenses.

[0057] 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.

[0058] In this invention, the server includes communication means for initial processing and transmitting driving behavior data, data analysis means for storing the received data and analyzing it using a generation AI model and machine learning techniques, and evaluation means for evaluating driving ability via an analysis engine and providing a score. This makes it possible for drivers to understand their own driving characteristics in detail and consider specific improvement measures for safe driving.

[0059] "Position acquisition means" refers to technology for accurately measuring the current location of a vehicle, utilizing GPS and related location information technologies.

[0060] "Communication means" refers to the technology that processes collected driving data and transmits it to a server via a network such as the internet.

[0061] "Data analysis means" refers to a technology that accumulates received driving data and analyzes it in detail using generated AI models and machine learning algorithms to evaluate the characteristics of driving behavior.

[0062] "Evaluation methods" refer to technologies for evaluating driving ability based on analysis results and scoring that ability, and they play a role in determining the driver's safety by comparing it with existing data.

[0063] "Report generation means" refers to technology that automatically generates detailed reports including evaluation results of driving ability and suggestions for improvement.

[0064] "Reporting means" refers to a technology that transmits the generated report electronically to the user's terminal, providing information to the driver.

[0065] This invention is a driving ability diagnostic system that appropriately evaluates the driving ability of elderly people and aims to improve traffic safety. This system mainly consists of terminals, a central server, and user terminals.

[0066] terminal

[0067] The terminal is fixed to the vehicle and collects location information and driving data using various sensors such as GPS and accelerometers. In this process, the in-vehicle computer performs initial processing to prepare the data format and filter out unnecessary data. For example, information on vehicle speed, sudden acceleration, sudden deceleration, and lane changes is collected. The data is then securely transmitted to a central server via an internet connection.

[0068] server

[0069] The server stores data received from terminals in a database and evaluates its characteristics using an analysis engine. This analysis engine utilizes generative AI models and machine learning algorithms to extract features related to driving behavior. For example, it uses frameworks such as TENSORFLOW® and PyTorch to score indicators related to reaction speed and cognitive ability. It also compares the data with existing traffic data and driving characteristics of the same age group.

[0070] User

[0071] Users can receive a detailed evaluation report of their driving ability on their device. This report includes a score for their driving characteristics and identifies areas for improvement, which can be shared with family and traffic professionals to consider countermeasures. For example, the report might prompt them to consider training at a driving school. Another specific example is a prompt to the generating AI model that reads, "Analyze the driving data of a 70-year-old driver at an intersection, extract characteristics related to reaction speed and cognitive ability, and evaluate their safe driving ability."

[0072] This invention enables drivers to gain a detailed understanding of their own driving abilities and take specific improvement measures for safer driving as needed. This will raise safety awareness among all drivers, including the elderly, and contribute to improving traffic safety.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The terminal collects driving data using various sensors installed in the vehicle. It receives location information from GPS and motion data from accelerometers as input and performs initial processing. This initial processing involves data format conversion and filtering of unnecessary data, generating organized driving data. The output is this organized driving data.

[0076] Step 2:

[0077] The terminal transmits the initially processed operating data to a central server via the internet. The terminal uses organized operating data as input and transfers it to the server via communication. Specifically, a protocol is used to transmit data while maintaining security through encryption. The output is the operating data received by the server.

[0078] Step 3:

[0079] The server stores the received operating data in a database. It receives operating data from terminals as input and uses a database management system to efficiently organize and store the data. The output is the entries in the stored database.

[0080] Step 4:

[0081] The server performs analysis based on accumulated driving data. This analysis uses a generative AI model and machine learning algorithms, supplying driving data retrieved from a database as input to the analysis engine. Data processing includes the extraction of features indicating reaction speed and cognitive ability. The output is an evaluation of driving ability.

[0082] Step 5:

[0083] The server scores driving ability based on evaluation results and generates a diagnostic report. It receives evaluation results obtained through analysis as input, and quantifies them using a scoring algorithm. Specifically, it compares the results with existing traffic databases and data from the same age group to perform scoring. The output is a diagnostic report for the user.

[0084] Step 6:

[0085] The user receives a diagnostic report sent from the server on their user terminal. The input is the diagnostic report sent from the server, which the user uses to consider their driving ability and the need for improvement. Specifically, they review the report and share the information with family members or traffic professionals. The output is behavioral changes, such as increased awareness of the user's driving habits or consideration of surrendering their license.

[0086] (Application Example 1)

[0087] 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."

[0088] Improving the safety of autonomous vehicles and reducing the risk of traffic accidents are key challenges. In particular, it is necessary to analyze surrounding traffic information in real time, taking into account the driving characteristics of elderly drivers and others, in order to predict potential hazards early and respond appropriately.

[0089] 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.

[0090] In this invention, the server includes information acquisition means for acquiring high-precision spatial information, information transmission means for transmitting collected motion data, and prediction means for predicting potential hazards based on surrounding motion information. This makes it possible to evaluate safety in real time and immediately and automatically control necessary countermeasures.

[0091] "Information acquisition means for acquiring high-precision spatial information" refers to a function for accurately collecting detailed positional data of the external environment.

[0092] "Information transmission means for transmitting collected operational data" refers to a function for transmitting the obtained operational data wirelessly or via wired connection to other systems or devices.

[0093] A "data processing method for evaluating capabilities" is a function that analyzes received data and performs evaluations based on specific performance indicators or criteria.

[0094] An "assessment tool for diagnosing ability" is a function that comprehensively judges the subject's ability based on the analysis results and detects its state.

[0095] A "report generation method" is a function that creates reports and notifications in a format that is easy for users to understand, based on the results of the diagnosis.

[0096] A "means of communication" refers to a function for delivering generated reports and notifications to their intended recipients.

[0097] A "predictive tool" is a function that analyzes the surrounding behavioral patterns to predict future possibilities and potential risks in advance.

[0098] A "control mechanism" is a function that appropriately adjusts the operation of a system according to external conditions.

[0099] The system of the present invention consists of a high-precision sensor mounted on a vehicle, a server for receiving and processing the data, and a user terminal for displaying the results. The operation of this system will be described in detail below.

[0100] First, the vehicle is equipped with LIDAR, cameras, and GPS sensors, which acquire high-precision spatial information in real time. This ensures that the vehicle's position and surrounding environment information are constantly updated. This information serves as a means of information acquisition.

[0101] The collected spatial information and motion data are transmitted to the server via an information transmission system. On the server, a data processing system using machine learning libraries operates to analyze the received data. The specific software used is Python and TensorFlow. In the data analysis, a predictive system functions to evaluate the surrounding motion patterns and predict potential dangers.

[0102] Based on the analysis results, the control system for controlling the vehicle becomes operational. This control system works in conjunction with the autonomous driving system to automatically adjust the distance between vehicles and suppress lane changes in response to predicted risks.

[0103] Subsequently, detailed analysis results are transmitted to the user's terminal via a communication device. On this terminal, a report generation device operates, displaying diagnostic results and advice in an easy-to-understand format for the user.

[0104] For example, if the system predicts a sudden movement of an adjacent vehicle while traveling on a highway, it will automatically adjust the vehicle speed to avoid the risk of collision. Such functions ensure a high level of safety even in autonomous vehicles.

[0105] An example of a prompt message that can be input into the generating AI model is: "Please propose a program to build a system that evaluates safe driving scores in real time based on driving data of vehicles surrounding an autonomous vehicle and automatically adjusts the system if the risk of an accident increases."

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The vehicle terminal uses high-precision sensors to acquire vehicle location information and surrounding motion data. The acquired inputs include distance data from LiDAR, image data from cameras, and location data from GPS. This data provides fundamental information for representing the vehicle's movement and surrounding environment.

[0109] Step 2:

[0110] The terminal transmits the collected data to the server using an information transmission method. The data is transmitted in real time and received by the server. The input data consists of raw numerical data and images, and is converted into a format to be used in the next analysis step as output.

[0111] Step 3:

[0112] The server begins processing the received data. The data processing uses Python and TensorFlow to execute machine learning algorithms. Location and motion data are supplied as input, and the output is a risk assessment of the vehicle and its surroundings. Data calculations include pattern recognition and anomaly detection.

[0113] Step 4:

[0114] The server uses predictive tools based on data to assess potential hazards. The input is the analyzed result, and the output is a specific risk situation or hazard prediction. For example, if a sudden stop of a vehicle ahead is predicted, risk information is generated.

[0115] Step 5:

[0116] Based on the risk assessment obtained by the server, the control system determines its response. The control system works in conjunction with the autonomous driving system to perform automatic adjustments such as speed control and maintaining distance between vehicles. In this process, the input is the assessment result, and the output is the vehicle's operation command.

[0117] Step 6:

[0118] The terminal sends the diagnostic results to the user's terminal. On the user's terminal, a report generation mechanism is activated, making the evaluation results easy to understand intuitively. The input is information sent from the server, and the output is a diagnostic report presented to the user. Specifically, it displays risk warnings and provides advice on safe driving.

[0119] 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.

[0120] This invention combines a driving ability diagnostic system that evaluates the driving behavior of elderly drivers with an emotion engine that recognizes and analyzes the user's emotions. This system comprehensively evaluates the driver's ability and safety and provides feedback by incorporating means for acquiring location information, means for data analysis and evaluation, means for generating and transmitting reports, and emotion recognition functions.

[0121] First, the device collects driving data such as the vehicle's location, speed, and acceleration while it is in motion. It also uses a camera and microphone to record the driver's face and voice, thereby detecting the driver's emotional state. This driving data and emotional data are transmitted to a central server via communication means.

[0122] The central server processes incoming data using an analysis engine. Driving behavior data is analyzed by a generating AI to assess abnormal behavior and safety aspects in order to evaluate driving ability. Simultaneously, an emotion engine analyzes emotional data to evaluate the driver's stress level and concentration levels. This data is integrated to provide a comprehensive evaluation of driving behavior.

[0123] The evaluation results are generated as a diagnostic report and sent to the user. This report includes not only an assessment of driving ability and safety, but also feedback and improvement suggestions based on emotional and stress levels. For example, if frequent irritability or anxiety is observed while driving, advice will be provided to investigate the cause.

[0124] As a concrete example, consider a scenario where an elderly driver is commuting to work in the morning. Along with the driver's driving data, anxiety is detected from their facial expressions and voice while driving. The server analyzes this data and generates an emotional report along with a safe driving score. This report provides an overview of driving ability, along with specific advice for improving driving safety based on the user's psychological state.

[0125] By combining this with emotion recognition, it becomes possible to provide a system that goes beyond simply diagnosing driving skills and aims to improve overall driver safety. This system will contribute to reducing traffic accidents and improving the safety of elderly drivers.

[0126] The following describes the processing flow.

[0127] Step 1:

[0128] The device uses various sensors, cameras, and microphones installed in the vehicle to collect data in real time while driving. This data includes driving information such as location (GPS), speed, acceleration, and frequency of sudden braking, as well as emotional data such as facial expression data and voice tone.

[0129] Step 2:

[0130] The terminal temporarily stores collected driving and emotional data and transmits it to a central server using a secure protocol via a communication method. The data is encrypted during this process to prevent data loss or eavesdropping.

[0131] Step 3:

[0132] The server processes the received driving data using a driving behavior analysis engine and evaluates driving ability using generated AI. Specifically, it identifies patterns of abnormal driving behavior and calculates a safe driving score.

[0133] Step 4:

[0134] The server analyzes emotional data using an emotion engine to assess the driver's emotional state, such as stress levels and decreased attention span. This helps understand the effect of the driver's psychological state on their driving behavior.

[0135] Step 5:

[0136] The server integrates driving evaluation data and emotional evaluation data to create a comprehensive diagnostic report. This report includes a safe driving score, specific driving tendencies, and advice on emotional state.

[0137] Step 6:

[0138] The server sends the generated diagnostic report to the user's terminal, providing real-time notifications. Through this, the user can understand their current driving ability and emotional state, and identify areas for improvement.

[0139] Step 7:

[0140] Based on the reports they receive, users can consider ways to improve their driving behavior and emotional state, and consult with family or professionals as needed. This allows them to take concrete actions to improve their driving habits and enhance safety.

[0141] (Example 2)

[0142] 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".

[0143] In evaluating the driving abilities of elderly drivers, it is necessary to conduct a comprehensive safety assessment that takes into account not only driving skills but also their psychological and emotional state. The lack of such a comprehensive assessment may increase the risk of potential traffic accidents and safety issues.

[0144] 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.

[0145] In this invention, the server includes a device for acquiring location information, a device for collecting driving data and emotional data, an analysis device for analyzing the received driving data and evaluating driving ability, and a device for analyzing emotional data and evaluating stress levels and concentration. This enables highly accurate evaluation of driving ability and feedback for safety improvements based on emotional state.

[0146] A "location information acquisition device" is a device that measures the location of a vehicle in real time and provides accurate geographical information.

[0147] A "device for collecting driving data and emotional data" is a device for detecting and recording various data related to the driving actions of a vehicle, as well as the psychological state of the driver.

[0148] A "communication device" is a device used to transmit collected data to a remote server.

[0149] An "analysis device" is a device that evaluates the driver's behavior based on received data and quantifies or diagnoses their abilities.

[0150] An "evaluation device" is a device that integrates analysis results and comprehensively diagnoses driving ability.

[0151] A "generation device" is a device that creates a diagnostic report that users can refer to based on the evaluation results.

[0152] A "device" is a device that has the function of sending reports in order to provide the user with diagnostic results.

[0153] An "algorithm" is a mathematical method or procedure used to analyze driving data and identify abnormal driving behaviors or safety risks.

[0154] "Feedback and improvement suggestions" refers to information that includes specific advice and recommendations for improving safety, tailored to the psychological state of the identified driver.

[0155] The present invention is a system for comprehensively evaluating the driving ability of elderly drivers, and includes a device for acquiring location information, a device for collecting driving data and emotional data, a communication device, an analysis device, an evaluation device, a generation device, and a feedback provision device based thereon.

[0156] The device is installed in the vehicle and collects driving data such as location, speed, and acceleration while driving. This uses GPS sensors and accelerometers. It also uses a camera and microphone to collect the driver's facial expressions and voice, and detect their emotional state. This data is transmitted to a server via communication means.

[0157] The server uses an analysis device with a generative AI model to analyze the received data. It evaluates abnormal behavior and safety from driving data, and assesses stress levels and concentration from emotional data. The analysis results are then integrated to provide a comprehensive diagnosis of driving ability.

[0158] The server then uses a generator to create a diagnostic report based on the analysis results. This report is sent to the user's terminal and includes not only an evaluation of driving ability but also specific feedback and improvement suggestions based on the user's emotional state.

[0159] As a concrete example, consider an elderly driver using this system during their commute. The terminal senses the driver's facial expressions along with location data and collects emotional data indicating their level of tension. The server analyzes this data, and the diagnostic report includes advice such as, "Consider using relaxation techniques to improve driving safety."

[0160] A concrete example of a prompt for a generative AI model would be, "Generate a safe driving score and an emotion report based on the driving and emotion data of an elderly driver." By using this prompt, the system can diagnose driving ability with high accuracy and provide feedback.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The device collects driving and emotional data within the vehicle. As input, the device receives location information from a GPS sensor, speed and acceleration from an accelerometer, video from a camera, and audio from a microphone. This data is recorded in real time by various sensors, specifically capturing driving behavior, the driver's facial expressions, and voice. As output, it generates integrated driving and emotional data.

[0164] Step 2:

[0165] The terminal sends the collected integrated data to the server. The input uses the driving data and emotion data generated in the previous step. This data is sent to the server via the internet or a dedicated communication protocol through a communication device. The output is the server, which receives this data and prepares it for analysis.

[0166] Step 3:

[0167] The server analyzes driving data. It uses the received driving data as input. Using a generative AI model, it analyzes patterns of driving behavior and calculates indicators related to abnormal behavior and safety. Through data calculations, it provides analysis results as output. These results include the driver's driving score and evaluation criteria.

[0168] Step 4:

[0169] The server analyzes emotional data. It utilizes the received emotional data as input. Using an emotion engine, it extracts indicators of stress levels and concentration from facial expressions and voice. Through data processing, these indicators are output as concrete evaluations.

[0170] Step 5:

[0171] The server integrates the analysis results of driving data and emotional data to perform a comprehensive evaluation. By integrating the outputs of both analysis steps, it comprehensively assesses the driver's abilities and emotional state. As output, it generates a comprehensive diagnostic result, preparing for the next step.

[0172] Step 6:

[0173] The server generates a diagnostic report using a generator. It uses the overall diagnostic results as input. Based on these results, it generates a report that includes specific feedback and improvement suggestions for the user. The output is a diagnostic report that the user can refer to.

[0174] Step 7:

[0175] The server sends the generated report to the user. The generated diagnostic report is used as input. The report is delivered to the user's terminal via communication. As output, the user receives feedback on their driving ability and emotional state on their terminal, which can be used to improve driving safety.

[0176] (Application Example 2)

[0177] 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 device 14 will be referred to as the "terminal."

[0178] Comprehensively evaluating the driving abilities and emotional states of drivers, including the elderly, is crucial for improving traffic safety. However, conventional driving ability diagnostic systems do not adequately consider emotional changes or stress levels, making it difficult to improve safety in actual driving situations.

[0179] 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.

[0180] In this invention, the server includes emotion analysis means for recognizing and monitoring the driver's emotional state in real time, feedback generation means for providing feedback according to the emotional state, and report generation means for providing diagnostic results to the user. This enables interactive feedback and improvement suggestions for safety enhancement that reflect the driver's emotional state.

[0181] "Position acquisition means" refers to a function for determining the vehicle's current location with high accuracy.

[0182] "Communication means" refers to a function for transmitting collected driving data to a central system or similar.

[0183] "Data analysis means" refers to a function that analyzes collected driving data and evaluates the driver's driving ability.

[0184] An "evaluation tool" is a function that diagnoses a driver's driving ability based on the analysis results and compiles those results.

[0185] The "report generation method" is a function that creates reports to provide users with easily understandable diagnostic results.

[0186] The "reporting method" refers to the function that sends the generated report to the user for notification.

[0187] The "emotion analysis method" is a function that extracts and evaluates the emotional state of a driver in real time from their facial expressions and voice while they are driving.

[0188] A "feedback generation means" is a function that provides appropriate feedback according to the driver's emotional state.

[0189] The system for realizing this invention first uses a terminal to acquire precise location information of the vehicle using high-precision position acquisition means. Furthermore, it transmits this driving behavior data to a central server using communication means. The server processes the received data from multiple angles and evaluates driving ability using data analysis means. In the specific analysis process, the driver's emotions are analyzed using a facial recognition API and a voice analysis engine, and their state is evaluated in real time by emotion analysis means.

[0190] The evaluation system performs a comprehensive diagnosis of the driver's driving ability based on the analysis results. The diagnostic results are compiled into a user-friendly format by the report generation system, and the reporting system sends the results to the user.

[0191] The feedback generation system creates real-time feedback tailored to the driver based on the evaluation results of the emotion analysis system, encouraging appropriate actions that contribute to safety. This system can use smart devices such as Apple iPhone® and Samsung Galaxy, and performs detailed emotion recognition using Google® Cloud Vision API and natural language processing technology.

[0192] For example, if the system detects that the driver's stress levels are increasing while driving, it can automatically play relaxing music and provide instructions such as "Please take a deep breath." By inputting the following example prompt into the AI, a more detailed situational analysis and appropriate feedback can be achieved: "Assess the driver's current emotional state and suggest what music or guidance should be provided to reduce stress."

[0193] Thus, by combining emotion analysis, this invention goes beyond merely diagnosing driving skills and becomes a system that improves overall driver safety and comfort.

[0194] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0195] Step 1:

[0196] The terminal acquires highly accurate location information of the vehicle using location acquisition means. This input data, obtained using GPS or other location data sources, is used to pinpoint the vehicle's exact location. This data is then sent to the next step.

[0197] Step 2:

[0198] The terminal collects driving behavior data and emotional states in real time through its camera and microphone, and transmits them to a central server via communication. Input data includes location information, acceleration, facial expressions, and voice. This data serves as the basis for a comprehensive analysis of the driver's behavior and emotions.

[0199] Step 3:

[0200] The server processes the received data using data analysis tools to evaluate driving ability and emotional state. The server utilizes facial recognition APIs and voice analysis engines to analyze the collected data in detail and output the driver's emotional state in digital format. These analysis results are used to evaluate safety.

[0201] Step 4:

[0202] The server uses evaluation tools to perform a comprehensive diagnosis of the driver's driving behavior based on the results of driving ability and emotion analysis. The input here is the analysis results from step 3, and the output is a diagnosis of the driver's driving ability and safety.

[0203] Step 5:

[0204] The server uses a report generation mechanism to create a report containing comprehensive diagnostic results and sentiment-based feedback, and sends it to the user via a reporting mechanism. The input is the diagnostic results from step 4, and the output report is provided to the user and used as driving feedback.

[0205] Step 6:

[0206] Based on prompts from the feedback generation system, the user receives interactive feedback tailored to their emotional state and adjusts their driving behavior accordingly. In this step, prompts are sent to a generating AI model to receive specific actionable guidance for improving safety, which they then apply to their driving. For example, feedback such as "Take a deep breath" may be presented audibly or visually.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] [Second Embodiment]

[0211] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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).

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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".

[0223] The driving ability diagnostic system of the present invention aims to improve traffic safety by monitoring and analyzing the driving behavior of elderly people in real time and appropriately evaluating their abilities. This system consists of a terminal installed in the vehicle, a central server, and a user terminal.

[0224] First, the terminal is integrated into the vehicle and uses various sensors such as GPS and accelerometers to acquire highly accurate location information and driving data (speed, sudden acceleration, sudden deceleration, lane changes, etc.). This information is initially processed by the vehicle's computer and then transmitted to a central server via the internet.

[0225] The central server stores received driving data and uses an analysis engine to evaluate the characteristics of driving behavior. By utilizing generative AI and machine learning algorithms, it extracts features related to reaction speed and cognitive ability, and scores safe driving ability. This evaluation criterion incorporates comparisons based on existing traffic databases and average driving characteristics of people of the same age and gender.

[0226] The evaluation results are automatically generated as a diagnostic report, and the details are sent to the user's terminal. Through this report, the user can understand the current state of their driving ability and consult with family or local government as needed. In addition, this system has a function to issue appropriate alerts when it detects data indicating a decline in driving ability, providing the user with an opportunity to consider changing their mindset or surrendering their license.

[0227] As a concrete example, data is collected from an elderly person's daily commute. From the transmitted data, the server analyzes detailed information such as changes in entry speed at intersections and braking timing, and notifies the user in a report if a decline in attention during recent driving has been observed. The user and their family can then consider this information to develop safer driving habits or prepare for surrendering their driver's license.

[0228] In this way, the system of the present invention visualizes the driving ability of elderly people and contributes to improving traffic safety and reducing risks.

[0229] The following describes the processing flow.

[0230] Step 1:

[0231] The device uses various sensors installed in the vehicle to collect data in real time while driving. The data acquired includes location information (GPS), speed, acceleration, brake usage frequency, and whether or not lane changes occurred.

[0232] Step 2:

[0233] The terminal processes the collected data, bundles it into data packets, and sends them to the server via the internet. During this process, data compression and encryption are performed to ensure communication efficiency and security.

[0234] Step 3:

[0235] The server saves the received data to a database and immediately starts data analysis using the analysis engine. The purpose of the analysis is to identify the driver's driving behavior and detect trends and anomalies by comparing it with past data.

[0236] Step 4:

[0237] The server uses AI generation to calculate indicators of reaction speed and attention. This algorithm considers factors such as the frequency of lane departures, sudden braking, and speed fluctuations to generate a safe driving score.

[0238] Step 5:

[0239] The server generates a diagnostic report based on the analysis results. The report includes driving safety levels, alerts for specific driving behaviors, and overall driving trends, all presented in an easy-to-understand format.

[0240] Step 6:

[0241] The server sends the generated report to the user's terminal. This allows the driver and related parties to view the results and take necessary corrective actions.

[0242] Step 7:

[0243] Based on the reports received, users will evaluate their own driving abilities and consult with their families and local governments to consider future options, including surrendering their licenses. This information will be used to improve their future driving behavior.

[0244] (Example 1)

[0245] 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."

[0246] The increase in human error by elderly drivers has become a social problem, raising the risk of traffic accidents. However, there are insufficient means for drivers themselves to objectively assess their own driving abilities and take appropriate action. There is a need for concrete approaches that allow elderly drivers to visualize their own driving abilities, raise their awareness of safe driving, and, when necessary, encourage them to surrender their licenses.

[0247] 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.

[0248] In this invention, the server includes communication means for initial processing and transmitting driving behavior data, data analysis means for storing the received data and analyzing it using a generation AI model and machine learning techniques, and evaluation means for evaluating driving ability via an analysis engine and providing a score. This makes it possible for drivers to understand their own driving characteristics in detail and consider specific improvement measures for safe driving.

[0249] "Position acquisition means" refers to technology for accurately measuring the current location of a vehicle, utilizing GPS and related location information technologies.

[0250] "Communication means" refers to the technology that processes collected driving data and transmits it to a server via a network such as the internet.

[0251] "Data analysis means" refers to a technology that accumulates received driving data and analyzes it in detail using generated AI models and machine learning algorithms to evaluate the characteristics of driving behavior.

[0252] "Evaluation methods" refer to technologies for evaluating driving ability based on analysis results and scoring that ability, and they play a role in determining the driver's safety by comparing it with existing data.

[0253] "Report generation means" refers to technology that automatically generates detailed reports including evaluation results of driving ability and suggestions for improvement.

[0254] "Reporting means" refers to a technology that transmits the generated report electronically to the user's terminal, providing information to the driver.

[0255] This invention is a driving ability diagnostic system that appropriately evaluates the driving ability of elderly people and aims to improve traffic safety. This system mainly consists of terminals, a central server, and user terminals.

[0256] terminal

[0257] The terminal is fixed to the vehicle and collects location information and driving data using various sensors such as GPS and accelerometers. In this process, the in-vehicle computer performs initial processing to prepare the data format and filter out unnecessary data. For example, information on vehicle speed, sudden acceleration, sudden deceleration, and lane changes is collected. The data is then securely transmitted to a central server via an internet connection.

[0258] server

[0259] The server stores data received from terminals in a database and evaluates its characteristics using an analysis engine. This analysis engine utilizes generative AI models and machine learning algorithms to extract features related to driving behavior. For example, it uses frameworks such as TensorFlow and PyTorch to score indicators related to reaction speed and cognitive ability. It also compares the data with existing traffic data and driving characteristics of the same age group.

[0260] User

[0261] Users can receive a detailed evaluation report of their driving ability on their device. This report includes a score for their driving characteristics and identifies areas for improvement, which can be shared with family and traffic professionals to consider countermeasures. For example, the report might prompt them to consider training at a driving school. Another specific example is a prompt to the generating AI model that reads, "Analyze the driving data of a 70-year-old driver at an intersection, extract characteristics related to reaction speed and cognitive ability, and evaluate their safe driving ability."

[0262] This invention enables drivers to gain a detailed understanding of their own driving abilities and take specific improvement measures for safer driving as needed. This will raise safety awareness among all drivers, including the elderly, and contribute to improving traffic safety.

[0263] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0264] Step 1:

[0265] The terminal collects driving data using various sensors installed in the vehicle. It receives location information from GPS and motion data from accelerometers as input and performs initial processing. This initial processing involves data format conversion and filtering of unnecessary data, generating organized driving data. The output is this organized driving data.

[0266] Step 2:

[0267] The terminal transmits the initially processed operating data to a central server via the internet. The terminal uses organized operating data as input and transfers it to the server via communication. Specifically, a protocol is used to transmit data while maintaining security through encryption. The output is the operating data received by the server.

[0268] Step 3:

[0269] The server stores the received operating data in a database. It receives operating data from terminals as input and uses a database management system to efficiently organize and store the data. The output is the entries in the stored database.

[0270] Step 4:

[0271] The server performs analysis based on accumulated driving data. This analysis uses a generative AI model and machine learning algorithms, supplying driving data retrieved from a database as input to the analysis engine. Data processing includes the extraction of features indicating reaction speed and cognitive ability. The output is an evaluation of driving ability.

[0272] Step 5:

[0273] The server scores driving ability based on evaluation results and generates a diagnostic report. It receives evaluation results obtained through analysis as input, and quantifies them using a scoring algorithm. Specifically, it compares the results with existing traffic databases and data from the same age group to perform scoring. The output is a diagnostic report for the user.

[0274] Step 6:

[0275] The user receives a diagnostic report sent from the server on their user terminal. The input is the diagnostic report sent from the server, which the user uses to consider their driving ability and the need for improvement. Specifically, they review the report and share the information with family members or traffic professionals. The output is behavioral changes, such as increased awareness of the user's driving habits or consideration of surrendering their license.

[0276] (Application Example 1)

[0277] 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."

[0278] It is an issue to improve the safety of autonomous vehicles and reduce the risk of traffic accidents. In particular, by considering the driving characteristics of the elderly and others and analyzing the surrounding motion information in real time, it is required to predict potential dangers at an early stage and respond appropriately.

[0279] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0280] In this invention, the server includes an information acquisition means for acquiring high-precision spatial information, an information transmission means for transmitting the collected motion data, and a prediction means for predicting potential dangers based on the surrounding motion information. Thereby, it becomes possible to evaluate safety in real time and automatically control necessary countermeasures immediately.

[0281] The "information acquisition means for acquiring high-precision spatial information" is a function for accurately collecting detailed position data of the external environment.

[0282] The "information transmission means for transmitting the collected motion data" is a function for wirelessly or wiredly transmitting the obtained motion data to other systems or devices.

[0283] The "data processing means for evaluating capabilities" is a function for analyzing the received data and performing an evaluation based on specific performance indicators or criteria.

[0284] The "evaluation means for diagnosing capabilities" is a function for comprehensively judging the capabilities of the target based on the analysis results and detecting its state.

[0285] The "report generation means" is a function for creating a report or notification in a form that is easy for the user to understand based on the diagnosed results.

[0286] The "transmission means" is a function for delivering the generated report or notification to the intended recipient.

[0287] A "predictive tool" is a function that analyzes the surrounding behavioral patterns to predict future possibilities and potential risks in advance.

[0288] A "control mechanism" is a function that appropriately adjusts the operation of a system according to external conditions.

[0289] The system of the present invention consists of a high-precision sensor mounted on a vehicle, a server for receiving and processing the data, and a user terminal for displaying the results. The operation of this system will be described in detail below.

[0290] First, the vehicle is equipped with LIDAR, cameras, and GPS sensors, which acquire high-precision spatial information in real time. This ensures that the vehicle's position and surrounding environment information are constantly updated. This information serves as a means of information acquisition.

[0291] The collected spatial information and motion data are transmitted to the server via an information transmission system. On the server, a data processing system using machine learning libraries operates to analyze the received data. The specific software used is Python and TensorFlow. In the data analysis, a predictive system functions to evaluate the surrounding motion patterns and predict potential dangers.

[0292] Based on the analysis results, the control system for controlling the vehicle becomes operational. This control system works in conjunction with the autonomous driving system to automatically adjust the distance between vehicles and suppress lane changes in response to predicted risks.

[0293] Subsequently, detailed analysis results are transmitted to the user's terminal via a communication device. On this terminal, a report generation device operates, displaying diagnostic results and advice in an easy-to-understand format for the user.

[0294] For example, if the system predicts a sudden movement of an adjacent vehicle while traveling on a highway, it will automatically adjust the vehicle speed to avoid the risk of collision. Such functions ensure a high level of safety even in autonomous vehicles.

[0295] An example of a prompt message that can be input into the generating AI model is: "Please propose a program to build a system that evaluates safe driving scores in real time based on driving data of vehicles surrounding an autonomous vehicle and automatically adjusts the system if the risk of an accident increases."

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] The vehicle terminal uses high-precision sensors to acquire vehicle location information and surrounding motion data. The acquired inputs include distance data from LiDAR, image data from cameras, and location data from GPS. This data provides fundamental information for representing the vehicle's movement and surrounding environment.

[0299] Step 2:

[0300] The terminal transmits the collected data to the server using an information transmission method. The data is transmitted in real time and received by the server. The input data consists of raw numerical data and images, and is converted into a format to be used in the next analysis step as output.

[0301] Step 3:

[0302] The server begins processing the received data. The data processing uses Python and TensorFlow to execute machine learning algorithms. Location and motion data are supplied as input, and the output is a risk assessment of the vehicle and its surroundings. Data calculations include pattern recognition and anomaly detection.

[0303] Step 4:

[0304] The server uses prediction means based on data to evaluate potential risks. The input is the analyzed result, and the output is a specific risk situation or risk prediction. As a specific operation, when a sudden stop of the vehicle ahead is predicted, the risk information is generated.

[0305] Step 5:

[0306] Based on the risk assessment obtained by the server, the control means determines a reaction. The control means cooperates with the automatic driving system and executes automatic adjustments such as speed adjustment and maintenance of the vehicle distance. At this time, the input is the evaluation result, and the output is the operation command of the vehicle.

[0307] Step 6:

[0308] The terminal transmits the diagnosis result to the user terminal. In the user's terminal, the report generation means operates to make the evaluation result intuitive and easy to view. The input is the information sent from the server, and the output is the diagnosis report presented to the user. As a specific operation, a risk warning is displayed and advice on safe driving is given.

[0309] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0310] The present invention combines an emotion engine that recognizes and analyzes the user's emotion with a driving ability diagnosis system that evaluates the driving behavior of elderly drivers. This system comprehensively evaluates the driver's ability and safety and provides feedback by having position information acquisition means, data analysis and evaluation means, report generation and transmission means, and an emotion recognition function.

[0311] First, the device collects driving data such as the vehicle's location, speed, and acceleration while it is in motion. It also uses a camera and microphone to record the driver's face and voice, thereby detecting the driver's emotional state. This driving data and emotional data are transmitted to a central server via communication means.

[0312] The central server processes incoming data using an analysis engine. Driving behavior data is analyzed by a generating AI to assess abnormal behavior and safety aspects in order to evaluate driving ability. Simultaneously, an emotion engine analyzes emotional data to evaluate the driver's stress level and concentration levels. This data is integrated to provide a comprehensive evaluation of driving behavior.

[0313] The evaluation results are generated as a diagnostic report and sent to the user. This report includes not only an assessment of driving ability and safety, but also feedback and improvement suggestions based on emotional and stress levels. For example, if frequent irritability or anxiety is observed while driving, advice will be provided to investigate the cause.

[0314] As a concrete example, consider a scenario where an elderly driver is commuting to work in the morning. Along with the driver's driving data, anxiety is detected from their facial expressions and voice while driving. The server analyzes this data and generates an emotional report along with a safe driving score. This report provides an overview of driving ability, along with specific advice for improving driving safety based on the user's psychological state.

[0315] By combining this with emotion recognition, it becomes possible to provide a system that goes beyond simply diagnosing driving skills and aims to improve overall driver safety. This system will contribute to reducing traffic accidents and improving the safety of elderly drivers.

[0316] The following describes the processing flow.

[0317] Step 1:

[0318] The device uses various sensors, cameras, and microphones installed in the vehicle to collect data in real time while driving. This data includes driving information such as location (GPS), speed, acceleration, and frequency of sudden braking, as well as emotional data such as facial expression data and voice tone.

[0319] Step 2:

[0320] The terminal temporarily stores collected driving and emotional data and transmits it to a central server using a secure protocol via a communication method. The data is encrypted during this process to prevent data loss or eavesdropping.

[0321] Step 3:

[0322] The server processes the received driving data using a driving behavior analysis engine and evaluates driving ability using generated AI. Specifically, it identifies patterns of abnormal driving behavior and calculates a safe driving score.

[0323] Step 4:

[0324] The server analyzes emotional data using an emotion engine to assess the driver's emotional state, such as stress levels and decreased attention span. This helps understand the effect of the driver's psychological state on their driving behavior.

[0325] Step 5:

[0326] The server integrates driving evaluation data and emotional evaluation data to create a comprehensive diagnostic report. This report includes a safe driving score, specific driving tendencies, and advice on emotional state.

[0327] Step 6:

[0328] The server sends the generated diagnostic report to the user's terminal, providing real-time notifications. Through this, the user can understand their current driving ability and emotional state, and identify areas for improvement.

[0329] Step 7:

[0330] Based on the reports they receive, users can consider ways to improve their driving behavior and emotional state, and consult with family or professionals as needed. This allows them to take concrete actions to improve their driving habits and enhance safety.

[0331] (Example 2)

[0332] 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".

[0333] In evaluating the driving abilities of elderly drivers, it is necessary to conduct a comprehensive safety assessment that takes into account not only driving skills but also their psychological and emotional state. The lack of such a comprehensive assessment may increase the risk of potential traffic accidents and safety issues.

[0334] 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.

[0335] In this invention, the server includes a device for acquiring location information, a device for collecting driving data and emotional data, an analysis device for analyzing the received driving data and evaluating driving ability, and a device for analyzing emotional data and evaluating stress levels and concentration. This enables highly accurate evaluation of driving ability and feedback for safety improvements based on emotional state.

[0336] A "location information acquisition device" is a device that measures the location of a vehicle in real time and provides accurate geographical information.

[0337] A "device for collecting driving data and emotional data" is a device for detecting and recording various data related to the driving actions of a vehicle, as well as the psychological state of the driver.

[0338] A "communication device" is a device used to transmit collected data to a remote server.

[0339] An "analysis device" is a device that evaluates the driver's behavior based on received data and quantifies or diagnoses their abilities.

[0340] An "evaluation device" is a device that integrates analysis results and comprehensively diagnoses driving ability.

[0341] A "generation device" is a device that creates a diagnostic report that users can refer to based on the evaluation results.

[0342] A "device" is a device that has the function of sending reports in order to provide the user with diagnostic results.

[0343] An "algorithm" is a mathematical method or procedure used to analyze driving data and identify abnormal driving behaviors or safety risks.

[0344] "Feedback and improvement suggestions" refers to information that includes specific advice and recommendations for improving safety, tailored to the psychological state of the identified driver.

[0345] The present invention is a system for comprehensively evaluating the driving ability of elderly drivers, and includes a device for acquiring location information, a device for collecting driving data and emotional data, a communication device, an analysis device, an evaluation device, a generation device, and a feedback provision device based thereon.

[0346] The device is installed in the vehicle and collects driving data such as location, speed, and acceleration while driving. This uses GPS sensors and accelerometers. It also uses a camera and microphone to collect the driver's facial expressions and voice, and detect their emotional state. This data is transmitted to a server via communication means.

[0347] The server uses an analysis device with a generative AI model to analyze the received data. It evaluates abnormal behavior and safety from driving data, and assesses stress levels and concentration from emotional data. The analysis results are then integrated to provide a comprehensive diagnosis of driving ability.

[0348] The server then uses a generator to create a diagnostic report based on the analysis results. This report is sent to the user's terminal and includes not only an evaluation of driving ability but also specific feedback and improvement suggestions based on the user's emotional state.

[0349] As a concrete example, consider an elderly driver using this system during their commute. The terminal senses the driver's facial expressions along with location data and collects emotional data indicating their level of tension. The server analyzes this data, and the diagnostic report includes advice such as, "Consider using relaxation techniques to improve driving safety."

[0350] A concrete example of a prompt for a generative AI model would be, "Generate a safe driving score and an emotion report based on the driving and emotion data of an elderly driver." By using this prompt, the system can diagnose driving ability with high accuracy and provide feedback.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] The device collects driving and emotional data within the vehicle. As input, the device receives location information from a GPS sensor, speed and acceleration from an accelerometer, video from a camera, and audio from a microphone. This data is recorded in real time by various sensors, specifically capturing driving behavior, the driver's facial expressions, and voice. As output, it generates integrated driving and emotional data.

[0354] Step 2:

[0355] The terminal sends the collected integrated data to the server. The input uses the driving data and emotion data generated in the previous step. This data is sent to the server via the internet or a dedicated communication protocol through a communication device. The output is the server, which receives this data and prepares it for analysis.

[0356] Step 3:

[0357] The server analyzes driving data. It uses the received driving data as input. Using a generative AI model, it analyzes patterns of driving behavior and calculates indicators related to abnormal behavior and safety. Through data calculations, it provides analysis results as output. These results include the driver's driving score and evaluation criteria.

[0358] Step 4:

[0359] The server analyzes emotional data. It utilizes the received emotional data as input. Using an emotion engine, it extracts indicators of stress levels and concentration from facial expressions and voice. Through data processing, these indicators are output as concrete evaluations.

[0360] Step 5:

[0361] The server integrates the analysis results of driving data and emotional data to perform a comprehensive evaluation. By integrating the outputs of both analysis steps, it comprehensively assesses the driver's abilities and emotional state. As output, it generates a comprehensive diagnostic result, preparing for the next step.

[0362] Step 6:

[0363] The server generates a diagnostic report using a generator. It uses the overall diagnostic results as input. Based on these results, it generates a report that includes specific feedback and improvement suggestions for the user. The output is a diagnostic report that the user can refer to.

[0364] Step 7:

[0365] The server sends the generated report to the user. The generated diagnostic report is used as input. The report is delivered to the user's terminal via communication. As output, the user receives feedback on their driving ability and emotional state on their terminal, which can be used to improve driving safety.

[0366] (Application Example 2)

[0367] 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 as the "terminal".

[0368] Comprehensively evaluating the driving abilities and emotional states of drivers, including the elderly, is crucial for improving traffic safety. However, conventional driving ability diagnostic systems do not adequately consider emotional changes or stress levels, making it difficult to improve safety in actual driving situations.

[0369] 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.

[0370] In this invention, the server includes emotion analysis means for recognizing and monitoring the driver's emotional state in real time, feedback generation means for providing feedback according to the emotional state, and report generation means for providing diagnostic results to the user. This enables interactive feedback and improvement suggestions for safety enhancement that reflect the driver's emotional state.

[0371] "Position acquisition means" refers to a function for determining the vehicle's current location with high accuracy.

[0372] "Communication means" refers to a function for transmitting collected driving data to a central system or similar.

[0373] "Data analysis means" refers to a function that analyzes collected driving data and evaluates the driver's driving ability.

[0374] An "evaluation tool" is a function that diagnoses a driver's driving ability based on the analysis results and compiles those results.

[0375] The "report generation method" is a function that creates reports to provide users with easily understandable diagnostic results.

[0376] The "reporting method" refers to the function that sends the generated report to the user for notification.

[0377] The "emotion analysis method" is a function that extracts and evaluates the emotional state of a driver in real time from their facial expressions and voice while they are driving.

[0378] A "feedback generation means" is a function that provides appropriate feedback according to the driver's emotional state.

[0379] The system for realizing this invention first uses a terminal to acquire precise location information of the vehicle using high-precision position acquisition means. Furthermore, it transmits this driving behavior data to a central server using communication means. The server processes the received data from multiple angles and evaluates driving ability using data analysis means. In the specific analysis process, the driver's emotions are analyzed using a facial recognition API and a voice analysis engine, and their state is evaluated in real time by emotion analysis means.

[0380] The evaluation system performs a comprehensive diagnosis of the driver's driving ability based on the analysis results. The diagnostic results are compiled into a user-friendly format by the report generation system, and the reporting system sends the results to the user.

[0381] The feedback generation system creates real-time feedback tailored to the driver based on the evaluation results of the emotion analysis system, encouraging appropriate actions that contribute to safety. This system can use smart devices such as Apple iPhones and Samsung Galaxy phones, and performs detailed emotion recognition using Google Cloud Vision API and natural language processing technology.

[0382] For example, if the system detects that the driver's stress levels are increasing while driving, it can automatically play relaxing music and provide instructions such as "Please take a deep breath." By inputting the following example prompt into the AI, a more detailed situational analysis and appropriate feedback can be achieved: "Assess the driver's current emotional state and suggest what music or guidance should be provided to reduce stress."

[0383] Thus, by combining emotion analysis, this invention goes beyond merely diagnosing driving skills and becomes a system that improves overall driver safety and comfort.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] The terminal acquires highly accurate location information of the vehicle using location acquisition means. This input data, obtained using GPS or other location data sources, is used to pinpoint the vehicle's exact location. This data is then sent to the next step.

[0387] Step 2:

[0388] The terminal collects driving behavior data and emotional states in real time through its camera and microphone, and transmits them to a central server via communication. Input data includes location information, acceleration, facial expressions, and voice. This data serves as the basis for a comprehensive analysis of the driver's behavior and emotions.

[0389] Step 3:

[0390] The server processes the received data using data analysis tools to evaluate driving ability and emotional state. The server utilizes facial recognition APIs and voice analysis engines to analyze the collected data in detail and output the driver's emotional state in digital format. These analysis results are used to evaluate safety.

[0391] Step 4:

[0392] The server uses evaluation tools to perform a comprehensive diagnosis of the driver's driving behavior based on the results of driving ability and emotion analysis. The input here is the analysis results from step 3, and the output is a diagnosis of the driver's driving ability and safety.

[0393] Step 5:

[0394] The server uses a report generation mechanism to create a report containing comprehensive diagnostic results and sentiment-based feedback, and sends it to the user via a reporting mechanism. The input is the diagnostic results from step 4, and the output report is provided to the user and used as driving feedback.

[0395] Step 6:

[0396] Based on prompts from the feedback generation system, the user receives interactive feedback tailored to their emotional state and adjusts their driving behavior accordingly. In this step, prompts are sent to a generating AI model to receive specific actionable guidance for improving safety, which they then apply to their driving. For example, feedback such as "Take a deep breath" may be presented audibly or visually.

[0397] 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.

[0398] 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.

[0399] 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.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] 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.

[0403] 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).

[0404] 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.

[0405] 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.

[0406] 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).

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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".

[0413] The driving ability diagnostic system of the present invention aims to improve traffic safety by monitoring and analyzing the driving behavior of elderly people in real time and appropriately evaluating their abilities. This system consists of a terminal installed in the vehicle, a central server, and a user terminal.

[0414] First, the terminal is integrated into the vehicle and uses various sensors such as GPS and accelerometers to acquire highly accurate location information and driving data (speed, sudden acceleration, sudden deceleration, lane changes, etc.). This information is initially processed by the vehicle's computer and then transmitted to a central server via the internet.

[0415] The central server stores received driving data and uses an analysis engine to evaluate the characteristics of driving behavior. By utilizing generative AI and machine learning algorithms, it extracts features related to reaction speed and cognitive ability, and scores safe driving ability. This evaluation criterion incorporates comparisons based on existing traffic databases and average driving characteristics of people of the same age and gender.

[0416] The evaluation results are automatically generated as a diagnostic report, and the details are sent to the user's terminal. Through this report, the user can understand the current state of their driving ability and consult with family or local government as needed. In addition, this system has a function to issue appropriate alerts when it detects data indicating a decline in driving ability, providing the user with an opportunity to consider changing their mindset or surrendering their license.

[0417] As a concrete example, data is collected from an elderly person's daily commute. From the transmitted data, the server analyzes detailed information such as changes in entry speed at intersections and braking timing, and notifies the user in a report if a decline in attention during recent driving has been observed. The user and their family can then consider this information to develop safer driving habits or prepare for surrendering their driver's license.

[0418] In this way, the system of the present invention visualizes the driving ability of elderly people and contributes to improving traffic safety and reducing risks.

[0419] The following describes the processing flow.

[0420] Step 1:

[0421] The device uses various sensors installed in the vehicle to collect data in real time while driving. The data acquired includes location information (GPS), speed, acceleration, brake usage frequency, and whether or not lane changes occurred.

[0422] Step 2:

[0423] The terminal processes the collected data, bundles it into data packets, and sends them to the server via the internet. During this process, data compression and encryption are performed to ensure communication efficiency and security.

[0424] Step 3:

[0425] The server saves the received data to a database and immediately starts data analysis using the analysis engine. The purpose of the analysis is to identify the driver's driving behavior and detect trends and anomalies by comparing it with past data.

[0426] Step 4:

[0427] The server uses AI generation to calculate indicators of reaction speed and attention. This algorithm considers factors such as the frequency of lane departures, sudden braking, and speed fluctuations to generate a safe driving score.

[0428] Step 5:

[0429] The server generates a diagnostic report based on the analysis results. The report includes driving safety levels, alerts for specific driving behaviors, and overall driving trends, all presented in an easy-to-understand format.

[0430] Step 6:

[0431] The server sends the generated report to the user's terminal. This allows the driver and related parties to view the results and take necessary corrective actions.

[0432] Step 7:

[0433] Based on the reports received, users will evaluate their own driving abilities and consult with their families and local governments to consider future options, including surrendering their licenses. This information will be used to improve their future driving behavior.

[0434] (Example 1)

[0435] 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."

[0436] The increase in human error by elderly drivers has become a social problem, raising the risk of traffic accidents. However, there are insufficient means for drivers themselves to objectively assess their own driving abilities and take appropriate action. There is a need for concrete approaches that allow elderly drivers to visualize their own driving abilities, raise their awareness of safe driving, and, when necessary, encourage them to surrender their licenses.

[0437] 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.

[0438] In this invention, the server includes communication means for initial processing and transmitting driving behavior data, data analysis means for storing the received data and analyzing it using a generation AI model and machine learning techniques, and evaluation means for evaluating driving ability via an analysis engine and providing a score. This makes it possible for drivers to understand their own driving characteristics in detail and consider specific improvement measures for safe driving.

[0439] "Position acquisition means" refers to technology for accurately measuring the current location of a vehicle, utilizing GPS and related location information technologies.

[0440] "Communication means" refers to the technology that processes collected driving data and transmits it to a server via a network such as the internet.

[0441] "Data analysis means" refers to a technology that accumulates received driving data and analyzes it in detail using generated AI models and machine learning algorithms to evaluate the characteristics of driving behavior.

[0442] "Evaluation methods" refer to technologies for evaluating driving ability based on analysis results and scoring that ability, and they play a role in determining the driver's safety by comparing it with existing data.

[0443] "Report generation means" refers to technology that automatically generates detailed reports including evaluation results of driving ability and suggestions for improvement.

[0444] "Reporting means" refers to a technology that transmits the generated report electronically to the user's terminal, providing information to the driver.

[0445] This invention is a driving ability diagnostic system that appropriately evaluates the driving ability of elderly people and aims to improve traffic safety. This system mainly consists of terminals, a central server, and user terminals.

[0446] terminal

[0447] The terminal is fixed to the vehicle and collects location information and driving data using various sensors such as GPS and accelerometers. In this process, the in-vehicle computer performs initial processing to prepare the data format and filter out unnecessary data. For example, information on vehicle speed, sudden acceleration, sudden deceleration, and lane changes is collected. The data is then securely transmitted to a central server via an internet connection.

[0448] server

[0449] The server stores data received from terminals in a database and evaluates its characteristics using an analysis engine. This analysis engine utilizes generative AI models and machine learning algorithms to extract features related to driving behavior. For example, it uses frameworks such as TensorFlow and PyTorch to score indicators related to reaction speed and cognitive ability. It also compares the data with existing traffic data and driving characteristics of the same age group.

[0450] User

[0451] Users can receive a detailed evaluation report of their driving ability on their device. This report includes a score for their driving characteristics and identifies areas for improvement, which can be shared with family and traffic professionals to consider countermeasures. For example, the report might prompt them to consider training at a driving school. Another specific example is a prompt to the generating AI model that reads, "Analyze the driving data of a 70-year-old driver at an intersection, extract characteristics related to reaction speed and cognitive ability, and evaluate their safe driving ability."

[0452] This invention enables drivers to gain a detailed understanding of their own driving abilities and take specific improvement measures for safer driving as needed. This will raise safety awareness among all drivers, including the elderly, and contribute to improving traffic safety.

[0453] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0454] Step 1:

[0455] The terminal collects driving data using various sensors installed in the vehicle. It receives location information from GPS and motion data from accelerometers as input and performs initial processing. This initial processing involves data format conversion and filtering of unnecessary data, generating organized driving data. The output is this organized driving data.

[0456] Step 2:

[0457] The terminal transmits the initially processed operating data to a central server via the internet. The terminal uses organized operating data as input and transfers it to the server via communication. Specifically, a protocol is used to transmit data while maintaining security through encryption. The output is the operating data received by the server.

[0458] Step 3:

[0459] The server stores the received operating data in a database. It receives operating data from terminals as input and uses a database management system to efficiently organize and store the data. The output is the entries in the stored database.

[0460] Step 4:

[0461] The server performs analysis based on accumulated driving data. This analysis uses a generative AI model and machine learning algorithms, supplying driving data retrieved from a database as input to the analysis engine. Data processing includes the extraction of features indicating reaction speed and cognitive ability. The output is an evaluation of driving ability.

[0462] Step 5:

[0463] The server scores driving ability based on evaluation results and generates a diagnostic report. It receives evaluation results obtained through analysis as input, and quantifies them using a scoring algorithm. Specifically, it compares the results with existing traffic databases and data from the same age group to perform scoring. The output is a diagnostic report for the user.

[0464] Step 6:

[0465] The user receives a diagnostic report sent from the server on their user terminal. The input is the diagnostic report sent from the server, which the user uses to consider their driving ability and the need for improvement. Specifically, they review the report and share the information with family members or traffic professionals. The output is behavioral changes, such as increased awareness of the user's driving habits or consideration of surrendering their license.

[0466] (Application Example 1)

[0467] 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."

[0468] Improving the safety of autonomous vehicles and reducing the risk of traffic accidents are key challenges. In particular, it is necessary to analyze surrounding traffic information in real time, taking into account the driving characteristics of elderly drivers and others, in order to predict potential hazards early and respond appropriately.

[0469] 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.

[0470] In this invention, the server includes information acquisition means for acquiring high-precision spatial information, information transmission means for transmitting collected motion data, and prediction means for predicting potential hazards based on surrounding motion information. This makes it possible to evaluate safety in real time and immediately and automatically control necessary countermeasures.

[0471] "Information acquisition means for acquiring high-precision spatial information" refers to a function for accurately collecting detailed positional data of the external environment.

[0472] "Information transmission means for transmitting collected operational data" refers to a function for transmitting the obtained operational data wirelessly or via wired connection to other systems or devices.

[0473] A "data processing method for evaluating capabilities" is a function that analyzes received data and performs evaluations based on specific performance indicators or criteria.

[0474] An "assessment tool for diagnosing ability" is a function that comprehensively judges the subject's ability based on the analysis results and detects its state.

[0475] A "report generation method" is a function that creates reports and notifications in a format that is easy for users to understand, based on the results of the diagnosis.

[0476] A "means of communication" refers to a function for delivering generated reports and notifications to their intended recipients.

[0477] A "predictive tool" is a function that analyzes the surrounding behavioral patterns to predict future possibilities and potential risks in advance.

[0478] A "control mechanism" is a function that appropriately adjusts the operation of a system according to external conditions.

[0479] The system of the present invention consists of a high-precision sensor mounted on a vehicle, a server for receiving and processing the data, and a user terminal for displaying the results. The operation of this system will be described in detail below.

[0480] First, the vehicle is equipped with LIDAR, cameras, and GPS sensors, which acquire high-precision spatial information in real time. This ensures that the vehicle's position and surrounding environment information are constantly updated. This information serves as a means of information acquisition.

[0481] The collected spatial information and motion data are transmitted to the server via an information transmission system. On the server, a data processing system using machine learning libraries operates to analyze the received data. The specific software used is Python and TensorFlow. In the data analysis, a predictive system functions to evaluate the surrounding motion patterns and predict potential dangers.

[0482] Based on the analysis results, the control system for controlling the vehicle becomes operational. This control system works in conjunction with the autonomous driving system to automatically adjust the distance between vehicles and suppress lane changes in response to predicted risks.

[0483] Subsequently, detailed analysis results are transmitted to the user's terminal via a communication device. On this terminal, a report generation device operates, displaying diagnostic results and advice in an easy-to-understand format for the user.

[0484] For example, if the system predicts a sudden movement of an adjacent vehicle while traveling on a highway, it will automatically adjust the vehicle speed to avoid the risk of collision. Such functions ensure a high level of safety even in autonomous vehicles.

[0485] An example of a prompt message that can be input into the generating AI model is: "Please propose a program to build a system that evaluates safe driving scores in real time based on driving data of vehicles surrounding an autonomous vehicle and automatically adjusts the system if the risk of an accident increases."

[0486] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0487] Step 1:

[0488] The vehicle terminal uses high-precision sensors to acquire vehicle location information and surrounding motion data. The acquired inputs include distance data from LiDAR, image data from cameras, and location data from GPS. This data provides fundamental information for representing the vehicle's movement and surrounding environment.

[0489] Step 2:

[0490] The terminal transmits the collected data to the server using an information transmission method. The data is transmitted in real time and received by the server. The input data consists of raw numerical data and images, and is converted into a format to be used in the next analysis step as output.

[0491] Step 3:

[0492] The server begins processing the received data. The data processing uses Python and TensorFlow to execute machine learning algorithms. Location and motion data are supplied as input, and the output is a risk assessment of the vehicle and its surroundings. Data calculations include pattern recognition and anomaly detection.

[0493] Step 4:

[0494] The server uses predictive tools based on data to assess potential hazards. The input is the analyzed result, and the output is a specific risk situation or hazard prediction. For example, if a sudden stop of a vehicle ahead is predicted, risk information is generated.

[0495] Step 5:

[0496] Based on the risk assessment obtained by the server, the control system determines its response. The control system works in conjunction with the autonomous driving system to perform automatic adjustments such as speed control and maintaining distance between vehicles. In this process, the input is the assessment result, and the output is the vehicle's operation command.

[0497] Step 6:

[0498] The terminal sends the diagnostic results to the user's terminal. On the user's terminal, a report generation mechanism is activated, making the evaluation results easy to understand intuitively. The input is information sent from the server, and the output is a diagnostic report presented to the user. Specifically, it displays risk warnings and provides advice on safe driving.

[0499] 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.

[0500] This invention combines a driving ability diagnostic system that evaluates the driving behavior of elderly drivers with an emotion engine that recognizes and analyzes the user's emotions. This system comprehensively evaluates the driver's ability and safety and provides feedback by incorporating means for acquiring location information, means for data analysis and evaluation, means for generating and transmitting reports, and emotion recognition functions.

[0501] First, the device collects driving data such as the vehicle's location, speed, and acceleration while it is in motion. It also uses a camera and microphone to record the driver's face and voice, thereby detecting the driver's emotional state. This driving data and emotional data are transmitted to a central server via communication means.

[0502] The central server processes incoming data using an analysis engine. Driving behavior data is analyzed by a generating AI to assess abnormal behavior and safety aspects in order to evaluate driving ability. Simultaneously, an emotion engine analyzes emotional data to evaluate the driver's stress level and concentration levels. This data is integrated to provide a comprehensive evaluation of driving behavior.

[0503] The evaluation results are generated as a diagnostic report and sent to the user. This report includes not only an assessment of driving ability and safety, but also feedback and improvement suggestions based on emotional and stress levels. For example, if frequent irritability or anxiety is observed while driving, advice will be provided to investigate the cause.

[0504] As a concrete example, consider a scenario where an elderly driver is commuting to work in the morning. Along with the driver's driving data, anxiety is detected from their facial expressions and voice while driving. The server analyzes this data and generates an emotional report along with a safe driving score. This report provides an overview of driving ability, along with specific advice for improving driving safety based on the user's psychological state.

[0505] By combining this with emotion recognition, it becomes possible to provide a system that goes beyond simply diagnosing driving skills and aims to improve overall driver safety. This system will contribute to reducing traffic accidents and improving the safety of elderly drivers.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The device uses various sensors, cameras, and microphones installed in the vehicle to collect data in real time while driving. This data includes driving information such as location (GPS), speed, acceleration, and frequency of sudden braking, as well as emotional data such as facial expression data and voice tone.

[0509] Step 2:

[0510] The terminal temporarily stores collected driving and emotional data and transmits it to a central server using a secure protocol via a communication method. The data is encrypted during this process to prevent data loss or eavesdropping.

[0511] Step 3:

[0512] The server processes the received driving data using a driving behavior analysis engine and evaluates driving ability using generated AI. Specifically, it identifies patterns of abnormal driving behavior and calculates a safe driving score.

[0513] Step 4:

[0514] The server analyzes emotional data using an emotion engine to assess the driver's emotional state, such as stress levels and decreased attention span. This helps understand the effect of the driver's psychological state on their driving behavior.

[0515] Step 5:

[0516] The server integrates driving evaluation data and emotional evaluation data to create a comprehensive diagnostic report. This report includes a safe driving score, specific driving tendencies, and advice on emotional state.

[0517] Step 6:

[0518] The server sends the generated diagnostic report to the user's terminal, providing real-time notifications. Through this, the user can understand their current driving ability and emotional state, and identify areas for improvement.

[0519] Step 7:

[0520] Based on the reports they receive, users can consider ways to improve their driving behavior and emotional state, and consult with family or professionals as needed. This allows them to take concrete actions to improve their driving habits and enhance safety.

[0521] (Example 2)

[0522] 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."

[0523] In evaluating the driving abilities of elderly drivers, it is necessary to conduct a comprehensive safety assessment that takes into account not only driving skills but also their psychological and emotional state. The lack of such a comprehensive assessment may increase the risk of potential traffic accidents and safety issues.

[0524] 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.

[0525] In this invention, the server includes a device for acquiring location information, a device for collecting driving data and emotional data, an analysis device for analyzing the received driving data and evaluating driving ability, and a device for analyzing emotional data and evaluating stress levels and concentration. This enables highly accurate evaluation of driving ability and feedback for safety improvements based on emotional state.

[0526] A "location information acquisition device" is a device that measures the location of a vehicle in real time and provides accurate geographical information.

[0527] A "device for collecting driving data and emotional data" is a device for detecting and recording various data related to the driving actions of a vehicle, as well as the psychological state of the driver.

[0528] A "communication device" is a device used to transmit collected data to a remote server.

[0529] An "analysis device" is a device that evaluates the driver's behavior based on received data and quantifies or diagnoses their abilities.

[0530] An "evaluation device" is a device that integrates analysis results and comprehensively diagnoses driving ability.

[0531] A "generation device" is a device that creates a diagnostic report that users can refer to based on the evaluation results.

[0532] A "device" is a device that has the function of sending reports in order to provide the user with diagnostic results.

[0533] An "algorithm" is a mathematical method or procedure used to analyze driving data and identify abnormal driving behaviors or safety risks.

[0534] "Feedback and improvement suggestions" refers to information that includes specific advice and recommendations for improving safety, tailored to the psychological state of the identified driver.

[0535] The present invention is a system for comprehensively evaluating the driving ability of elderly drivers, and includes a device for acquiring location information, a device for collecting driving data and emotional data, a communication device, an analysis device, an evaluation device, a generation device, and a feedback provision device based thereon.

[0536] The device is installed in the vehicle and collects driving data such as location, speed, and acceleration while driving. This uses GPS sensors and accelerometers. It also uses a camera and microphone to collect the driver's facial expressions and voice, and detect their emotional state. This data is transmitted to a server via communication means.

[0537] The server uses an analysis device with a generative AI model to analyze the received data. It evaluates abnormal behavior and safety from driving data, and assesses stress levels and concentration from emotional data. The analysis results are then integrated to provide a comprehensive diagnosis of driving ability.

[0538] The server then uses a generator to create a diagnostic report based on the analysis results. This report is sent to the user's terminal and includes not only an evaluation of driving ability but also specific feedback and improvement suggestions based on the user's emotional state.

[0539] As a concrete example, consider an elderly driver using this system during their commute. The terminal senses the driver's facial expressions along with location data and collects emotional data indicating their level of tension. The server analyzes this data, and the diagnostic report includes advice such as, "Consider using relaxation techniques to improve driving safety."

[0540] A concrete example of a prompt for a generative AI model would be, "Generate a safe driving score and an emotion report based on the driving and emotion data of an elderly driver." By using this prompt, the system can diagnose driving ability with high accuracy and provide feedback.

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The device collects driving and emotional data within the vehicle. As input, the device receives location information from a GPS sensor, speed and acceleration from an accelerometer, video from a camera, and audio from a microphone. This data is recorded in real time by various sensors, specifically capturing driving behavior, the driver's facial expressions, and voice. As output, it generates integrated driving and emotional data.

[0544] Step 2:

[0545] The terminal sends the collected integrated data to the server. The input uses the driving data and emotion data generated in the previous step. This data is sent to the server via the internet or a dedicated communication protocol through a communication device. The output is the server, which receives this data and prepares it for analysis.

[0546] Step 3:

[0547] The server analyzes driving data. It uses the received driving data as input. Using a generative AI model, it analyzes patterns of driving behavior and calculates indicators related to abnormal behavior and safety. Through data calculations, it provides analysis results as output. These results include the driver's driving score and evaluation criteria.

[0548] Step 4:

[0549] The server analyzes emotional data. It utilizes the received emotional data as input. Using an emotion engine, it extracts indicators of stress levels and concentration from facial expressions and voice. Through data processing, these indicators are output as concrete evaluations.

[0550] Step 5:

[0551] The server integrates the analysis results of driving data and emotional data to perform a comprehensive evaluation. By integrating the outputs of both analysis steps, it comprehensively assesses the driver's abilities and emotional state. As output, it generates a comprehensive diagnostic result, preparing for the next step.

[0552] Step 6:

[0553] The server generates a diagnostic report using a generator. It uses the overall diagnostic results as input. Based on these results, it generates a report that includes specific feedback and improvement suggestions for the user. The output is a diagnostic report that the user can refer to.

[0554] Step 7:

[0555] The server sends the generated report to the user. The generated diagnostic report is used as input. The report is delivered to the user's terminal via communication. As output, the user receives feedback on their driving ability and emotional state on their terminal, which can be used to improve driving safety.

[0556] (Application Example 2)

[0557] 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."

[0558] Comprehensively evaluating the driving abilities and emotional states of drivers, including the elderly, is crucial for improving traffic safety. However, conventional driving ability diagnostic systems do not adequately consider emotional changes or stress levels, making it difficult to improve safety in actual driving situations.

[0559] 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.

[0560] In this invention, the server includes emotion analysis means for recognizing and monitoring the driver's emotional state in real time, feedback generation means for providing feedback according to the emotional state, and report generation means for providing diagnostic results to the user. This enables interactive feedback and improvement suggestions for safety enhancement that reflect the driver's emotional state.

[0561] "Position acquisition means" refers to a function for determining the vehicle's current location with high accuracy.

[0562] "Communication means" refers to a function for transmitting collected driving data to a central system or similar.

[0563] "Data analysis means" refers to a function that analyzes collected driving data and evaluates the driver's driving ability.

[0564] An "evaluation tool" is a function that diagnoses a driver's driving ability based on the analysis results and compiles those results.

[0565] The "report generation method" is a function that creates reports to provide users with easily understandable diagnostic results.

[0566] The "reporting method" refers to the function that sends the generated report to the user for notification.

[0567] The "emotion analysis method" is a function that extracts and evaluates the emotional state of a driver in real time from their facial expressions and voice while they are driving.

[0568] A "feedback generation means" is a function that provides appropriate feedback according to the driver's emotional state.

[0569] The system for realizing this invention first uses a terminal to acquire precise location information of the vehicle using high-precision position acquisition means. Furthermore, it transmits this driving behavior data to a central server using communication means. The server processes the received data from multiple angles and evaluates driving ability using data analysis means. In the specific analysis process, the driver's emotions are analyzed using a facial recognition API and a voice analysis engine, and their state is evaluated in real time by emotion analysis means.

[0570] The evaluation system performs a comprehensive diagnosis of the driver's driving ability based on the analysis results. The diagnostic results are compiled into a user-friendly format by the report generation system, and the reporting system sends the results to the user.

[0571] The feedback generation system creates real-time feedback tailored to the driver based on the evaluation results of the emotion analysis system, encouraging appropriate actions that contribute to safety. This system can use smart devices such as Apple iPhones and Samsung Galaxy phones, and performs detailed emotion recognition using Google Cloud Vision API and natural language processing technology.

[0572] For example, if the system detects that the driver's stress levels are increasing while driving, it can automatically play relaxing music and provide instructions such as "Please take a deep breath." By inputting the following example prompt into the AI, a more detailed situational analysis and appropriate feedback can be achieved: "Assess the driver's current emotional state and suggest what music or guidance should be provided to reduce stress."

[0573] Thus, by combining emotion analysis, this invention goes beyond merely diagnosing driving skills and becomes a system that improves overall driver safety and comfort.

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The terminal acquires highly accurate location information of the vehicle using location acquisition means. This input data, obtained using GPS or other location data sources, is used to pinpoint the vehicle's exact location. This data is then sent to the next step.

[0577] Step 2:

[0578] The terminal collects driving behavior data and emotional states in real time through its camera and microphone, and transmits them to a central server via communication. Input data includes location information, acceleration, facial expressions, and voice. This data serves as the basis for a comprehensive analysis of the driver's behavior and emotions.

[0579] Step 3:

[0580] The server processes the received data using data analysis tools to evaluate driving ability and emotional state. The server utilizes facial recognition APIs and voice analysis engines to analyze the collected data in detail and output the driver's emotional state in digital format. These analysis results are used to evaluate safety.

[0581] Step 4:

[0582] The server uses evaluation tools to perform a comprehensive diagnosis of the driver's driving behavior based on the results of driving ability and emotion analysis. The input here is the analysis results from step 3, and the output is a diagnosis of the driver's driving ability and safety.

[0583] Step 5:

[0584] The server uses a report generation mechanism to create a report containing comprehensive diagnostic results and sentiment-based feedback, and sends it to the user via a reporting mechanism. The input is the diagnostic results from step 4, and the output report is provided to the user and used as driving feedback.

[0585] Step 6:

[0586] Based on prompts from the feedback generation system, the user receives interactive feedback tailored to their emotional state and adjusts their driving behavior accordingly. In this step, prompts are sent to a generating AI model to receive specific actionable guidance for improving safety, which they then apply to their driving. For example, feedback such as "Take a deep breath" may be presented audibly or visually.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] [Fourth Embodiment]

[0591] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0592] 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.

[0593] 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).

[0594] 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.

[0595] 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.

[0596] 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).

[0597] 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.

[0598] 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.

[0599] 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.

[0600] 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.

[0601] 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.

[0602] 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.

[0603] 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".

[0604] The driving ability diagnostic system of the present invention aims to improve traffic safety by monitoring and analyzing the driving behavior of elderly people in real time and appropriately evaluating their abilities. This system consists of a terminal installed in the vehicle, a central server, and a user terminal.

[0605] First, the terminal is integrated into the vehicle and uses various sensors such as GPS and accelerometers to acquire highly accurate location information and driving data (speed, sudden acceleration, sudden deceleration, lane changes, etc.). This information is initially processed by the vehicle's computer and then transmitted to a central server via the internet.

[0606] The central server stores received driving data and uses an analysis engine to evaluate the characteristics of driving behavior. By utilizing generative AI and machine learning algorithms, it extracts features related to reaction speed and cognitive ability, and scores safe driving ability. This evaluation criterion incorporates comparisons based on existing traffic databases and average driving characteristics of people of the same age and gender.

[0607] The evaluation results are automatically generated as a diagnostic report, and the details are sent to the user's terminal. Through this report, the user can understand the current state of their driving ability and consult with family or local government as needed. In addition, this system has a function to issue appropriate alerts when it detects data indicating a decline in driving ability, providing the user with an opportunity to consider changing their mindset or surrendering their license.

[0608] As a concrete example, data is collected from an elderly person's daily commute. From the transmitted data, the server analyzes detailed information such as changes in entry speed at intersections and braking timing, and notifies the user in a report if a decline in attention during recent driving has been observed. The user and their family can then consider this information to develop safer driving habits or prepare for surrendering their driver's license.

[0609] In this way, the system of the present invention visualizes the driving ability of elderly people and contributes to improving traffic safety and reducing risks.

[0610] The following describes the processing flow.

[0611] Step 1:

[0612] The device uses various sensors installed in the vehicle to collect data in real time while driving. The data acquired includes location information (GPS), speed, acceleration, brake usage frequency, and whether or not lane changes occurred.

[0613] Step 2:

[0614] The terminal processes the collected data, bundles it into data packets, and sends them to the server via the internet. During this process, data compression and encryption are performed to ensure communication efficiency and security.

[0615] Step 3:

[0616] The server saves the received data to a database and immediately starts data analysis using the analysis engine. The purpose of the analysis is to identify the driver's driving behavior and detect trends and anomalies by comparing it with past data.

[0617] Step 4:

[0618] The server uses AI generation to calculate indicators of reaction speed and attention. This algorithm considers factors such as the frequency of lane departures, sudden braking, and speed fluctuations to generate a safe driving score.

[0619] Step 5:

[0620] The server generates a diagnostic report based on the analysis results. The report includes driving safety levels, alerts for specific driving behaviors, and overall driving trends, all presented in an easy-to-understand format.

[0621] Step 6:

[0622] The server sends the generated report to the user's terminal. This allows the driver and related parties to view the results and take necessary corrective actions.

[0623] Step 7:

[0624] Based on the reports received, users will evaluate their own driving abilities and consult with their families and local governments to consider future options, including surrendering their licenses. This information will be used to improve their future driving behavior.

[0625] (Example 1)

[0626] 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".

[0627] The increase in human error by elderly drivers has become a social problem, raising the risk of traffic accidents. However, there are insufficient means for drivers themselves to objectively assess their own driving abilities and take appropriate action. There is a need for concrete approaches that allow elderly drivers to visualize their own driving abilities, raise their awareness of safe driving, and, when necessary, encourage them to surrender their licenses.

[0628] 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.

[0629] In this invention, the server includes communication means for initial processing and transmitting driving behavior data, data analysis means for storing the received data and analyzing it using a generation AI model and machine learning techniques, and evaluation means for evaluating driving ability via an analysis engine and providing a score. This makes it possible for drivers to understand their own driving characteristics in detail and consider specific improvement measures for safe driving.

[0630] "Position acquisition means" refers to technology for accurately measuring the current location of a vehicle, utilizing GPS and related location information technologies.

[0631] "Communication means" refers to the technology that processes collected driving data and transmits it to a server via a network such as the internet.

[0632] "Data analysis means" refers to a technology that accumulates received driving data and analyzes it in detail using generated AI models and machine learning algorithms to evaluate the characteristics of driving behavior.

[0633] "Evaluation methods" refer to technologies for evaluating driving ability based on analysis results and scoring that ability, and they play a role in determining the driver's safety by comparing it with existing data.

[0634] "Report generation means" refers to technology that automatically generates detailed reports including evaluation results of driving ability and suggestions for improvement.

[0635] "Reporting means" refers to a technology that transmits the generated report electronically to the user's terminal, providing information to the driver.

[0636] This invention is a driving ability diagnostic system that appropriately evaluates the driving ability of elderly people and aims to improve traffic safety. This system mainly consists of terminals, a central server, and user terminals.

[0637] terminal

[0638] The terminal is fixed to the vehicle and collects location information and driving data using various sensors such as GPS and accelerometers. In this process, the in-vehicle computer performs initial processing to prepare the data format and filter out unnecessary data. For example, information on vehicle speed, sudden acceleration, sudden deceleration, and lane changes is collected. The data is then securely transmitted to a central server via an internet connection.

[0639] server

[0640] The server stores data received from terminals in a database and evaluates its characteristics using an analysis engine. This analysis engine utilizes generative AI models and machine learning algorithms to extract features related to driving behavior. For example, it uses frameworks such as TensorFlow and PyTorch to score indicators related to reaction speed and cognitive ability. It also compares the data with existing traffic data and driving characteristics of the same age group.

[0641] User

[0642] Users can receive a detailed evaluation report of their driving ability on their device. This report includes a score for their driving characteristics and identifies areas for improvement, which can be shared with family and traffic professionals to consider countermeasures. For example, the report might prompt them to consider training at a driving school. Another specific example is a prompt to the generating AI model that reads, "Analyze the driving data of a 70-year-old driver at an intersection, extract characteristics related to reaction speed and cognitive ability, and evaluate their safe driving ability."

[0643] This invention enables drivers to gain a detailed understanding of their own driving abilities and take specific improvement measures for safer driving as needed. This will raise safety awareness among all drivers, including the elderly, and contribute to improving traffic safety.

[0644] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0645] Step 1:

[0646] The terminal collects driving data using various sensors installed in the vehicle. It receives location information from GPS and motion data from accelerometers as input and performs initial processing. This initial processing involves data format conversion and filtering of unnecessary data, generating organized driving data. The output is this organized driving data.

[0647] Step 2:

[0648] The terminal transmits the initially processed operating data to a central server via the internet. The terminal uses organized operating data as input and transfers it to the server via communication. Specifically, a protocol is used to transmit data while maintaining security through encryption. The output is the operating data received by the server.

[0649] Step 3:

[0650] The server stores the received operating data in a database. It receives operating data from terminals as input and uses a database management system to efficiently organize and store the data. The output is the entries in the stored database.

[0651] Step 4:

[0652] The server performs analysis based on accumulated driving data. This analysis uses a generative AI model and machine learning algorithms, supplying driving data retrieved from a database as input to the analysis engine. Data processing includes the extraction of features indicating reaction speed and cognitive ability. The output is an evaluation of driving ability.

[0653] Step 5:

[0654] The server scores driving ability based on evaluation results and generates a diagnostic report. It receives evaluation results obtained through analysis as input, and quantifies them using a scoring algorithm. Specifically, it compares the results with existing traffic databases and data from the same age group to perform scoring. The output is a diagnostic report for the user.

[0655] Step 6:

[0656] The user receives a diagnostic report sent from the server on their user terminal. The input is the diagnostic report sent from the server, which the user uses to consider their driving ability and the need for improvement. Specifically, they review the report and share the information with family members or traffic professionals. The output is behavioral changes, such as increased awareness of the user's driving habits or consideration of surrendering their license.

[0657] (Application Example 1)

[0658] 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".

[0659] Improving the safety of autonomous vehicles and reducing the risk of traffic accidents are key challenges. In particular, it is necessary to analyze surrounding traffic information in real time, taking into account the driving characteristics of elderly drivers and others, in order to predict potential hazards early and respond appropriately.

[0660] 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.

[0661] In this invention, the server includes information acquisition means for acquiring high-precision spatial information, information transmission means for transmitting collected motion data, and prediction means for predicting potential hazards based on surrounding motion information. This makes it possible to evaluate safety in real time and immediately and automatically control necessary countermeasures.

[0662] "Information acquisition means for acquiring high-precision spatial information" refers to a function for accurately collecting detailed positional data of the external environment.

[0663] "Information transmission means for transmitting collected operational data" refers to a function for transmitting the obtained operational data wirelessly or via wired connection to other systems or devices.

[0664] A "data processing method for evaluating capabilities" is a function that analyzes received data and performs evaluations based on specific performance indicators or criteria.

[0665] An "assessment tool for diagnosing ability" is a function that comprehensively judges the subject's ability based on the analysis results and detects its state.

[0666] A "report generation method" is a function that creates reports and notifications in a format that is easy for users to understand, based on the results of the diagnosis.

[0667] A "means of communication" refers to a function for delivering generated reports and notifications to their intended recipients.

[0668] A "predictive tool" is a function that analyzes the surrounding behavioral patterns to predict future possibilities and potential risks in advance.

[0669] A "control mechanism" is a function that appropriately adjusts the operation of a system according to external conditions.

[0670] The system of the present invention consists of a high-precision sensor mounted on a vehicle, a server for receiving and processing the data, and a user terminal for displaying the results. The operation of this system will be described in detail below.

[0671] First, the vehicle is equipped with LIDAR, cameras, and GPS sensors, which acquire high-precision spatial information in real time. This ensures that the vehicle's position and surrounding environment information are constantly updated. This information serves as a means of information acquisition.

[0672] The collected spatial information and motion data are transmitted to the server via an information transmission system. On the server, a data processing system using machine learning libraries operates to analyze the received data. The specific software used is Python and TensorFlow. In the data analysis, a predictive system functions to evaluate the surrounding motion patterns and predict potential dangers.

[0673] Based on the analysis results, the control system for controlling the vehicle becomes operational. This control system works in conjunction with the autonomous driving system to automatically adjust the distance between vehicles and suppress lane changes in response to predicted risks.

[0674] Subsequently, detailed analysis results are transmitted to the user's terminal via a communication device. On this terminal, a report generation device operates, displaying diagnostic results and advice in an easy-to-understand format for the user.

[0675] For example, if the system predicts a sudden movement of an adjacent vehicle while traveling on a highway, it will automatically adjust the vehicle speed to avoid the risk of collision. Such functions ensure a high level of safety even in autonomous vehicles.

[0676] An example of a prompt message that can be input into the generating AI model is: "Please propose a program to build a system that evaluates safe driving scores in real time based on driving data of vehicles surrounding an autonomous vehicle and automatically adjusts the system if the risk of an accident increases."

[0677] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0678] Step 1:

[0679] The vehicle terminal uses high-precision sensors to acquire vehicle location information and surrounding motion data. The acquired inputs include distance data from LiDAR, image data from cameras, and location data from GPS. This data provides fundamental information for representing the vehicle's movement and surrounding environment.

[0680] Step 2:

[0681] The terminal transmits the collected data to the server using an information transmission method. The data is transmitted in real time and received by the server. The input data consists of raw numerical data and images, and is converted into a format to be used in the next analysis step as output.

[0682] Step 3:

[0683] The server begins processing the received data. The data processing uses Python and TensorFlow to execute machine learning algorithms. Location and motion data are supplied as input, and the output is a risk assessment of the vehicle and its surroundings. Data calculations include pattern recognition and anomaly detection.

[0684] Step 4:

[0685] The server uses predictive tools based on data to assess potential hazards. The input is the analyzed result, and the output is a specific risk situation or hazard prediction. For example, if a sudden stop of a vehicle ahead is predicted, risk information is generated.

[0686] Step 5:

[0687] Based on the risk assessment obtained by the server, the control system determines its response. The control system works in conjunction with the autonomous driving system to perform automatic adjustments such as speed control and maintaining distance between vehicles. In this process, the input is the assessment result, and the output is the vehicle's operation command.

[0688] Step 6:

[0689] The terminal sends the diagnostic results to the user's terminal. On the user's terminal, a report generation mechanism is activated, making the evaluation results easy to understand intuitively. The input is information sent from the server, and the output is a diagnostic report presented to the user. Specifically, it displays risk warnings and provides advice on safe driving.

[0690] 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.

[0691] This invention combines a driving ability diagnostic system that evaluates the driving behavior of elderly drivers with an emotion engine that recognizes and analyzes the user's emotions. This system comprehensively evaluates the driver's ability and safety and provides feedback by incorporating means for acquiring location information, means for data analysis and evaluation, means for generating and transmitting reports, and emotion recognition functions.

[0692] First, the device collects driving data such as the vehicle's location, speed, and acceleration while it is in motion. It also uses a camera and microphone to record the driver's face and voice, thereby detecting the driver's emotional state. This driving data and emotional data are transmitted to a central server via communication means.

[0693] The central server processes incoming data using an analysis engine. Driving behavior data is analyzed by a generating AI to assess abnormal behavior and safety aspects in order to evaluate driving ability. Simultaneously, an emotion engine analyzes emotional data to evaluate the driver's stress level and concentration levels. This data is integrated to provide a comprehensive evaluation of driving behavior.

[0694] The evaluation results are generated as a diagnostic report and sent to the user. This report includes not only an assessment of driving ability and safety, but also feedback and improvement suggestions based on emotional and stress levels. For example, if frequent irritability or anxiety is observed while driving, advice will be provided to investigate the cause.

[0695] As a concrete example, consider a scenario where an elderly driver is commuting to work in the morning. Along with the driver's driving data, anxiety is detected from their facial expressions and voice while driving. The server analyzes this data and generates an emotional report along with a safe driving score. This report provides an overview of driving ability, along with specific advice for improving driving safety based on the user's psychological state.

[0696] By combining this with emotion recognition, it becomes possible to provide a system that goes beyond simply diagnosing driving skills and aims to improve overall driver safety. This system will contribute to reducing traffic accidents and improving the safety of elderly drivers.

[0697] The following describes the processing flow.

[0698] Step 1:

[0699] The device uses various sensors, cameras, and microphones installed in the vehicle to collect data in real time while driving. This data includes driving information such as location (GPS), speed, acceleration, and frequency of sudden braking, as well as emotional data such as facial expression data and voice tone.

[0700] Step 2:

[0701] The terminal temporarily stores collected driving and emotional data and transmits it to a central server using a secure protocol via a communication method. The data is encrypted during this process to prevent data loss or eavesdropping.

[0702] Step 3:

[0703] The server processes the received driving data using a driving behavior analysis engine and evaluates driving ability using generated AI. Specifically, it identifies patterns of abnormal driving behavior and calculates a safe driving score.

[0704] Step 4:

[0705] The server analyzes emotional data using an emotion engine to assess the driver's emotional state, such as stress levels and decreased attention span. This helps understand the effect of the driver's psychological state on their driving behavior.

[0706] Step 5:

[0707] The server integrates driving evaluation data and emotional evaluation data to create a comprehensive diagnostic report. This report includes a safe driving score, specific driving tendencies, and advice on emotional state.

[0708] Step 6:

[0709] The server sends the generated diagnostic report to the user's terminal, providing real-time notifications. Through this, the user can understand their current driving ability and emotional state, and identify areas for improvement.

[0710] Step 7:

[0711] Based on the reports they receive, users can consider ways to improve their driving behavior and emotional state, and consult with family or professionals as needed. This allows them to take concrete actions to improve their driving habits and enhance safety.

[0712] (Example 2)

[0713] 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".

[0714] In evaluating the driving abilities of elderly drivers, it is necessary to conduct a comprehensive safety assessment that takes into account not only driving skills but also their psychological and emotional state. The lack of such a comprehensive assessment may increase the risk of potential traffic accidents and safety issues.

[0715] 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.

[0716] In this invention, the server includes a device for acquiring location information, a device for collecting driving data and emotional data, an analysis device for analyzing the received driving data and evaluating driving ability, and a device for analyzing emotional data and evaluating stress levels and concentration. This enables highly accurate evaluation of driving ability and feedback for safety improvements based on emotional state.

[0717] A "location information acquisition device" is a device that measures the location of a vehicle in real time and provides accurate geographical information.

[0718] A "device for collecting driving data and emotional data" is a device for detecting and recording various data related to the driving actions of a vehicle, as well as the psychological state of the driver.

[0719] A "communication device" is a device used to transmit collected data to a remote server.

[0720] An "analysis device" is a device that evaluates the driver's behavior based on received data and quantifies or diagnoses their abilities.

[0721] An "evaluation device" is a device that integrates analysis results and comprehensively diagnoses driving ability.

[0722] A "generation device" is a device that creates a diagnostic report that users can refer to based on the evaluation results.

[0723] A "device" is a device that has the function of sending reports in order to provide the user with diagnostic results.

[0724] An "algorithm" is a mathematical method or procedure used to analyze driving data and identify abnormal driving behaviors or safety risks.

[0725] "Feedback and improvement suggestions" refers to information that includes specific advice and recommendations for improving safety, tailored to the psychological state of the identified driver.

[0726] The present invention is a system for comprehensively evaluating the driving ability of elderly drivers, and includes a device for acquiring location information, a device for collecting driving data and emotional data, a communication device, an analysis device, an evaluation device, a generation device, and a feedback provision device based thereon.

[0727] The device is installed in the vehicle and collects driving data such as location, speed, and acceleration while driving. This uses GPS sensors and accelerometers. It also uses a camera and microphone to collect the driver's facial expressions and voice, and detect their emotional state. This data is transmitted to a server via communication means.

[0728] The server uses an analysis device with a generative AI model to analyze the received data. It evaluates abnormal behavior and safety from driving data, and assesses stress levels and concentration from emotional data. The analysis results are then integrated to provide a comprehensive diagnosis of driving ability.

[0729] The server then uses a generator to create a diagnostic report based on the analysis results. This report is sent to the user's terminal and includes not only an evaluation of driving ability but also specific feedback and improvement suggestions based on the user's emotional state.

[0730] As a concrete example, consider an elderly driver using this system during their commute. The terminal senses the driver's facial expressions along with location data and collects emotional data indicating their level of tension. The server analyzes this data, and the diagnostic report includes advice such as, "Consider using relaxation techniques to improve driving safety."

[0731] A concrete example of a prompt for a generative AI model would be, "Generate a safe driving score and an emotion report based on the driving and emotion data of an elderly driver." By using this prompt, the system can diagnose driving ability with high accuracy and provide feedback.

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The device collects driving and emotional data within the vehicle. As input, the device receives location information from a GPS sensor, speed and acceleration from an accelerometer, video from a camera, and audio from a microphone. This data is recorded in real time by various sensors, specifically capturing driving behavior, the driver's facial expressions, and voice. As output, it generates integrated driving and emotional data.

[0735] Step 2:

[0736] The terminal sends the collected integrated data to the server. The input uses the driving data and emotion data generated in the previous step. This data is sent to the server via the internet or a dedicated communication protocol through a communication device. The output is the server, which receives this data and prepares it for analysis.

[0737] Step 3:

[0738] The server analyzes driving data. It uses the received driving data as input. Using a generative AI model, it analyzes patterns of driving behavior and calculates indicators related to abnormal behavior and safety. Through data calculations, it provides analysis results as output. These results include the driver's driving score and evaluation criteria.

[0739] Step 4:

[0740] The server analyzes emotional data. It utilizes the received emotional data as input. Using an emotion engine, it extracts indicators of stress levels and concentration from facial expressions and voice. Through data processing, these indicators are output as concrete evaluations.

[0741] Step 5:

[0742] The server integrates the analysis results of driving data and emotional data to perform a comprehensive evaluation. By integrating the outputs of both analysis steps, it comprehensively assesses the driver's abilities and emotional state. As output, it generates a comprehensive diagnostic result, preparing for the next step.

[0743] Step 6:

[0744] The server generates a diagnostic report using a generator. It uses the overall diagnostic results as input. Based on these results, it generates a report that includes specific feedback and improvement suggestions for the user. The output is a diagnostic report that the user can refer to.

[0745] Step 7:

[0746] The server sends the generated report to the user. The generated diagnostic report is used as input. The report is delivered to the user's terminal via communication. As output, the user receives feedback on their driving ability and emotional state on their terminal, which can be used to improve driving safety.

[0747] (Application Example 2)

[0748] 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".

[0749] Comprehensively evaluating the driving abilities and emotional states of drivers, including the elderly, is crucial for improving traffic safety. However, conventional driving ability diagnostic systems do not adequately consider emotional changes or stress levels, making it difficult to improve safety in actual driving situations.

[0750] 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.

[0751] In this invention, the server includes emotion analysis means for recognizing and monitoring the driver's emotional state in real time, feedback generation means for providing feedback according to the emotional state, and report generation means for providing diagnostic results to the user. This enables interactive feedback and improvement suggestions for safety enhancement that reflect the driver's emotional state.

[0752] "Position acquisition means" refers to a function for determining the vehicle's current location with high accuracy.

[0753] "Communication means" refers to a function for transmitting collected driving data to a central system or similar.

[0754] "Data analysis means" refers to a function that analyzes collected driving data and evaluates the driver's driving ability.

[0755] An "evaluation tool" is a function that diagnoses a driver's driving ability based on the analysis results and compiles those results.

[0756] The "report generation method" is a function that creates reports to provide users with easily understandable diagnostic results.

[0757] The "reporting method" refers to the function that sends the generated report to the user for notification.

[0758] The "emotion analysis method" is a function that extracts and evaluates the emotional state of a driver in real time from their facial expressions and voice while they are driving.

[0759] A "feedback generation means" is a function that provides appropriate feedback according to the driver's emotional state.

[0760] The system for realizing this invention first uses a terminal to acquire precise location information of the vehicle using high-precision position acquisition means. Furthermore, it transmits this driving behavior data to a central server using communication means. The server processes the received data from multiple angles and evaluates driving ability using data analysis means. In the specific analysis process, the driver's emotions are analyzed using a facial recognition API and a voice analysis engine, and their state is evaluated in real time by emotion analysis means.

[0761] The evaluation system performs a comprehensive diagnosis of the driver's driving ability based on the analysis results. The diagnostic results are compiled into a user-friendly format by the report generation system, and the reporting system sends the results to the user.

[0762] The feedback generation system creates real-time feedback tailored to the driver based on the evaluation results of the emotion analysis system, encouraging appropriate actions that contribute to safety. This system can use smart devices such as Apple iPhones and Samsung Galaxy phones, and performs detailed emotion recognition using Google Cloud Vision API and natural language processing technology.

[0763] For example, if the system detects that the driver's stress levels are increasing while driving, it can automatically play relaxing music and provide instructions such as "Please take a deep breath." By inputting the following example prompt into the AI, a more detailed situational analysis and appropriate feedback can be achieved: "Assess the driver's current emotional state and suggest what music or guidance should be provided to reduce stress."

[0764] Thus, by combining emotion analysis, this invention goes beyond merely diagnosing driving skills and becomes a system that improves overall driver safety and comfort.

[0765] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0766] Step 1:

[0767] The terminal acquires highly accurate location information of the vehicle using location acquisition means. This input data, obtained using GPS or other location data sources, is used to pinpoint the vehicle's exact location. This data is then sent to the next step.

[0768] Step 2:

[0769] The terminal collects driving behavior data and emotional states in real time through its camera and microphone, and transmits them to a central server via communication. Input data includes location information, acceleration, facial expressions, and voice. This data serves as the basis for a comprehensive analysis of the driver's behavior and emotions.

[0770] Step 3:

[0771] The server processes the received data using data analysis tools to evaluate driving ability and emotional state. The server utilizes facial recognition APIs and voice analysis engines to analyze the collected data in detail and output the driver's emotional state in digital format. These analysis results are used to evaluate safety.

[0772] Step 4:

[0773] The server uses evaluation tools to perform a comprehensive diagnosis of the driver's driving behavior based on the results of driving ability and emotion analysis. The input here is the analysis results from step 3, and the output is a diagnosis of the driver's driving ability and safety.

[0774] Step 5:

[0775] The server uses a report generation mechanism to create a report containing comprehensive diagnostic results and sentiment-based feedback, and sends it to the user via a reporting mechanism. The input is the diagnostic results from step 4, and the output report is provided to the user and used as driving feedback.

[0776] Step 6:

[0777] Based on prompts from the feedback generation system, the user receives interactive feedback tailored to their emotional state and adjusts their driving behavior accordingly. In this step, prompts are sent to a generating AI model to receive specific actionable guidance for improving safety, which they then apply to their driving. For example, feedback such as "Take a deep breath" may be presented audibly or visually.

[0778] 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.

[0779] 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.

[0780] 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 robot 414.

[0781] 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.

[0782] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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."

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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.

[0798] 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 as being incorporated by reference.

[0799] The following is further disclosed regarding the embodiments described above.

[0800] (Claim 1)

[0801] A location acquisition means for acquiring highly accurate location information,

[0802] A communication means for transmitting collected driving behavior data,

[0803] A data analysis means for analyzing received data and evaluating driving ability,

[0804] An evaluation method for diagnosing driving ability based on analysis results,

[0805] A means for generating reports that provide diagnostic results to the user,

[0806] A reporting method for sending reports,

[0807] A driving ability diagnostic system including...

[0808] (Claim 2)

[0809] The driving ability diagnostic system according to claim 1, comprising a data analysis means that executes an algorithm for identifying abnormalities in driving behavior and evaluating safety.

[0810] (Claim 3)

[0811] The driving ability diagnostic system according to claim 1, wherein the report generation means generates a diagnostic report that includes additional information and guidance for considering voluntary surrender.

[0812] "Example 1"

[0813] (Claim 1)

[0814] A location acquisition means for acquiring highly accurate location information,

[0815] A communication means for initial processing and transmitting driving behavior data,

[0816] A data analysis method that stores received data and analyzes it using a generative AI model and machine learning technology,

[0817] An evaluation means that evaluates driving ability via an analysis engine and provides a score,

[0818] A report generation means that generates a report including diagnostic results and improvement suggestions,

[0819] A reporting method for sending reports to user terminals,

[0820] A system that includes this.

[0821] (Claim 2)

[0822] The system according to claim 1, which extracts characteristics of driving behavior using a machine learning algorithm and scores safety.

[0823] (Claim 3)

[0824] The system according to claim 1, which notifies the user of evaluation results, including alerts that serve as a basis for considering voluntary surrender.

[0825] "Application Example 1"

[0826] (Claim 1)

[0827] Information acquisition means for acquiring high-precision spatial information,

[0828] Information transmission means for transmitting collected motion data,

[0829] A data processing means that analyzes received data and evaluates capabilities,

[0830] An evaluation method for diagnosing ability based on analysis results,

[0831] A means for generating reports that provide diagnostic results to users,

[0832] A means of communication for sending reports,

[0833] A predictive means for predicting potential dangers based on surrounding activity information,

[0834] A control means that automatically adjusts its operation according to the prediction of danger,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the data processing means executes an algorithm that identifies abnormal behavior and predicts safety.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the report generation means generates a diagnostic report that includes additional information and guidance to support independent judgment.

[0840] "Example 2 of combining an emotion engine"

[0841] (Claim 1)

[0842] A device for acquiring location information,

[0843] A device that collects driving data and emotional data,

[0844] A communication device that transmits the collected data,

[0845] An analysis device that analyzes received driving data and evaluates driving performance,

[0846] A device that analyzes emotional data to evaluate stress levels and concentration,

[0847] An evaluation device that integrates analysis results to diagnose driving ability,

[0848] A generator that generates a report based on the diagnostic results,

[0849] A device for sending reports,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, which identifies abnormalities in driving behavior data and executes an algorithm to evaluate safety.

[0853] (Claim 3)

[0854] The system according to claim 1, which generates a report that includes feedback and improvement suggestions based on emotional state.

[0855] "Application example 2 of combining emotional engines"

[0856] (Claim 1)

[0857] A location acquisition means for acquiring highly accurate location information,

[0858] A communication means for transmitting collected driving behavior data,

[0859] A data analysis means for analyzing received data and evaluating driving ability,

[0860] An evaluation method for diagnosing driving ability based on analysis results,

[0861] A means for generating reports that provide diagnostic results to the user,

[0862] A reporting method for sending reports,

[0863] An emotion analysis method that recognizes and monitors the driver's emotional state in real time,

[0864] A feedback generation means that provides feedback according to the emotional state,

[0865] A system that includes this.

[0866] (Claim 2)

[0867] The system according to claim 1, comprising a data analysis means that identifies abnormalities in driving behavior, executes an algorithm to evaluate safety, and provides interactive feedback based on emotional state.

[0868] (Claim 3)

[0869] The system according to claim 1, wherein the report generation means generates a diagnostic report that includes not only additional information and guidance for considering voluntary surrender, but also specific feedback for improving driving safety based on emotional state. [Explanation of Symbols]

[0870] 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 location acquisition means for acquiring highly accurate location information, A communication means for transmitting collected driving behavior data, A data analysis means for analyzing received data and evaluating driving ability, An evaluation method for diagnosing driving ability based on analysis results, A means for generating reports that provide diagnostic results to the user, A reporting method for sending reports, A driving ability diagnostic system including...

2. The driving ability diagnostic system according to claim 1, wherein the data analysis means executes an algorithm that identifies abnormalities in driving behavior and evaluates safety.

3. The driving ability diagnostic system according to claim 1, wherein the report generation means generates a diagnostic report that includes additional information and guidance for considering voluntary surrender.

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