system

Abstract

We provide the system. [Solution] A means for receiving information in real time from a device that collects vehicle operation information, A means for applying a machine intelligence algorithm to analyze the driving conditions based on the aforementioned information, A means for generating driving advice based on the aforementioned analysis results and providing it to the user using natural language processing, A means for predicting danger from the aforementioned operational information and external environmental information and providing warnings to users, A means of evaluating the user's driving behavior, scoring it, and providing feedback, A means of proposing optimal driving adjustments to users, including weather conditions in the aforementioned external environmental information, A system that includes this.

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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern transportation society, reducing traffic accidents and promoting safe driving are major issues. Many traffic accidents are caused by human error, and the risk increases particularly when novice or elderly drivers are operating. Also, in the context of environmental considerations, it is important to suppress fuel consumption and reduce greenhouse gas emissions by driving efficiently. However, it is difficult to solve these problems individually, and a system that can respond comprehensively and in real-time is needed.

Means for Solving the Problems

[0005] This invention provides a system that receives data in real time from a device that collects vehicle operation information, and analyzes driving conditions by applying an artificial intelligence algorithm based on the received data. Based on the analysis results, it generates easy-to-understand driving advice using natural language processing and provides it to the driver. It also includes a function to predict hazards based on the collected information and provide appropriate warnings. Furthermore, it scores the user's driving behavior, provides feedback based on the evaluation results, and proposes more energy-efficient driving methods, thereby comprehensively supporting improved traffic safety and reduced environmental impact.

[0006] A "device for collecting vehicle operation information" refers to a device installed inside a vehicle that includes sensors and GPS devices for acquiring operation information such as speed, position, and acceleration.

[0007] "Real-time" refers to the fact that data collection, analysis, and application of the results occur almost instantly and without delay.

[0008] "Data" refers to various types of information related to the operation of a vehicle, including speed, position, acceleration, and external environmental conditions.

[0009] An "artificial intelligence algorithm" is a program that uses mechanical or statistical models to analyze complex data and identify or predict specific patterns.

[0010] "Natural language processing" is a technology that enables computers to understand, interpret, and generate human natural language.

[0011] A "user" refers to a driver who uses the system to receive analysis results of their driving conditions.

[0012] "Means of predicting danger" refers to functions that include methods for detecting and warning about potentially dangerous situations in advance based on collected data.

[0013] A "means for evaluating and scoring driving behavior" is a system that measures a driver's driving performance and evaluates it numerically based on that measurement.

[0014] "Driving methods aimed at improving energy efficiency" refer to driving styles that are designed to minimize fuel consumption. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Embodiment for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), etc.

[0019] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the 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 disk (e.g., hard disk), or magnetic tape, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] This invention provides a system that collects operational data using sensors and GPS devices mounted on a vehicle, and analyzes driving conditions by applying an artificial intelligence algorithm based on that data. This system supports safe driving by presenting the analysis results to the user in an easy-to-understand manner using natural language processing and providing real-time driving advice and warnings.

[0037] The terminal collects information from various sensors in the vehicle. This information includes vehicle speed from the speed sensor, location information from the GPS device, and acceleration data from the accelerometer. This data is transmitted to the server at regular intervals.

[0038] The server immediately passes the received operational data to an artificial intelligence algorithm for analysis. This algorithm comprehensively evaluates the driving situation, analyzing whether speed and following distance are appropriate, and whether sudden braking or acceleration has occurred. Based on the analysis results, the server generates driving advice and warning messages.

[0039] The generated message is transformed into a form that is easy for the user to understand using natural language processing. The device then conveys this message to the user via screen display or audio. For example, if sudden braking is detected, the user will be notified with a message such as, "Sudden braking has been detected. Please maintain a safe distance from the vehicle in front."

[0040] Furthermore, the server scores the user's driving behavior and provides feedback. This score evaluates the driving pattern and informs the user of areas for improvement and strengths. This feedback is provided to the user periodically through the terminal.

[0041] The server also collects external traffic and weather data and predicts potential hazards based on this information. These predictions are also provided to the user, warning them in advance of any potential new hazards.

[0042] For example, if heavy rain is predicted while driving on a highway, the system will provide a warning such as, "Heavy rain is predicted ahead. Reduce your speed."

[0043] This invention provides drivers with real-time information and feedback, promoting safe driving and environmental considerations.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The terminal collects speed, acceleration, and location data from the vehicle's installed speed sensor, accelerometer, and GPS device. This data is periodically sent to the server in batches.

[0047] Step 2:

[0048] The server applies an AI algorithm to analyze the received operational data. This algorithm evaluates the data to identify sudden braking, sudden acceleration, speeding, and inappropriate following distances.

[0049] Step 3:

[0050] The server generates driver advice based on the analysis results. This advice is then converted into an easily understandable format using natural language processing technology.

[0051] Step 4:

[0052] The terminal provides the user with advice received from the server via voice or display. Examples include instructions such as "Slow down" or "Maintain a safe distance from the vehicle in front."

[0053] Step 5:

[0054] The server collects and analyzes traffic conditions and weather forecast information through external databases and APIs, and generates warnings if there are potential hazards.

[0055] Step 6:

[0056] The device communicates warnings to the user. For example, based on future road conditions, it may provide warning messages such as, "There is traffic congestion ahead."

[0057] Step 7:

[0058] The server analyzes the user's past driving data, evaluates their driving behavior, and assigns a score. This score takes into account various aspects of driving and serves as an indicator for evaluating, for example, safety and fuel efficiency.

[0059] Step 8:

[0060] The device periodically provides the user with a driving score and feedback. The feedback includes specific suggestions, such as, "Your driving is generally stable, but try to reduce sudden acceleration."

[0061] (Example 1)

[0062] 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."

[0063] Conventional operational information systems have presented challenges such as the complexity of the information received by end users, making immediate and effective responses difficult. Furthermore, risk prediction based on the external environment is insufficient, resulting in a lack of real-time advice to improve driving safety. Additionally, personalized support for end users is lacking, and specific improvement measures tailored to individual driving habits are not provided.

[0064] 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.

[0065] In this invention, the server includes means for collecting operational information, means for applying an artificial intelligence program for analyzing the operating status based on the operational information, and means for generating operational advice based on the analysis results and supplying it to the end user using natural language processing technology. This enables the end user to obtain information that is easy to understand in real time and to immediately perform safe driving.

[0066] "Operational information" refers to data that indicates the operating status of a vehicle, and includes information such as speed, position, and acceleration.

[0067] "Real-time" refers to a state where data generation, processing, and result delivery occur without delay.

[0068] An "artificial intelligence program" is a computer program that contains algorithms for automatically performing specific computational tasks.

[0069] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[0070] "External situational data" refers to data that externally affects vehicles in operation, including traffic information and weather data.

[0071] "Risk prediction" is the process of predicting potential future risks based on collected data.

[0072] "Personalized assistance" refers to specific support provided based on the individual user's characteristics and behavioral history.

[0073] A "machine learning model" is a computational model that learns from large amounts of data and performs predictions and classifications.

[0074] This invention relates to a system for collecting and analyzing operational information. This system provides end users with a safe driving environment by processing operational data obtained from terminals installed in vehicles on a server.

[0075] The terminal is installed in the vehicle and collects operational information using a wide variety of sensors. It utilizes speed sensors, GPS devices for obtaining location, and accelerometers to capture vehicle movement. This data is transmitted to the server in real time at regular intervals.

[0076] The server contains hardware that executes an artificial intelligence program based on data received from the terminal. The program incorporates a generative AI model, which is used to analyze driving conditions. Specifically, it analyzes driving data to determine if the vehicle's speed is within limits and whether there are any sudden accelerations or stops. Furthermore, natural language processing technology is used based on the analysis results to provide end-users with easily understandable feedback on the situation.

[0077] The feedback provided to end users is a message generated on the server and delivered as voice notifications or screen displays. In particular, it analyzes vehicle behavior in real time to improve driving safety and provides warnings as needed. Traffic information and weather data acquired from external sources are also integrated, enabling risk prediction.

[0078] For example, if heavy rain is predicted on a highway, a warning message such as "Heavy rain is predicted ahead. Please reduce your speed" will be sent to the end user. This system encourages users to drive safely and in an environmentally conscious manner.

[0079] An example of a prompt would be: "Have the AI ​​propose an algorithm that analyzes a large amount of operational data and provides real-time advice for safe driving. Please describe the necessary data requirements and the data processing process on the server in detail."

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] The terminal collects operational information from various sensors placed within the vehicle. It receives speed data from a speed sensor, location information from a GPS device, and acceleration data from an accelerometer as input. This data is converted into a digital format and collected at regular intervals. As output, the data is transmitted in real time to a server via a communication module.

[0083] Step 2:

[0084] The server receives operational data transmitted from the terminal. It acquires raw data regarding speed, position, and acceleration as input. Preprocessing, such as removing outliers and noise, is performed on this data to generate an analyzable dataset. This dataset becomes the output and is passed on to the next analysis process.

[0085] Step 3:

[0086] The server inputs pre-processed data into an artificial intelligence program and performs analysis using a generated AI model. It receives a formatted dataset as input and analyzes the vehicle's driving patterns using an AI algorithm. The data calculation evaluates the presence or absence of sudden braking or acceleration, the appropriateness of the speed, and the safety of the following distance. The output generates an evaluation of the driving situation.

[0087] Step 4:

[0088] The server generates driving advice and warning messages based on the analysis results. It uses evaluation results, traffic information obtained from external sources, and weather data as input. Utilizing a generation AI model and natural language processing, it generates messages in a format easily understood by end users. The output includes notification messages in text and audio formats.

[0089] Step 5:

[0090] The terminal receives messages sent from the server and notifies the user through its display and speaker. It takes messages in text or audio format as input. Specifically, it displays the message on the screen and outputs audio through the speaker, providing information to the user in real time. The output is the completion of the notification to the user.

[0091] (Application Example 1)

[0092] 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."

[0093] To improve safety and increase energy efficiency while operating vehicles, it is necessary to analyze driving conditions and external environmental information in real time and provide optimal driving adjustments and personalized support. However, conventional systems have limited information provided to drivers, making it difficult to warn them in advance of dangers caused by changes in weather or traffic conditions.

[0094] 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.

[0095] In this invention, the server includes means for receiving information in real time from a device that collects vehicle operation information, means for applying a machine intelligence algorithm for analyzing the driving situation based on the information, and means for generating driving advice based on the analysis results and providing it to the user using natural language processing. This enables the driver to receive real-time driving support that improves safety and energy efficiency.

[0096] "Vehicle operation information" refers to data collected by sensors during operation, such as vehicle speed, position, and acceleration.

[0097] A "means of receiving data in real time" refers to a function that enables the instantaneous receipt and processing of data without delay.

[0098] A "machine intelligence algorithm" is a set of computational procedures that computers use to analyze data and perform pattern recognition and prediction.

[0099] "Driving advice" refers to information that suggests optimal driving methods and points to be aware of to the driver.

[0100] "Natural language processing" is a technology that enables computers to understand and generate human language.

[0101] "User" refers to a driver or person involved with a vehicle who uses the system.

[0102] "External environmental information" refers to data about the surrounding conditions outside the vehicle, including traffic information and weather conditions.

[0103] A "means of predicting danger" refers to a function that analyzes potential future dangers based on collected data and issues warnings.

[0104] "Feedback" refers to evaluating the driving behavior of the user and providing a report on areas for improvement and positive aspects.

[0105] "Means of suggesting driving adjustments" refers to a function that indicates the optimal driving method to the driver in accordance with external conditions.

[0106] This invention is a driver assistance system for improving vehicle safety and energy efficiency. A server receives real-time operational information from multiple sensors installed in the vehicle, including vehicle speed, location, and acceleration. Furthermore, the server analyzes this data using machine intelligence algorithms to evaluate the driving situation. Based on this evaluation, it generates appropriate driving advice and presents it to the user using natural language processing.

[0107] Specifically, the system displays messages to the driver via screen and audio, alerting them to situations such as sudden braking or traffic congestion ahead. The server also collects weather and traffic information as external environmental data, enabling it to predict potential hazards. For example, if heavy rain is approaching on a highway, the system can warn the driver to slow down. This type of information provision makes it easier for drivers to make appropriate decisions.

[0108] The hardware used includes the vehicle's sensor system and GPS devices. For software, the Python programming language is used for data analysis. Existing libraries are often utilized for natural language processing. For example, when generating driving advice, a prompt such as "Please provide warnings inferred from the current driving data to help the autonomous vehicle drive safely" can be used for the generating AI model. This enables personalized, real-time driving assistance.

[0109] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0110] Step 1:

[0111] The server receives real-time operational information from sensors mounted on the vehicle. Inputs include vehicle speed, location information, and acceleration data. This data is then organized and formatted for analysis. The output is an analyzable dataset.

[0112] Step 2:

[0113] The server applies a machine intelligence algorithm based on the received operational information to analyze the driving conditions. The input is the dataset formatted in step 1. Data analysis is performed on this data to detect abnormal driving patterns and potential hazards. The output is information about the significant driving events that were discovered.

[0114] Step 3:

[0115] The server generates driving advice based on the analysis results. The input is the driving analysis information obtained in step 2. Based on this context, a generative AI model is used to create a message in natural language. An example of a prompt is "Based on the current driving data, what driving precautions should be provided?". This output is specific advice presented to the driver.

[0116] Step 4:

[0117] The server sends this generated driving advice to the terminal. The terminal conveys the information to the driver via a display or speaker. The input is the natural language message generated in step 3. Specifically, the message is displayed or read aloud. The output is driving caution and warning information provided to the driver.

[0118] Step 5:

[0119] The server acquires external environmental information and compares it with operational information to predict potential hazards. Inputs include traffic and weather data. The server analyzes the data to detect predicted hazards and generates necessary warning messages. The output is a warning message regarding the predicted hazards.

[0120] Step 6:

[0121] The server scores the driver's driving behavior and provides feedback. The input is long-term driving data. Through data analysis, the server evaluates the driver's behavior, identifying areas for improvement and positive aspects. The output is a feedback report provided to the driver.

[0122] 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.

[0123] This invention constructs a system that utilizes a sensor network installed within a vehicle to monitor the driver's emotional state in real time and provide driving advice based on that data. This system aims not only to improve driver safety and comfort but also to promote environmentally friendly driving styles.

[0124] In addition to conventional sensors that collect vehicle operation information, the terminal is equipped with an emotion engine that includes biosensors and camera devices. This emotion engine detects physiological indicators such as the driver's facial expressions, heart rate, and skin temperature, and analyzes this data to identify the driver's emotional state.

[0125] The terminal transmits this operational information and emotional data to the server in real time. The server uses AI algorithms to analyze the driving situation based on this data. This analysis takes into account not only vehicle operation patterns but also the influence of emotional state on driving.

[0126] The server generates driving advice based on the analysis results. This advice is optimized to suit the driver's current emotional state and converted into a user-friendly format using natural language processing technology. The terminal then provides this advice to the user via voice or display.

[0127] For example, if the driver is feeling stressed, the system can generate advice such as, "Take a deep breath to relax while driving." As the driver's emotional state improves, they will be able to drive more calmly and safely.

[0128] Furthermore, the server evaluates the driver's driving behavior and assigns a score that takes emotional data into account. This score is periodically provided to the user as feedback via the terminal. This feedback includes not only driving performance but also advice on managing emotions while driving.

[0129] This invention provides comprehensive feedback that takes user emotions into account, resulting in a system that promotes safe and efficient driving.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] The device collects vehicle operation information from speed sensors, accelerometers, and GPS devices. In addition, it uses biosensors and camera devices to collect data indicating the driver's emotions, such as heart rate, facial expressions, and skin temperature.

[0133] Step 2:

[0134] The terminal transmits collected operational information and sentiment data to the server. This data is appropriately packetized in real time and delivered to the server without delay.

[0135] Step 3:

[0136] The server applies AI algorithms to the received data to analyze driving conditions and the driver's emotional state. Specifically, it identifies signs of anxiety and stress that may influence driving patterns.

[0137] Step 4:

[0138] The server generates driving advice based on the analysis results. This advice is tailored to the driver's emotional state and optimized to improve driving quality.

[0139] Step 5:

[0140] The server uses natural language processing to translate the generated advice into a form that is easy for the user to understand.

[0141] Step 6:

[0142] The device provides the user with advice received from the server. This advice is conveyed through voice messages or on-screen displays. An example of advice tailored to the user's emotions might be, "Take a deep breath and relax."

[0143] Step 7:

[0144] The server evaluates the driver's driving behavior and assigns a score, taking emotional data into consideration. This score serves as a comprehensive indicator of the driver's performance.

[0145] Step 8:

[0146] The device provides users with regular feedback along with their scores. This feedback includes specific advice on driving performance and suggestions for emotional management.

[0147] (Example 2)

[0148] 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".

[0149] In recent years, there has been a growing demand for technologies that improve the safety and comfort of driving vehicles. However, conventional technologies have struggled to monitor drivers' emotional states in real time and provide appropriate advice based on that information. In particular, there has been a lack of comprehensive improvement measures that take into account the impact of drivers' emotions on their driving behavior. Therefore, there is a need for a system that reduces the impact of emotional changes on safe driving and provides advice tailored to individual drivers.

[0150] 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.

[0151] In this invention, the server includes means for collecting the driver's physiological data and emotional state from emotion sensors and camera devices mounted on the vehicle, means for transmitting the data and vehicle operation information to the server in real time, and means for applying an artificial intelligence algorithm for analyzing the driver's emotional state based on the data. This makes it possible to provide individualized and appropriate driving advice tailored to the driver's emotional state.

[0152] An "emotion sensor" is a measuring device used to detect the driver's physiological indicators in real time and analyze their emotional state.

[0153] A "camera device" is a video acquisition device that captures the driver's facial expressions and uses that facial expression data for emotion analysis.

[0154] "Physiological data" refers to biometric information that indicates the driver's physical condition, such as heart rate and skin temperature.

[0155] "Emotional state" refers to the driver's psychological state and is information that is monitored in real time.

[0156] An "artificial intelligence algorithm" is a computer technology used to analyze large amounts of data and detect specific patterns or trends.

[0157] "Natural language processing technology" is a computer technology that understands and generates human language, and is used to generate advice for users.

[0158] "Driving advice" refers to instructions and suggestions provided to support safe and efficient driving, based on the driver's emotional state and driving conditions.

[0159] "Scoring" is the process of quantifying and evaluating a driver's driving behavior.

[0160] "Feedback" refers to information provided to drivers, such as evaluation results and advice, to encourage improvements in driving behavior.

[0161] This invention is a vehicle system aimed at monitoring the driver's emotional state in real time and providing appropriate driving advice. The system consists of multiple sensors installed in the vehicle and the cooperation of a terminal and server that process the collected data.

[0162] First, the terminal uses emotion sensors and camera devices mounted in the vehicle to collect the driver's physiological data (heart rate, skin temperature, etc.) and facial expressions in real time. These sensors have high-precision biometric measurement capabilities and enable precise facial recognition. Biosensors and camera devices can be selected from a wide range of existing products.

[0163] The terminal collects data and simultaneously transmits it to a server using wireless communication technology. Mobile communication technologies such as Wi-Fi, 4G / LTE, or 5G are used for communication. Based on the received data, the server analyzes the driver's emotional state using machine learning algorithms and artificial intelligence (AI) technology. Specifically, deep learning frameworks (e.g., TENSORFLOW® and PyTorch) are used for the analysis. The AI ​​algorithms are pre-trained and can accurately determine the driver's emotional state.

[0164] Based on the analysis results, the server automatically generates driving advice. This advice is generated using natural language processing (NLP) technology and converted into a format that is easy for the driver to understand. OpenAI's GPT-3® is used as a representative generative AI model. In this process, an example of a prompt message to the user is, "Stress has been detected while driving. Please generate advice to encourage the driver to relax."

[0165] Ultimately, the device delivers the generated driving advice to the user via the in-car voice system or display. This allows the driver to receive personalized advice based on their emotional state, enabling calm and safe driving.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] Data collection by devices

[0169] The device uses emotion sensors and a camera to acquire the driver's physiological data and facial expressions in real time. This input data includes heart rate, skin temperature, and facial expression images. Based on this physiological data, the device preprocesses the data to estimate the driver's provisional emotional tendencies and converts the raw data into a format that is easy to organize. The output is processed sensor data.

[0170] Step 2:

[0171] Data transmission by terminal

[0172] The terminal transfers the organized sensor data to the server using wireless communication (e.g., Wi-Fi, 4G / LTE, 5G). The input to this step is pre-processed sensor data. The terminal packets this data and applies a protocol to send it to the server efficiently and securely. The output is the data arriving at the server.

[0173] Step 3:

[0174] Server-based data analysis

[0175] The server receives sensor data as input. It then uses machine learning algorithms and artificial intelligence techniques to analyze the driver's emotional state. This analysis uses deep learning models (e.g., TensorFlow, PyTorch) to evaluate the data multidimensionally. The resulting output is a quantitative indicator of the driver's emotional state.

[0176] Step 4:

[0177] Server-based generation of driving advice

[0178] The server uses the analyzed emotional state as input to generate advice for the driver. Natural language processing techniques are employed here. Specifically, a generative AI model (e.g., GPT-3) generates the most appropriate advice for the driver's state in natural language. The output is a text of advice optimized for the driver.

[0179] Step 5:

[0180] Providing advice via devices

[0181] The terminal receives advice messages sent from the server and conveys them to the user through the in-car voice assistant or display. The input for this step is the advice message from the server. The terminal sets this up on the voice output device or visual display and presents it to the driver in an easily understandable format. As output, the advice is provided in a format that the driver can recognize.

[0182] Step 6:

[0183] Server-based operational evaluation and feedback generation

[0184] The server integrates driver behavioral and emotional data to score driving performance. It uses analyzed driving and emotional data as input. The server considers past data, quantifies driving performance, and generates feedback based on the results. The output is an evaluation report that includes areas for improvement for the driver.

[0185] This enables the entire system to provide advice based on the driver's emotional state.

[0186] (Application Example 2)

[0187] 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."

[0188] Current autonomous driving systems prioritize safety and efficiency, but they lack the ability to respond to passenger comfort and changes in emotional state. As a result, they fail to make fine adjustments based on passengers' emotional state, making it difficult to provide a comfortable travel experience.

[0189] 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.

[0190] In this invention, the server includes means for receiving data in real time from a device that collects vehicle operation information, means for applying an artificial intelligence algorithm for analyzing the driving situation based on the data, and means for detecting the emotional state of passengers and adjusting the vehicle's route and in-vehicle environment based on that. This makes it possible to provide a comfortable travel experience that is adapted to the emotional state of passengers.

[0191] "Devices for collecting vehicle operation information" refer to sensors and communication devices installed to acquire data such as the vehicle's location, speed, and fuel consumption.

[0192] "Means of receiving data in real time" refers to software or hardware functions that enable the receipt of information transmitted from a vehicle without delay.

[0193] An "artificial intelligence algorithm for analyzing driving conditions" is a computational method that evaluates and analyzes driving behavior based on accumulated operational data, and derives methods to improve efficiency and safety.

[0194] "Means of providing information to users using natural language processing" refers to technologies that convert analysis results into language that is easy for humans to understand and present them as audio or text.

[0195] "External environmental data" refers to external information that affects vehicle operation, such as traffic conditions and weather conditions.

[0196] "Means of quantifying and providing opinions" refers to a system for representing driving behavior with standardized indicators and providing advice based on those results.

[0197] "Means of proposing driving methods aimed at improving energy efficiency" refers to a function that presents driving techniques to reduce fuel consumption and electricity usage.

[0198] "Means for detecting passengers' emotional states and adjusting the vehicle's route and in-vehicle environment based on those states" refers to a system for identifying changes in passengers' psychological state during operation and taking appropriate action.

[0199] To implement this invention, it is first necessary to collect operational information and passenger emotional states in real time using sensors and camera systems installed in the vehicle. This will allow the server to constantly understand the vehicle's location, speed, and the situation influenced by the external environment. Specifically, emotional states will be analyzed using data obtained by combining facial recognition technology using OpenCV and biosensors.

[0200] Next, this data is sent to a server, where it is analyzed using machine learning libraries such as TensorFlow and Keras. Based on this information, the server suggests appropriate driving routes and adjustments to the in-car environment. For example, if passengers are feeling stressed, it may suggest playing relaxing music.

[0201] The analyzed information is transformed into a user-friendly format using natural language processing technology. Throughout this process, the server uses a generative AI model to generate guidelines for providing the optimal user experience. Personalized advice and route adjustments are also provided in real time based on user actions.

[0202] One concrete example is a scenario where, during a long-distance journey, if passengers begin to feel fatigued, the system automatically adjusts the vehicle's temperature and seat angle, and suggests the optimal rest stop. An example of a prompt message used to generate these suggestions is: "Please explain how to analyze passenger emotions in real time in a home-use autonomous vehicle and provide a comfortable ride. Please provide examples based on specific methods and technologies." This would allow passengers to have a comfortable and safe travel experience.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The terminal collects operational information and passenger sentiment data using sensors and cameras mounted on the vehicle. This includes location information, speed, heart rate, and facial expressions. The collected data is temporarily stored on the terminal as raw data.

[0206] Step 2:

[0207] The terminal formats the collected raw data and sends it to the server in real time. The data is converted to JSON format and sent to the server via the communication network.

[0208] Step 3:

[0209] To analyze the received data, the server first uses OpenCV to identify the passenger's emotional state from the camera data. It then uses a facial recognition algorithm to quantify the facial expression data and classify it into specific emotional categories.

[0210] Step 4:

[0211] In parallel, the server analyzes data from biosensors and assesses passengers' stress levels based on physiological indicators such as heart rate and temperature. At this stage, a statistical model is used to weight each indicator.

[0212] Step 5:

[0213] Based on the analyzed emotional data, the server runs a generative AI model using TensorFlow and Keras to propose the optimal driving route and in-vehicle environment adjustments for passengers. This proposal is formed by a scoring algorithm based on emotional state and operational information.

[0214] Step 6:

[0215] The server translates the proposal results into language using natural language processing techniques and sends them to the terminal. Libraries such as NLTK and spaCy are used here, and the generated text message is formatted for the user to see as audio output or on a display.

[0216] Step 7:

[0217] The user reviews the provided information and takes action on the device as needed. If a new selection is made, that data is sent back from the device to the server, which then uses it for the next analysis.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] 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).

[0225] 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.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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".

[0234] This invention provides a system that collects operational data using sensors and GPS devices mounted on a vehicle, and analyzes driving conditions by applying an artificial intelligence algorithm based on that data. This system supports safe driving by presenting the analysis results to the user in an easy-to-understand manner using natural language processing and providing real-time driving advice and warnings.

[0235] The terminal collects information from various sensors in the vehicle. This information includes vehicle speed from the speed sensor, location information from the GPS device, and acceleration data from the accelerometer. This data is transmitted to the server at regular intervals.

[0236] The server immediately passes the received operational data to an artificial intelligence algorithm for analysis. This algorithm comprehensively evaluates the driving situation, analyzing whether speed and following distance are appropriate, and whether sudden braking or acceleration has occurred. Based on the analysis results, the server generates driving advice and warning messages.

[0237] The generated message is transformed into a form that is easy for the user to understand using natural language processing. The device then conveys this message to the user via screen display or audio. For example, if sudden braking is detected, the user will be notified with a message such as, "Sudden braking has been detected. Please maintain a safe distance from the vehicle in front."

[0238] Furthermore, the server scores the user's driving behavior and provides feedback. This score evaluates the driving pattern and informs the user of areas for improvement and strengths. This feedback is provided to the user periodically through the terminal.

[0239] The server also collects external traffic and weather data and predicts potential hazards based on this information. These predictions are also provided to the user, warning them in advance of any potential new hazards.

[0240] For example, if heavy rain is predicted while driving on a highway, the system will provide a warning such as, "Heavy rain is predicted ahead. Reduce your speed."

[0241] This invention provides drivers with real-time information and feedback, promoting safe driving and environmental considerations.

[0242] The following describes the processing flow.

[0243] Step 1:

[0244] The terminal collects speed, acceleration, and location data from the vehicle's installed speed sensor, accelerometer, and GPS device. This data is periodically sent to the server in batches.

[0245] Step 2:

[0246] The server applies an AI algorithm to analyze the received operational data. This algorithm evaluates the data to identify sudden braking, sudden acceleration, speeding, and inappropriate following distances.

[0247] Step 3:

[0248] The server generates driver advice based on the analysis results. This advice is then converted into an easily understandable format using natural language processing technology.

[0249] Step 4:

[0250] The terminal provides the user with advice received from the server via voice or display. Examples include instructions such as "Slow down" or "Maintain a safe distance from the vehicle in front."

[0251] Step 5:

[0252] The server collects and analyzes traffic conditions and weather forecast information through external databases and APIs, and generates warnings if there are potential hazards.

[0253] Step 6:

[0254] The device communicates warnings to the user. For example, based on future road conditions, it may provide warning messages such as, "There is traffic congestion ahead."

[0255] Step 7:

[0256] The server analyzes the user's past driving data, evaluates their driving behavior, and assigns a score. This score takes into account various aspects of driving and serves as an indicator for evaluating, for example, safety and fuel efficiency.

[0257] Step 8:

[0258] The device periodically provides the user with a driving score and feedback. The feedback includes specific suggestions, such as, "Your driving is generally stable, but try to reduce sudden acceleration."

[0259] (Example 1)

[0260] 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."

[0261] Conventional operational information systems have presented challenges such as the complexity of the information received by end users, making immediate and effective responses difficult. Furthermore, risk prediction based on the external environment is insufficient, resulting in a lack of real-time advice to improve driving safety. Additionally, personalized support for end users is lacking, and specific improvement measures tailored to individual driving habits are not provided.

[0262] 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.

[0263] In this invention, the server includes means for collecting operational information, means for applying an artificial intelligence program for analyzing the operating status based on the operational information, and means for generating operational advice based on the analysis results and supplying it to the end user using natural language processing technology. This enables the end user to obtain information that is easy to understand in real time and to immediately perform safe driving.

[0264] "Operational information" refers to data that indicates the operating status of a vehicle, and includes information such as speed, position, and acceleration.

[0265] "Real-time" refers to a state where data generation, processing, and result delivery occur without delay.

[0266] An "artificial intelligence program" is a computer program that contains algorithms for automatically performing specific computational tasks.

[0267] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[0268] "External situation data" refers to data that externally affects vehicles in operation, including traffic information and weather data.

[0269] "Risk prediction" is the process of predicting potential future risks based on collected data.

[0270] "Personalized assistance" refers to specific support provided based on the individual user's characteristics and behavioral history.

[0271] A "machine learning model" is a computational model that learns from large amounts of data and performs predictions and classifications.

[0272] This invention relates to a system for collecting and analyzing operational information. This system provides end users with a safe driving environment by processing operational data obtained from terminals installed in vehicles on a server.

[0273] The terminal is installed in the vehicle and collects operational information using a wide variety of sensors. It utilizes speed sensors, GPS devices for obtaining location, and accelerometers to capture vehicle movement. This data is transmitted to the server in real time at regular intervals.

[0274] The server contains hardware that executes an artificial intelligence program based on data received from the terminal. The program incorporates a generative AI model, which is used to analyze driving conditions. Specifically, it analyzes driving data to determine if the vehicle's speed is within limits and whether there are any sudden accelerations or stops. Furthermore, natural language processing technology is used based on the analysis results to provide end-users with easily understandable feedback on the situation.

[0275] The feedback provided to end users is a message generated on the server and delivered as voice notifications or screen displays. In particular, it analyzes vehicle behavior in real time to improve driving safety and provides warnings as needed. Traffic information and weather data acquired from external sources are also integrated, enabling risk prediction.

[0276] For example, if heavy rain is predicted on a highway, a warning message such as "Heavy rain is predicted ahead. Please reduce your speed" will be sent to the end user. This system encourages users to drive safely and in an environmentally conscious manner.

[0277] An example of a prompt would be: "Have the AI ​​propose an algorithm that analyzes a large amount of operational data and provides real-time advice for safe driving. Please describe the necessary data requirements and the data processing process on the server in detail."

[0278] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0279] Step 1:

[0280] The terminal collects operational information from various sensors placed within the vehicle. It receives speed data from a speed sensor, location information from a GPS device, and acceleration data from an accelerometer as input. This data is converted into a digital format and collected at regular intervals. As output, the data is transmitted in real time to a server via a communication module.

[0281] Step 2:

[0282] The server receives the operation data transmitted from the terminal. As input, it acquires raw data regarding speed, position, and acceleration. For these data, it performs preprocessing such as removing outliers and noise, and generates an analyzable data set. This data set serves as the output and is passed on to the next analysis process.

[0283] Step 3:

[0284] The server inputs the preprocessed data into an artificial intelligence program and performs analysis using the generated AI model. It receives the formatted data set as input and analyzes the driving pattern of the vehicle with an algorithm by AI. As data operations, it evaluates the presence or absence of sudden braking or sudden acceleration, the appropriateness of speed, and the safety of the inter-vehicle distance. As output, an evaluation result of the driving situation is generated.

[0285] Step 4:

[0286] The server generates driving advice and warning messages based on the analysis results. As input, it uses the evaluation results, traffic information obtained from outside, and weather data. Utilizing the generated AI model, it generates messages in a form that is easy for end-users to understand through natural language processing. As output, notification messages in text or voice format are created.

[0287] Step 5:

[0288] The terminal receives the message sent from the server and notifies the user through a display or a speaker. As input, it acquires messages in text or voice format. As specific operations, it displays the message on the display and outputs voice through the speaker, providing information to the user in real time. As output, the notification to the user is completed.

[0289] (Application Example 1)

[0290] 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."

[0291] To improve safety and increase energy efficiency while operating vehicles, it is necessary to analyze driving conditions and external environmental information in real time and provide optimal driving adjustments and personalized support. However, conventional systems have limited information provided to drivers, making it difficult to warn them in advance of dangers caused by changes in weather or traffic conditions.

[0292] 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.

[0293] In this invention, the server includes means for receiving information in real time from a device that collects vehicle operation information, means for applying a machine intelligence algorithm for analyzing the driving situation based on the information, and means for generating driving advice based on the analysis results and providing it to the user using natural language processing. This enables the driver to receive real-time driving support that improves safety and energy efficiency.

[0294] "Vehicle operation information" refers to data collected by sensors during operation, such as vehicle speed, position, and acceleration.

[0295] A "means of receiving data in real time" refers to a function that enables the instantaneous receipt and processing of data without delay.

[0296] A "machine intelligence algorithm" is a set of computational procedures that computers use to analyze data and perform pattern recognition and prediction.

[0297] "Driving advice" refers to information that suggests optimal driving methods and points to be aware of to the driver.

[0298] "Natural language processing" is a technology that enables computers to understand and generate human language.

[0299] "User" refers to a driver or person involved with a vehicle who uses the system.

[0300] "External environmental information" refers to data about the surrounding conditions outside the vehicle, including traffic information and weather conditions.

[0301] A "means of predicting danger" refers to a function that analyzes potential future dangers based on collected data and issues warnings.

[0302] "Feedback" refers to evaluating the driving behavior of the user and providing a report on areas for improvement and positive aspects.

[0303] "Means of suggesting driving adjustments" refers to a function that indicates the optimal driving method to the driver in accordance with external conditions.

[0304] This invention is a driver assistance system for improving vehicle safety and energy efficiency. A server receives real-time operational information from multiple sensors installed in the vehicle, including vehicle speed, location, and acceleration. Furthermore, the server analyzes this data using machine intelligence algorithms to evaluate the driving situation. Based on this evaluation, it generates appropriate driving advice and presents it to the user using natural language processing.

[0305] Specifically, the system displays messages to the driver via screen and audio, alerting them to situations such as sudden braking or traffic congestion ahead. The server also collects weather and traffic information as external environmental data, enabling it to predict potential hazards. For example, if heavy rain is approaching on a highway, the system can warn the driver to slow down. This type of information provision makes it easier for drivers to make appropriate decisions.

[0306] The hardware to be used includes the vehicle's sensor system and GPS device. As for software, the Python programming language is used for data analysis. Also, existing libraries are often utilized for natural language processing. As a specific example, when generating driving advice, a prompt sentence such as "Please indicate the precautions inferred from the current driving data for the automated vehicle to assist in safe driving" can be used for the generative AI model. This enables individualized real-time driving assistance.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server receives driving information from the sensors mounted on the vehicle in real time. The inputs include vehicle speed, position information, acceleration data, etc. These data are sorted and arranged into a format for analysis. As the output, an analyzable dataset is obtained.

[0310] Step 2:

[0311] The server applies a machine intelligence algorithm based on the received driving information to analyze the driving situation. The input is the dataset formatted in Step 1. Data analysis is performed on this data to detect abnormal driving patterns and potential risks. The output is information regarding the discovered important driving events.

[0312] Step 3:

[0313] The server generates driving advice based on the analysis results. The input is the driving analysis information obtained in Step 2. Based on this context, a natural language-formatted message is created using the generative AI model. An example of a prompt sentence is "Based on the current driving data, what are the driving precautions to be provided?" This output is the specific advice presented to the driver.

[0314] Step 4:

[0315] The server sends this generated driving advice to the terminal. The terminal conveys the information to the driver via a display or speaker. The input is the natural language message generated in step 3. Specifically, the message is displayed or read aloud. The output is driving caution and warning information provided to the driver.

[0316] Step 5:

[0317] The server acquires external environmental information and compares it with operational information to predict potential hazards. Inputs include traffic and weather data. The server analyzes the data to detect predicted hazards and generates necessary warning messages. The output is a warning message regarding the predicted hazards.

[0318] Step 6:

[0319] The server scores the driver's driving behavior and provides feedback. The input is long-term driving data. Through data analysis, the server evaluates the driver's behavior, identifying areas for improvement and positive aspects. The output is a feedback report provided to the driver.

[0320] 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.

[0321] This invention constructs a system that utilizes a sensor network installed within a vehicle to monitor the driver's emotional state in real time and provide driving advice based on that data. This system aims not only to improve driver safety and comfort but also to promote environmentally friendly driving styles.

[0322] In addition to conventional sensors that collect vehicle operation information, the terminal is equipped with an emotion engine that includes biosensors and camera devices. This emotion engine detects physiological indicators such as the driver's facial expressions, heart rate, and skin temperature, and analyzes this data to identify the driver's emotional state.

[0323] The terminal transmits this operational information and emotional data to the server in real time. The server uses AI algorithms to analyze the driving situation based on this data. This analysis takes into account not only vehicle operation patterns but also the influence of emotional state on driving.

[0324] The server generates driving advice based on the analysis results. This advice is optimized to suit the driver's current emotional state and converted into a user-friendly format using natural language processing technology. The terminal then provides this advice to the user via voice or display.

[0325] For example, if the driver is feeling stressed, the system can generate advice such as, "Take a deep breath to relax while driving." As the driver's emotional state improves, they will be able to drive more calmly and safely.

[0326] Furthermore, the server evaluates the driver's driving behavior and assigns a score that takes emotional data into account. This score is periodically provided to the user as feedback via the terminal. This feedback includes not only driving performance but also advice on managing emotions while driving.

[0327] This invention provides comprehensive feedback that takes user emotions into account, resulting in a system that promotes safe and efficient driving.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The device collects vehicle operation information from speed sensors, accelerometers, and GPS devices. In addition, it uses biosensors and camera devices to collect data indicating the driver's emotions, such as heart rate, facial expressions, and skin temperature.

[0331] Step 2:

[0332] The terminal transmits collected operational information and sentiment data to the server. This data is appropriately packetized in real time and delivered to the server without delay.

[0333] Step 3:

[0334] The server applies AI algorithms to the received data to analyze driving conditions and the driver's emotional state. Specifically, it identifies signs of anxiety and stress that may influence driving patterns.

[0335] Step 4:

[0336] The server generates driving advice based on the analysis results. This advice is tailored to the driver's emotional state and optimized to improve driving quality.

[0337] Step 5:

[0338] The server uses natural language processing to translate the generated advice into a form that is easy for the user to understand.

[0339] Step 6:

[0340] The device provides the user with advice received from the server. This advice is conveyed through voice messages or on-screen displays. An example of advice tailored to the user's emotions might be, "Take a deep breath and relax."

[0341] Step 7:

[0342] The server evaluates the driver's driving behavior and assigns a score, taking emotional data into consideration. This score serves as a comprehensive indicator of the driver's performance.

[0343] Step 8:

[0344] The device provides users with regular feedback along with their scores. This feedback includes specific advice on driving performance and suggestions for emotional management.

[0345] (Example 2)

[0346] 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".

[0347] In recent years, there has been a growing demand for technologies that improve the safety and comfort of driving vehicles. However, conventional technologies have struggled to monitor drivers' emotional states in real time and provide appropriate advice based on that information. In particular, there has been a lack of comprehensive improvement measures that take into account the impact of drivers' emotions on their driving behavior. Therefore, there is a need for a system that reduces the impact of emotional changes on safe driving and provides advice tailored to individual drivers.

[0348] 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.

[0349] In this invention, the server includes means for collecting the driver's physiological data and emotional state from emotion sensors and camera devices mounted on the vehicle, means for transmitting the data and vehicle operation information to the server in real time, and means for applying an artificial intelligence algorithm for analyzing the driver's emotional state based on the data. This makes it possible to provide individualized and appropriate driving advice tailored to the driver's emotional state.

[0350] An "emotion sensor" is a measuring device used to detect the driver's physiological indicators in real time and analyze their emotional state.

[0351] A "camera device" is a video acquisition device that captures the driver's facial expressions and uses that facial expression data for emotion analysis.

[0352] "Physiological data" refers to biometric information that indicates the driver's physical condition, such as heart rate and skin temperature.

[0353] "Emotional state" refers to the driver's psychological state and is information that is monitored in real time.

[0354] An "artificial intelligence algorithm" is a computer technology used to analyze large amounts of data and detect specific patterns or trends.

[0355] "Natural language processing technology" is a computer technology that understands and generates human language, and is used to generate advice for users.

[0356] "Driving advice" refers to instructions and suggestions provided to support safe and efficient driving, based on the driver's emotional state and driving conditions.

[0357] "Scoring" is the process of quantifying and evaluating a driver's driving behavior.

[0358] "Feedback" refers to information provided to drivers, such as evaluation results and advice, to encourage improvements in driving behavior.

[0359] This invention is a vehicle system aimed at monitoring the driver's emotional state in real time and providing appropriate driving advice. The system consists of multiple sensors installed in the vehicle and the cooperation of a terminal and server that process the collected data.

[0360] First, the terminal uses emotion sensors and camera devices mounted in the vehicle to collect the driver's physiological data (heart rate, skin temperature, etc.) and facial expressions in real time. These sensors have high-precision biometric measurement capabilities and enable precise facial recognition. Biosensors and camera devices can be selected from a wide range of existing products.

[0361] The terminal collects data and simultaneously transmits it to a server using wireless communication technology. Mobile communication technologies such as Wi-Fi, 4G / LTE, or 5G are used for communication. Based on the received data, the server analyzes the driver's emotional state using machine learning algorithms and artificial intelligence (AI) technology. Specifically, deep learning frameworks (e.g., TensorFlow and PyTorch) are used for the analysis. The AI ​​algorithms are pre-trained to accurately determine the driver's emotional state.

[0362] Based on the analysis results, the server automatically generates driving advice. This advice is generated using natural language processing (NLP) techniques and converted into a format that is easy for the driver to understand. OpenAI's GPT-3 is used as a representative generative AI model. In this process, an example of a prompt message to the user is, "Stress has been detected while driving. Please generate advice to encourage the driver to relax."

[0363] Ultimately, the device delivers the generated driving advice to the user via the in-car voice system or display. This allows the driver to receive personalized advice based on their emotional state, enabling calm and safe driving.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Data collection by devices

[0367] The device uses emotion sensors and a camera to acquire the driver's physiological data and facial expressions in real time. This input data includes heart rate, skin temperature, and facial expression images. Based on this physiological data, the device preprocesses the data to estimate the driver's provisional emotional tendencies and converts the raw data into a format that is easy to organize. The output is processed sensor data.

[0368] Step 2:

[0369] Data transmission by terminal

[0370] The terminal transfers the organized sensor data to the server using wireless communication (e.g., Wi-Fi, 4G / LTE, 5G). The input to this step is pre-processed sensor data. The terminal packets this data and applies a protocol to send it to the server efficiently and securely. The output is the data arriving at the server.

[0371] Step 3:

[0372] Server-based data analysis

[0373] The server receives sensor data as input. It then uses machine learning algorithms and artificial intelligence techniques to analyze the driver's emotional state. This analysis uses deep learning models (e.g., TensorFlow, PyTorch) to evaluate the data multidimensionally. The resulting output is a quantitative indicator of the driver's emotional state.

[0374] Step 4:

[0375] Server-based generation of driving advice

[0376] The server uses the analyzed emotional state as input to generate advice for the driver. Natural language processing techniques are employed here. Specifically, a generative AI model (e.g., GPT-3) generates the most appropriate advice for the driver's state in natural language. The output is a text of advice optimized for the driver.

[0377] Step 5:

[0378] Providing advice via devices

[0379] The terminal receives advice messages sent from the server and conveys them to the user through the in-car voice assistant or display. The input for this step is the advice message from the server. The terminal sets this up on the voice output device or visual display and presents it to the driver in an easily understandable format. As output, the advice is provided in a format that the driver can recognize.

[0380] Step 6:

[0381] Server-based operational evaluation and feedback generation

[0382] The server integrates driver behavioral and emotional data to score driving performance. It uses analyzed driving and emotional data as input. The server considers past data, quantifies driving performance, and generates feedback based on the results. The output is an evaluation report that includes areas for improvement for the driver.

[0383] This enables the entire system to provide advice based on the driver's emotional state.

[0384] (Application Example 2)

[0385] 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".

[0386] Current autonomous driving systems prioritize safety and efficiency, but they lack the ability to respond to passenger comfort and changes in emotional state. As a result, they fail to make fine adjustments based on passengers' emotional state, making it difficult to provide a comfortable travel experience.

[0387] 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.

[0388] In this invention, the server includes means for receiving data in real time from a device that collects vehicle operation information, means for applying an artificial intelligence algorithm for analyzing the driving situation based on the data, and means for detecting the emotional state of passengers and adjusting the vehicle's route and in-vehicle environment based on that. This makes it possible to provide a comfortable travel experience that is adapted to the emotional state of passengers.

[0389] "Devices for collecting vehicle operation information" refer to sensors and communication devices installed to acquire data such as the vehicle's location, speed, and fuel consumption.

[0390] "Means of receiving data in real time" refers to software or hardware functions that enable the receipt of information transmitted from a vehicle without delay.

[0391] An "artificial intelligence algorithm for analyzing driving conditions" is a computational method that evaluates and analyzes driving behavior based on accumulated operational data, and derives methods to improve efficiency and safety.

[0392] "Means of providing information to users using natural language processing" refers to technologies that convert analysis results into language that is easy for humans to understand and present them as audio or text.

[0393] "External environmental data" refers to external information that affects vehicle operation, such as traffic conditions and weather conditions.

[0394] "Means of quantifying and providing opinions" refers to a system for representing driving behavior with standardized indicators and providing advice based on those results.

[0395] "Means of proposing driving methods aimed at improving energy efficiency" refers to a function that presents driving techniques to reduce fuel consumption and electricity usage.

[0396] "Means for detecting passengers' emotional states and adjusting the vehicle's route and in-vehicle environment based on those states" refers to a system for identifying changes in passengers' psychological state during operation and taking appropriate action.

[0397] To implement this invention, it is first necessary to collect operational information and passenger emotional states in real time using sensors and camera systems installed in the vehicle. This will allow the server to constantly understand the vehicle's location, speed, and the situation influenced by the external environment. Specifically, emotional states will be analyzed using data obtained by combining facial recognition technology using OpenCV and biosensors.

[0398] Next, this data is sent to a server, where it is analyzed using machine learning libraries such as TensorFlow and Keras. Based on this information, the server suggests appropriate driving routes and adjustments to the in-car environment. For example, if passengers are feeling stressed, it may suggest playing relaxing music.

[0399] The analyzed information is transformed into a user-friendly format using natural language processing technology. Throughout this process, the server uses a generative AI model to generate guidelines for providing the optimal user experience. Personalized advice and route adjustments are also provided in real time based on user actions.

[0400] One concrete example is a scenario where, during a long-distance journey, if passengers begin to feel fatigued, the system automatically adjusts the vehicle's temperature and seat angle, and suggests the optimal rest stop. An example of a prompt message used to generate these suggestions is: "Please explain how to analyze passenger emotions in real time in a home-use autonomous vehicle and provide a comfortable ride. Please provide examples based on specific methods and technologies." This would allow passengers to have a comfortable and safe travel experience.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The terminal collects operational information and passenger sentiment data using sensors and cameras mounted on the vehicle. This includes location information, speed, heart rate, and facial expressions. The collected data is temporarily stored on the terminal as raw data.

[0404] Step 2:

[0405] The terminal formats the collected raw data and sends it to the server in real time. The data is converted to JSON format and sent to the server via the communication network.

[0406] Step 3:

[0407] To analyze the received data, the server first uses OpenCV to identify the passenger's emotional state from the camera data. It then uses a facial recognition algorithm to quantify the facial expression data and classify it into specific emotional categories.

[0408] Step 4:

[0409] In parallel, the server analyzes data from biosensors and assesses passengers' stress levels based on physiological indicators such as heart rate and temperature. At this stage, a statistical model is used to weight each indicator.

[0410] Step 5:

[0411] Based on the analyzed emotional data, the server runs a generative AI model using TensorFlow and Keras to propose the optimal driving route and in-vehicle environment adjustments for passengers. This proposal is formed by a scoring algorithm based on emotional state and operational information.

[0412] Step 6:

[0413] The server translates the proposal results into language using natural language processing techniques and sends them to the terminal. Libraries such as NLTK and spaCy are used here, and the generated text message is formatted for the user to see as audio output or on a display.

[0414] Step 7:

[0415] The user reviews the provided information and takes action on the device as needed. If a new selection is made, that data is sent back from the device to the server, which then uses it for the next analysis.

[0416] 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.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0418] 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.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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".

[0432] This invention provides a system that collects operational data using sensors and GPS devices mounted on a vehicle, and analyzes driving conditions by applying an artificial intelligence algorithm based on that data. This system supports safe driving by presenting the analysis results to the user in an easy-to-understand manner using natural language processing and providing real-time driving advice and warnings.

[0433] The terminal collects information from various sensors in the vehicle. This information includes vehicle speed from the speed sensor, location information from the GPS device, and acceleration data from the accelerometer. This data is transmitted to the server at regular intervals.

[0434] The server immediately passes the received operational data to an artificial intelligence algorithm for analysis. This algorithm comprehensively evaluates the driving situation, analyzing whether speed and following distance are appropriate, and whether sudden braking or acceleration has occurred. Based on the analysis results, the server generates driving advice and warning messages.

[0435] The generated message is transformed into a form that is easy for the user to understand using natural language processing. The device then conveys this message to the user via screen display or audio. For example, if sudden braking is detected, the user will be notified with a message such as, "Sudden braking has been detected. Please maintain a safe distance from the vehicle in front."

[0436] Furthermore, the server scores the user's driving behavior and provides feedback. This score evaluates the driving pattern and informs the user of areas for improvement and strengths. This feedback is provided to the user periodically through the terminal.

[0437] The server also collects external traffic and weather data and predicts potential hazards based on this information. These predictions are also provided to the user, warning them in advance of any potential new hazards.

[0438] For example, if heavy rain is predicted while driving on a highway, the system will provide a warning such as, "Heavy rain is predicted ahead. Reduce your speed."

[0439] This invention provides drivers with real-time information and feedback, promoting safe driving and environmental considerations.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The terminal collects speed, acceleration, and location data from the vehicle's installed speed sensor, accelerometer, and GPS device. This data is periodically sent to the server in batches.

[0443] Step 2:

[0444] The server applies an AI algorithm to analyze the received operational data. This algorithm evaluates the data to identify sudden braking, sudden acceleration, speeding, and inappropriate following distances.

[0445] Step 3:

[0446] The server generates driver advice based on the analysis results. This advice is then converted into an easily understandable format using natural language processing technology.

[0447] Step 4:

[0448] The terminal provides the user with advice received from the server via voice or display. Examples include instructions such as "Slow down" or "Maintain a safe distance from the vehicle in front."

[0449] Step 5:

[0450] The server collects and analyzes traffic conditions and weather forecast information through external databases and APIs, and generates warnings if there are potential hazards.

[0451] Step 6:

[0452] The device communicates warnings to the user. For example, based on future road conditions, it may provide warning messages such as, "There is traffic congestion ahead."

[0453] Step 7:

[0454] The server analyzes the user's past driving data, evaluates their driving behavior, and assigns a score. This score takes into account various aspects of driving and serves as an indicator for evaluating, for example, safety and fuel efficiency.

[0455] Step 8:

[0456] The device periodically provides the user with a driving score and feedback. The feedback includes specific suggestions, such as, "Your driving is generally stable, but try to reduce sudden acceleration."

[0457] (Example 1)

[0458] 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."

[0459] Conventional operational information systems have presented challenges such as the complexity of the information received by end users, making immediate and effective responses difficult. Furthermore, risk prediction based on the external environment is insufficient, resulting in a lack of real-time advice to improve driving safety. Additionally, personalized support for end users is lacking, and specific improvement measures tailored to individual driving habits are not provided.

[0460] 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.

[0461] In this invention, the server includes means for collecting operational information, means for applying an artificial intelligence program for analyzing the operating status based on the operational information, and means for generating operational advice based on the analysis results and supplying it to the end user using natural language processing technology. This enables the end user to obtain information that is easy to understand in real time and to immediately perform safe driving.

[0462] "Operational information" refers to data that indicates the operating status of a vehicle, and includes information such as speed, position, and acceleration.

[0463] "Real-time" refers to a state where data generation, processing, and result delivery occur without delay.

[0464] An "artificial intelligence program" is a computer program that contains algorithms for automatically performing specific computational tasks.

[0465] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[0466] "External situation data" refers to data that externally affects vehicles in operation, including traffic information and weather data.

[0467] "Risk prediction" is the process of predicting potential future risks based on collected data.

[0468] "Personalized assistance" refers to specific support provided based on the individual user's characteristics and behavioral history.

[0469] A "machine learning model" is a computational model that learns from large amounts of data and performs predictions and classifications.

[0470] This invention relates to a system for collecting and analyzing operational information. This system provides end users with a safe driving environment by processing operational data obtained from terminals installed in vehicles on a server.

[0471] The terminal is installed in the vehicle and collects operational information using a wide variety of sensors. It utilizes speed sensors, GPS devices for obtaining location, and accelerometers to capture vehicle movement. This data is transmitted to the server in real time at regular intervals.

[0472] The server contains hardware that executes an artificial intelligence program based on data received from the terminal. The program incorporates a generative AI model, which is used to analyze driving conditions. Specifically, it analyzes driving data to determine if the vehicle's speed is within limits and whether there are any sudden accelerations or stops. Furthermore, natural language processing technology is used based on the analysis results to provide end-users with easily understandable feedback on the situation.

[0473] The feedback provided to end users is a message generated on the server and delivered as voice notifications or screen displays. In particular, it analyzes vehicle behavior in real time to improve driving safety and provides warnings as needed. Traffic information and weather data acquired from external sources are also integrated, enabling risk prediction.

[0474] For example, if heavy rain is predicted on a highway, a warning message such as "Heavy rain is predicted ahead. Please reduce your speed" will be sent to the end user. This system encourages users to drive safely and in an environmentally conscious manner.

[0475] An example of a prompt would be: "Have the AI ​​propose an algorithm that analyzes a large amount of operational data and provides real-time advice for safe driving. Please describe the necessary data requirements and the data processing process on the server in detail."

[0476] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0477] Step 1:

[0478] The terminal collects operational information from various sensors placed within the vehicle. It receives speed data from a speed sensor, location information from a GPS device, and acceleration data from an accelerometer as input. This data is converted into a digital format and collected at regular intervals. As output, the data is transmitted in real time to a server via a communication module.

[0479] Step 2:

[0480] The server receives operational data transmitted from the terminal. It acquires raw data regarding speed, position, and acceleration as input. Preprocessing, such as removing outliers and noise, is performed on this data to generate an analyzable dataset. This dataset becomes the output and is passed on to the next analysis process.

[0481] Step 3:

[0482] The server inputs pre-processed data into an artificial intelligence program and performs analysis using a generated AI model. It receives a formatted dataset as input and analyzes the vehicle's driving patterns using an AI algorithm. The data calculation evaluates the presence or absence of sudden braking or acceleration, the appropriateness of the speed, and the safety of the following distance. The output generates an evaluation of the driving situation.

[0483] Step 4:

[0484] The server generates driving advice and warning messages based on the analysis results. It uses evaluation results, traffic information obtained from external sources, and weather data as input. Utilizing a generation AI model and natural language processing, it generates messages in a format easily understood by end users. The output includes notification messages in text and audio formats.

[0485] Step 5:

[0486] The terminal receives messages sent from the server and notifies the user through its display and speaker. It takes messages in text or audio format as input. Specifically, it displays the message on the screen and outputs audio through the speaker, providing information to the user in real time. The output is the completion of the notification to the user.

[0487] (Application Example 1)

[0488] 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."

[0489] To improve safety and increase energy efficiency while operating vehicles, it is necessary to analyze driving conditions and external environmental information in real time and provide optimal driving adjustments and personalized support. However, conventional systems have limited information provided to drivers, making it difficult to warn them in advance of dangers caused by changes in weather or traffic conditions.

[0490] 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.

[0491] In this invention, the server includes means for receiving information in real time from a device that collects vehicle operation information, means for applying a machine intelligence algorithm for analyzing the driving situation based on the information, and means for generating driving advice based on the analysis results and providing it to the user using natural language processing. This enables the driver to receive real-time driving support that improves safety and energy efficiency.

[0492] "Vehicle operation information" refers to data collected by sensors during operation, such as vehicle speed, position, and acceleration.

[0493] A "means of receiving data in real time" refers to a function that enables the instantaneous receipt and processing of data without delay.

[0494] A "machine intelligence algorithm" is a set of computational procedures that computers use to analyze data and perform pattern recognition and prediction.

[0495] "Driving advice" refers to information that suggests optimal driving methods and points to be aware of to the driver.

[0496] "Natural language processing" is a technology that enables computers to understand and generate human language.

[0497] "User" refers to a driver or person involved with a vehicle who uses the system.

[0498] "External environmental information" refers to data about the surrounding conditions outside the vehicle, including traffic information and weather conditions.

[0499] A "means of predicting danger" refers to a function that analyzes potential future dangers based on collected data and issues warnings.

[0500] "Feedback" refers to evaluating the driving behavior of the user and providing a report on areas for improvement and positive aspects.

[0501] "Means of suggesting driving adjustments" refers to a function that indicates the optimal driving method to the driver in accordance with external conditions.

[0502] This invention is a driver assistance system for improving vehicle safety and energy efficiency. A server receives real-time operational information from multiple sensors installed in the vehicle, including vehicle speed, location, and acceleration. Furthermore, the server analyzes this data using machine intelligence algorithms to evaluate the driving situation. Based on this evaluation, it generates appropriate driving advice and presents it to the user using natural language processing.

[0503] Specifically, the system displays messages to the driver via screen and audio, alerting them to situations such as sudden braking or traffic congestion ahead. The server also collects weather and traffic information as external environmental data, enabling it to predict potential hazards. For example, if heavy rain is approaching on a highway, the system can warn the driver to slow down. This type of information provision makes it easier for drivers to make appropriate decisions.

[0504] The hardware used includes the vehicle's sensor system and GPS devices. For software, the Python programming language is used for data analysis. Existing libraries are often utilized for natural language processing. For example, when generating driving advice, a prompt such as "Please provide warnings inferred from the current driving data to help the autonomous vehicle drive safely" can be used for the generating AI model. This enables personalized, real-time driving assistance.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The server receives real-time operational information from sensors mounted on the vehicle. Inputs include vehicle speed, location information, and acceleration data. This data is then organized and formatted for analysis. The output is an analyzable dataset.

[0508] Step 2:

[0509] The server applies a machine intelligence algorithm based on the received operational information to analyze the driving conditions. The input is the dataset formatted in step 1. Data analysis is performed on this data to detect abnormal driving patterns and potential hazards. The output is information about the significant driving events that were discovered.

[0510] Step 3:

[0511] The server generates driving advice based on the analysis results. The input is the driving analysis information obtained in step 2. Based on this context, a generative AI model is used to create a message in natural language. An example of a prompt is "Based on the current driving data, what driving precautions should be provided?". This output is specific advice presented to the driver.

[0512] Step 4:

[0513] The server sends this generated driving advice to the terminal. The terminal conveys the information to the driver via a display or speaker. The input is the natural language message generated in step 3. Specifically, the message is displayed or read aloud. The output is driving caution and warning information provided to the driver.

[0514] Step 5:

[0515] The server acquires external environmental information and compares it with operational information to predict potential hazards. Inputs include traffic and weather data. The server analyzes the data to detect predicted hazards and generates necessary warning messages. The output is a warning message regarding the predicted hazards.

[0516] Step 6:

[0517] The server scores the driver's driving behavior and provides feedback. The input is long-term driving data. Through data analysis, the server evaluates the driver's behavior, identifying areas for improvement and positive aspects. The output is a feedback report provided to the driver.

[0518] 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.

[0519] This invention constructs a system that utilizes a sensor network installed within a vehicle to monitor the driver's emotional state in real time and provide driving advice based on that data. This system aims not only to improve driver safety and comfort but also to promote environmentally friendly driving styles.

[0520] In addition to conventional sensors that collect vehicle operation information, the terminal is equipped with an emotion engine that includes biosensors and camera devices. This emotion engine detects physiological indicators such as the driver's facial expressions, heart rate, and skin temperature, and analyzes this data to identify the driver's emotional state.

[0521] The terminal transmits this operational information and emotional data to the server in real time. The server uses AI algorithms to analyze driving conditions based on this data. This analysis takes into account not only vehicle operation patterns but also the influence of emotional states on driving.

[0522] The server generates driving advice based on the analysis results. This advice is optimized to suit the driver's current emotional state and converted into a user-friendly format using natural language processing technology. The terminal then provides this advice to the user via voice or display.

[0523] For example, if the driver is feeling stressed, the system can generate advice such as, "Take a deep breath to relax while driving." As their emotional state improves, they will be able to drive more calmly and safely.

[0524] Furthermore, the server evaluates the driver's driving behavior and assigns a score that takes emotional data into account. This score is periodically provided to the user as feedback via the terminal. This feedback includes not only driving performance but also advice on managing emotions while driving.

[0525] This invention provides comprehensive feedback that takes user emotions into account, resulting in a system that promotes safe and efficient driving.

[0526] The following describes the processing flow.

[0527] Step 1:

[0528] The device collects vehicle operation information from speed sensors, accelerometers, and GPS devices. In addition, it uses biosensors and camera devices to collect data indicating the driver's emotions, such as heart rate, facial expressions, and skin temperature.

[0529] Step 2:

[0530] The terminal transmits collected operational information and sentiment data to the server. This data is appropriately packetized in real time and delivered to the server without delay.

[0531] Step 3:

[0532] The server applies AI algorithms to the received data to analyze driving conditions and the driver's emotional state. Specifically, it identifies signs of anxiety and stress that may influence driving patterns.

[0533] Step 4:

[0534] The server generates driving advice based on the analysis results. This advice is tailored to the driver's emotional state and optimized to improve driving quality.

[0535] Step 5:

[0536] The server uses natural language processing to translate the generated advice into a form that is easy for the user to understand.

[0537] Step 6:

[0538] The device provides the user with advice received from the server. This advice is conveyed through voice messages or on-screen displays. An example of advice tailored to the user's emotions might be, "Take a deep breath and relax."

[0539] Step 7:

[0540] The server evaluates the driver's driving behavior and assigns a score, taking emotional data into consideration. This score serves as a comprehensive indicator of the driver's performance.

[0541] Step 8:

[0542] The device provides users with regular feedback along with their scores. This feedback includes specific advice on driving performance and suggestions for emotional management.

[0543] (Example 2)

[0544] 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."

[0545] In recent years, there has been a growing demand for technologies that improve the safety and comfort of driving vehicles. However, conventional technologies have struggled to monitor drivers' emotional states in real time and provide appropriate advice based on that information. In particular, there has been a lack of comprehensive improvement measures that take into account the impact of drivers' emotions on their driving behavior. Therefore, there is a need for a system that reduces the impact of emotional changes on safe driving and provides advice tailored to individual drivers.

[0546] 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.

[0547] In this invention, the server includes means for collecting the driver's physiological data and emotional state from emotion sensors and camera devices mounted on the vehicle, means for transmitting the data and vehicle operation information to the server in real time, and means for applying an artificial intelligence algorithm for analyzing the driver's emotional state based on the data. This makes it possible to provide individualized and appropriate driving advice tailored to the driver's emotional state.

[0548] An "emotion sensor" is a measuring device used to detect the driver's physiological indicators in real time and analyze their emotional state.

[0549] A "camera device" is a video acquisition device that captures the driver's facial expressions and uses that facial expression data for emotion analysis.

[0550] "Physiological data" refers to biometric information that indicates the driver's physical condition, such as heart rate and skin temperature.

[0551] "Emotional state" refers to the driver's psychological state and is information that is monitored in real time.

[0552] An "artificial intelligence algorithm" is a computer technology used to analyze large amounts of data and detect specific patterns or trends.

[0553] "Natural language processing technology" is a computer technology that understands and generates human language, and is used to generate advice for users.

[0554] "Driving advice" refers to instructions and suggestions provided to support safe and efficient driving, based on the driver's emotional state and driving conditions.

[0555] "Scoring" is the process of quantifying and evaluating a driver's driving behavior.

[0556] "Feedback" refers to information provided to drivers, such as evaluation results and advice, to encourage improvements in driving behavior.

[0557] This invention is a vehicle system aimed at monitoring the driver's emotional state in real time and providing appropriate driving advice. The system consists of multiple sensors installed in the vehicle and the cooperation of a terminal and server that process the collected data.

[0558] First, the terminal uses emotion sensors and camera devices mounted in the vehicle to collect the driver's physiological data (heart rate, skin temperature, etc.) and facial expressions in real time. These sensors have high-precision biometric measurement capabilities and enable precise facial recognition. Biosensors and camera devices can be selected from a wide range of existing products.

[0559] The terminal collects data and simultaneously transmits it to a server using wireless communication technology. Mobile communication technologies such as Wi-Fi, 4G / LTE, or 5G are used for communication. Based on the received data, the server analyzes the driver's emotional state using machine learning algorithms and artificial intelligence (AI) technology. Specifically, deep learning frameworks (e.g., TensorFlow and PyTorch) are used for the analysis. The AI ​​algorithms are pre-trained to accurately determine the driver's emotional state.

[0560] Based on the analysis results, the server automatically generates driving advice. This advice is generated using natural language processing (NLP) techniques and converted into a format that is easy for the driver to understand. OpenAI's GPT-3 is used as a representative generative AI model. In this process, an example of a prompt message to the user is, "Stress has been detected while driving. Please generate advice to encourage the driver to relax."

[0561] Ultimately, the device provides the generated driving advice to the user through the in-car voice system or display. This allows the driver to receive personalized advice based on their emotional state, enabling calm and safe driving.

[0562] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0563] Step 1:

[0564] Data collection by devices

[0565] The device uses emotion sensors and a camera to acquire the driver's physiological data and facial expressions in real time. This input data includes heart rate, skin temperature, and facial expression images. Based on this physiological data, the device preprocesses the data to estimate the driver's provisional emotional tendencies and converts the raw data into a format that is easy to organize. The output is processed sensor data.

[0566] Step 2:

[0567] Data transmission by terminal

[0568] The terminal transfers the organized sensor data to the server using wireless communication (e.g., Wi-Fi, 4G / LTE, 5G). The input to this step is pre-processed sensor data. The terminal packets this data and applies a protocol to send it to the server efficiently and securely. The output is the data arriving at the server.

[0569] Step 3:

[0570] Server-based data analysis

[0571] The server receives sensor data as input. It then uses machine learning algorithms and artificial intelligence techniques to analyze the driver's emotional state. This analysis uses deep learning models (e.g., TensorFlow, PyTorch) to evaluate the data multidimensionally. The resulting output is a quantitative indicator of the driver's emotional state.

[0572] Step 4:

[0573] Server-based generation of driving advice

[0574] The server uses the analyzed emotional state as input to generate advice for the driver. Natural language processing techniques are employed here. Specifically, a generative AI model (e.g., GPT-3) generates the most appropriate advice for the driver's state in natural language. The output is a text of advice optimized for the driver.

[0575] Step 5:

[0576] Providing advice via devices

[0577] The terminal receives advice messages sent from the server and conveys them to the user through the in-car voice assistant or display. The input for this step is the advice message from the server. The terminal sets this up on the voice output device or visual display and presents it to the driver in an easily understandable format. As output, the advice is provided in a format that the driver can recognize.

[0578] Step 6:

[0579] Server-based operational evaluation and feedback generation

[0580] The server integrates driver behavioral and emotional data to score driving performance. It uses analyzed driving and emotional data as input. The server considers past data, quantifies driving performance, and generates feedback based on the results. The output is an evaluation report that includes areas for improvement for the driver.

[0581] This enables the entire system to provide advice based on the driver's emotional state.

[0582] (Application Example 2)

[0583] 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."

[0584] Current autonomous driving systems prioritize safety and efficiency, but they lack the ability to respond to passenger comfort and changes in emotional state. As a result, they fail to make fine adjustments based on passengers' emotional state, making it difficult to provide a comfortable travel experience.

[0585] 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.

[0586] In this invention, the server includes means for receiving data in real time from a device that collects vehicle operation information, means for applying an artificial intelligence algorithm for analyzing the driving situation based on the data, and means for detecting the emotional state of passengers and adjusting the vehicle's route and in-vehicle environment based on that. This makes it possible to provide a comfortable travel experience that is adapted to the emotional state of passengers.

[0587] "Devices for collecting vehicle operation information" refer to sensors and communication devices installed to acquire data such as the vehicle's location, speed, and fuel consumption.

[0588] "Means of receiving data in real time" refers to software or hardware functions that enable the receipt of information transmitted from a vehicle without delay.

[0589] An "artificial intelligence algorithm for analyzing driving conditions" is a computational method that evaluates and analyzes driving behavior based on accumulated operational data, and derives methods to improve efficiency and safety.

[0590] "Means of providing information to users using natural language processing" refers to technologies that convert analysis results into language that is easy for humans to understand and present them as audio or text.

[0591] "External environmental data" refers to external information that affects vehicle operation, such as traffic conditions and weather conditions.

[0592] "Means of quantifying and providing opinions" refers to a system for representing driving behavior with standardized indicators and providing advice based on those results.

[0593] "Means of proposing driving methods aimed at improving energy efficiency" refers to a function that presents driving techniques to reduce fuel consumption and electricity usage.

[0594] "Means for detecting passengers' emotional states and adjusting the vehicle's route and in-vehicle environment based on those states" refers to a system for identifying changes in passengers' psychological state during operation and taking appropriate action.

[0595] To implement this invention, it is first necessary to collect operational information and passenger emotional states in real time using sensors and camera systems installed in the vehicle. This will allow the server to constantly understand the vehicle's location, speed, and the situation influenced by the external environment. Specifically, emotional states will be analyzed using data obtained by combining facial recognition technology using OpenCV and biosensors.

[0596] Next, this data is sent to a server, where it is analyzed using machine learning libraries such as TensorFlow and Keras. Based on this information, the server suggests appropriate driving routes and adjustments to the in-car environment. For example, if passengers are feeling stressed, it may suggest playing relaxing music.

[0597] The analyzed information is transformed into a user-friendly format using natural language processing technology. Throughout this process, the server uses a generative AI model to generate guidelines for providing the optimal user experience. Personalized advice and route adjustments are also provided in real time based on user actions.

[0598] One concrete example is a scenario where, during a long-distance journey, if passengers begin to feel fatigued, the system automatically adjusts the vehicle's temperature and seat angle, and suggests the optimal rest stop. An example of a prompt message used to generate these suggestions is: "Please explain how to analyze passenger emotions in real time in a home-use autonomous vehicle and provide a comfortable ride. Please provide examples based on specific methods and technologies." This would allow passengers to have a comfortable and safe travel experience.

[0599] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0600] Step 1:

[0601] The terminal collects operational information and passenger sentiment data using sensors and cameras mounted on the vehicle. This includes location information, speed, heart rate, and facial expressions. The collected data is temporarily stored on the terminal as raw data.

[0602] Step 2:

[0603] The terminal formats the collected raw data and sends it to the server in real time. The data is converted to JSON format and sent to the server via the communication network.

[0604] Step 3:

[0605] To analyze the received data, the server first uses OpenCV to identify the passenger's emotional state from the camera data. It then uses a facial recognition algorithm to quantify the facial expression data and classify it into specific emotional categories.

[0606] Step 4:

[0607] In parallel, the server analyzes data from biosensors and assesses passengers' stress levels based on physiological indicators such as heart rate and temperature. At this stage, a statistical model is used to weight each indicator.

[0608] Step 5:

[0609] Based on the analyzed emotional data, the server runs a generative AI model using TensorFlow and Keras to propose the optimal driving route and in-vehicle environment adjustments for passengers. This proposal is formed by a scoring algorithm based on emotional state and operational information.

[0610] Step 6:

[0611] The server translates the proposal results into language using natural language processing techniques and sends them to the terminal. Libraries such as NLTK and spaCy are used here, and the generated text message is formatted for the user to see as audio output or on a display.

[0612] Step 7:

[0613] The user reviews the provided information and takes action on the device as needed. If a new selection is made, that data is sent back from the device to the server, which then uses it for the next analysis.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] [Fourth Embodiment]

[0618] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0619] 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.

[0620] 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).

[0621] 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.

[0622] 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.

[0623] 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).

[0624] 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.

[0625] 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.

[0626] 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.

[0627] 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.

[0628] 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.

[0629] 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.

[0630] 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".

[0631] This invention provides a system that collects operational data using sensors and GPS devices mounted on a vehicle, and analyzes driving conditions by applying an artificial intelligence algorithm based on that data. This system supports safe driving by presenting the analysis results to the user in an easy-to-understand manner using natural language processing and providing real-time driving advice and warnings.

[0632] The terminal collects information from various sensors in the vehicle. This information includes vehicle speed from the speed sensor, location information from the GPS device, and acceleration data from the accelerometer. This data is transmitted to the server at regular intervals.

[0633] The server immediately passes the received operational data to an artificial intelligence algorithm for analysis. This algorithm comprehensively evaluates the driving situation, analyzing whether speed and following distance are appropriate, and whether sudden braking or acceleration has occurred. Based on the analysis results, the server generates driving advice and warning messages.

[0634] The generated message is transformed into a form that is easy for the user to understand using natural language processing. The device then conveys this message to the user via screen display or audio. For example, if sudden braking is detected, the user will be notified with a message such as, "Sudden braking has been detected. Please maintain a safe distance from the vehicle in front."

[0635] Furthermore, the server scores the user's driving behavior and provides feedback. This score evaluates the driving pattern and informs the user of areas for improvement and strengths. This feedback is provided to the user periodically through the terminal.

[0636] The server also collects external traffic and weather data and predicts potential hazards based on this information. These predictions are also provided to the user, warning them in advance of any potential new hazards.

[0637] For example, if heavy rain is predicted while driving on a highway, the system will provide a warning such as, "Heavy rain is predicted ahead. Reduce your speed."

[0638] This invention provides drivers with real-time information and feedback, promoting safe driving and environmental considerations.

[0639] The following describes the processing flow.

[0640] Step 1:

[0641] The terminal collects speed, acceleration, and location data from the vehicle's installed speed sensor, accelerometer, and GPS device. This data is periodically sent to the server in batches.

[0642] Step 2:

[0643] The server applies an AI algorithm to analyze the received operational data. This algorithm evaluates the data to identify sudden braking, sudden acceleration, speeding, and inappropriate following distances.

[0644] Step 3:

[0645] The server generates driver advice based on the analysis results. This advice is then converted into an easily understandable format using natural language processing technology.

[0646] Step 4:

[0647] The terminal provides the user with advice received from the server via voice or display. Examples include instructions such as "Slow down" or "Maintain a safe distance from the vehicle in front."

[0648] Step 5:

[0649] The server collects and analyzes traffic conditions and weather forecast information through external databases and APIs, and generates warnings if there are potential hazards.

[0650] Step 6:

[0651] The device communicates warnings to the user. For example, based on future road conditions, it may provide warning messages such as, "There is traffic congestion ahead."

[0652] Step 7:

[0653] The server analyzes the user's past driving data, evaluates their driving behavior, and assigns a score. This score takes into account various aspects of driving and serves as an indicator for evaluating, for example, safety and fuel efficiency.

[0654] Step 8:

[0655] The device periodically provides the user with a driving score and feedback. The feedback includes specific suggestions, such as, "Your driving is generally stable, but try to reduce sudden acceleration."

[0656] (Example 1)

[0657] 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".

[0658] Conventional operational information systems have presented challenges such as the complexity of the information received by end users, making immediate and effective responses difficult. Furthermore, risk prediction based on the external environment is insufficient, resulting in a lack of real-time advice to improve driving safety. Additionally, personalized support for end users is lacking, and specific improvement measures tailored to individual driving habits are not provided.

[0659] 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.

[0660] In this invention, the server includes means for collecting operational information, means for applying an artificial intelligence program for analyzing the operating status based on the operational information, and means for generating operational advice based on the analysis results and supplying it to the end user using natural language processing technology. This enables the end user to obtain information that is easy to understand in real time and to immediately perform safe driving.

[0661] "Operational information" refers to data that indicates the operating status of a vehicle, and includes information such as speed, position, and acceleration.

[0662] "Real-time" refers to a state where data generation, processing, and result delivery occur without delay.

[0663] An "artificial intelligence program" is a computer program that contains algorithms for automatically performing specific computational tasks.

[0664] "Natural language processing technology" is a technology that enables computers to understand and generate human language.

[0665] "External situation data" refers to data that externally affects vehicles in operation, including traffic information and weather data.

[0666] "Risk prediction" is the process of predicting potential future risks based on collected data.

[0667] "Personalized assistance" refers to specific support provided based on the individual user's characteristics and behavioral history.

[0668] A "machine learning model" is a computational model that learns from large amounts of data and performs predictions and classifications.

[0669] This invention relates to a system for collecting and analyzing operational information. This system provides end users with a safe driving environment by processing operational data obtained from terminals installed in vehicles on a server.

[0670] The terminal is installed in the vehicle and collects operational information using a wide variety of sensors. It utilizes speed sensors, GPS devices for obtaining location, and accelerometers to capture vehicle movement. This data is transmitted to the server in real time at regular intervals.

[0671] The server contains hardware that executes an artificial intelligence program based on data received from the terminal. The program incorporates a generative AI model, which is used to analyze driving conditions. Specifically, it analyzes driving data to determine if the vehicle's speed is within limits and whether there are any sudden accelerations or stops. Furthermore, natural language processing technology is used based on the analysis results to provide end-users with easily understandable feedback on the situation.

[0672] The feedback provided to end users is a message generated on the server and delivered as voice notifications or screen displays. In particular, it analyzes vehicle behavior in real time to improve driving safety and provides warnings as needed. Traffic information and weather data acquired from external sources are also integrated, enabling risk prediction.

[0673] For example, if heavy rain is predicted on a highway, a warning message such as "Heavy rain is predicted ahead. Please reduce your speed" will be sent to the end user. This system encourages users to drive safely and in an environmentally conscious manner.

[0674] An example of a prompt would be: "Have the AI ​​propose an algorithm that analyzes a large amount of operational data and provides real-time advice for safe driving. Please describe the necessary data requirements and the data processing process on the server in detail."

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The terminal collects operational information from various sensors placed within the vehicle. It receives speed data from a speed sensor, location information from a GPS device, and acceleration data from an accelerometer as input. This data is converted into a digital format and collected at regular intervals. As output, the data is transmitted in real time to a server via a communication module.

[0678] Step 2:

[0679] The server receives operational data transmitted from the terminal. It acquires raw data regarding speed, position, and acceleration as input. Preprocessing, such as removing outliers and noise, is performed on this data to generate an analyzable dataset. This dataset becomes the output and is passed on to the next analysis process.

[0680] Step 3:

[0681] The server inputs pre-processed data into an artificial intelligence program and performs analysis using a generated AI model. It receives a formatted dataset as input and analyzes the vehicle's driving patterns using an AI algorithm. The data calculation evaluates the presence or absence of sudden braking or acceleration, the appropriateness of the speed, and the safety of the following distance. The output generates an evaluation of the driving situation.

[0682] Step 4:

[0683] The server generates driving advice and warning messages based on the analysis results. It uses evaluation results, traffic information obtained from external sources, and weather data as input. Utilizing a generation AI model and natural language processing, it generates messages in a format easily understood by end users. The output includes notification messages in text and audio formats.

[0684] Step 5:

[0685] The terminal receives messages sent from the server and notifies the user through its display and speaker. It takes messages in text or audio format as input. Specifically, it displays the message on the screen and outputs audio through the speaker, providing information to the user in real time. The output is the completion of the notification to the user.

[0686] (Application Example 1)

[0687] 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".

[0688] To improve safety and increase energy efficiency while operating vehicles, it is necessary to analyze driving conditions and external environmental information in real time and provide optimal driving adjustments and personalized support. However, conventional systems have limited information provided to drivers, making it difficult to warn them in advance of dangers caused by changes in weather or traffic conditions.

[0689] 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.

[0690] In this invention, the server includes means for receiving information in real time from a device that collects vehicle operation information, means for applying a machine intelligence algorithm for analyzing the driving situation based on the information, and means for generating driving advice based on the analysis results and providing it to the user using natural language processing. This enables the driver to receive real-time driving support that improves safety and energy efficiency.

[0691] "Vehicle operation information" refers to data collected by sensors during operation, such as vehicle speed, position, and acceleration.

[0692] A "means of receiving data in real time" refers to a function that enables the instantaneous receipt and processing of data without delay.

[0693] A "machine intelligence algorithm" is a set of computational procedures that computers use to analyze data and perform pattern recognition and prediction.

[0694] "Driving advice" refers to information that suggests optimal driving methods and points to be aware of to the driver.

[0695] "Natural language processing" is a technology that enables computers to understand and generate human language.

[0696] "User" refers to a driver or person involved with a vehicle who uses the system.

[0697] "External environmental information" refers to data about the surrounding conditions outside the vehicle, including traffic information and weather conditions.

[0698] A "means of predicting danger" refers to a function that analyzes potential future dangers based on collected data and issues warnings.

[0699] "Feedback" refers to evaluating the driving behavior of the user and providing a report on areas for improvement and positive aspects.

[0700] "Means of suggesting driving adjustments" refers to a function that indicates the optimal driving method to the driver in accordance with external conditions.

[0701] This invention is a driver assistance system for improving vehicle safety and energy efficiency. A server receives real-time operational information from multiple sensors installed in the vehicle, including vehicle speed, location, and acceleration. Furthermore, the server analyzes this data using machine intelligence algorithms to evaluate the driving situation. Based on this evaluation, it generates appropriate driving advice and presents it to the user using natural language processing.

[0702] Specifically, the system displays messages to the driver via screen and audio, alerting them to situations such as sudden braking or traffic congestion ahead. The server also collects weather and traffic information as external environmental data, enabling it to predict potential hazards. For example, if heavy rain is approaching on a highway, the system can warn the driver to slow down. This type of information provision makes it easier for drivers to make appropriate decisions.

[0703] The hardware used includes the vehicle's sensor system and GPS devices. For software, the Python programming language is used for data analysis. Existing libraries are often utilized for natural language processing. For example, when generating driving advice, a prompt such as "Please provide warnings inferred from the current driving data to help the autonomous vehicle drive safely" can be used for the generating AI model. This enables personalized, real-time driving assistance.

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The server receives real-time operational information from sensors mounted on the vehicle. Inputs include vehicle speed, location information, and acceleration data. This data is then organized and formatted for analysis. The output is an analyzable dataset.

[0707] Step 2:

[0708] The server applies a machine intelligence algorithm based on the received operational information to analyze the driving conditions. The input is the dataset formatted in step 1. Data analysis is performed on this data to detect abnormal driving patterns and potential hazards. The output is information about the significant driving events that were discovered.

[0709] Step 3:

[0710] The server generates driving advice based on the analysis results. The input is the driving analysis information obtained in step 2. Based on this context, a generative AI model is used to create a message in natural language. An example of a prompt is "Based on the current driving data, what driving precautions should be provided?". This output is specific advice presented to the driver.

[0711] Step 4:

[0712] The server sends this generated driving advice to the terminal. The terminal conveys the information to the driver via a display or speaker. The input is the natural language message generated in step 3. Specifically, the message is displayed or read aloud. The output is driving caution and warning information provided to the driver.

[0713] Step 5:

[0714] The server acquires external environmental information and compares it with operational information to predict potential hazards. Inputs include traffic and weather data. The server analyzes the data to detect predicted hazards and generates necessary warning messages. The output is a warning message regarding the predicted hazards.

[0715] Step 6:

[0716] The server scores the driver's driving behavior and provides feedback. The input is long-term driving data. Through data analysis, the server evaluates the driver's behavior, identifying areas for improvement and positive aspects. The output is a feedback report provided to the driver.

[0717] 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.

[0718] This invention constructs a system that utilizes a sensor network installed within a vehicle to monitor the driver's emotional state in real time and provide driving advice based on that data. This system aims not only to improve driver safety and comfort but also to promote environmentally friendly driving styles.

[0719] In addition to conventional sensors that collect vehicle operation information, the terminal is equipped with an emotion engine that includes biosensors and camera devices. This emotion engine detects physiological indicators such as the driver's facial expressions, heart rate, and skin temperature, and analyzes this data to identify the driver's emotional state.

[0720] The terminal transmits this operational information and emotional data to the server in real time. The server uses AI algorithms to analyze the driving situation based on this data. This analysis takes into account not only vehicle operation patterns but also the influence of emotional state on driving.

[0721] The server generates driving advice based on the analysis results. This advice is optimized to suit the driver's current emotional state and converted into a user-friendly format using natural language processing technology. The terminal then provides this advice to the user via voice or display.

[0722] For example, if the driver is feeling stressed, the system can generate advice such as, "Take a deep breath to relax while driving." As the driver's emotional state improves, they will be able to drive more calmly and safely.

[0723] Furthermore, the server evaluates the driver's driving behavior and assigns a score that takes emotional data into account. This score is periodically provided to the user as feedback via the terminal. This feedback includes not only driving performance but also advice on managing emotions while driving.

[0724] This invention provides comprehensive feedback that takes user emotions into account, resulting in a system that promotes safe and efficient driving.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The device collects vehicle operation information from speed sensors, accelerometers, and GPS devices. In addition, it uses biosensors and camera devices to collect data indicating the driver's emotions, such as heart rate, facial expressions, and skin temperature.

[0728] Step 2:

[0729] The terminal transmits collected operational information and sentiment data to the server. This data is appropriately packetized in real time and delivered to the server without delay.

[0730] Step 3:

[0731] The server applies AI algorithms to the received data to analyze driving conditions and the driver's emotional state. Specifically, it identifies signs of anxiety and stress that may influence driving patterns.

[0732] Step 4:

[0733] The server generates driving advice based on the analysis results. This advice is tailored to the driver's emotional state and optimized to improve driving quality.

[0734] Step 5:

[0735] The server uses natural language processing to translate the generated advice into a form that is easy for the user to understand.

[0736] Step 6:

[0737] The device provides the user with advice received from the server. This advice is conveyed through voice messages or on-screen displays. An example of advice tailored to the user's emotions might be, "Take a deep breath and relax."

[0738] Step 7:

[0739] The server evaluates the driver's driving behavior and assigns a score, taking emotional data into consideration. This score serves as a comprehensive indicator of the driver's performance.

[0740] Step 8:

[0741] The device provides users with regular feedback along with their scores. This feedback includes specific advice on driving performance and suggestions for emotional management.

[0742] (Example 2)

[0743] 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".

[0744] In recent years, there has been a growing demand for technologies that improve the safety and comfort of driving vehicles. However, conventional technologies have struggled to monitor drivers' emotional states in real time and provide appropriate advice based on that information. In particular, there has been a lack of comprehensive improvement measures that take into account the impact of drivers' emotions on their driving behavior. Therefore, there is a need for a system that reduces the impact of emotional changes on safe driving and provides advice tailored to individual drivers.

[0745] 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.

[0746] In this invention, the server includes means for collecting the driver's physiological data and emotional state from emotion sensors and camera devices mounted on the vehicle, means for transmitting the data and vehicle operation information to the server in real time, and means for applying an artificial intelligence algorithm for analyzing the driver's emotional state based on the data. This makes it possible to provide individualized and appropriate driving advice tailored to the driver's emotional state.

[0747] An "emotion sensor" is a measuring device used to detect the driver's physiological indicators in real time and analyze their emotional state.

[0748] A "camera device" is a video acquisition device that captures the driver's facial expressions and uses that facial expression data for emotion analysis.

[0749] "Physiological data" refers to biometric information that indicates the driver's physical condition, such as heart rate and skin temperature.

[0750] "Emotional state" refers to the driver's psychological state and is information that is monitored in real time.

[0751] An "artificial intelligence algorithm" is a computer technology used to analyze large amounts of data and detect specific patterns or trends.

[0752] "Natural language processing technology" is a computer technology that understands and generates human language, and is used to generate advice for users.

[0753] "Driving advice" refers to instructions and suggestions provided to support safe and efficient driving, based on the driver's emotional state and driving conditions.

[0754] "Scoring" is the process of quantifying and evaluating a driver's driving behavior.

[0755] "Feedback" refers to information provided to drivers, such as evaluation results and advice, to encourage improvements in driving behavior.

[0756] This invention is a vehicle system aimed at monitoring the driver's emotional state in real time and providing appropriate driving advice. The system consists of multiple sensors installed in the vehicle and the cooperation of a terminal and server that process the collected data.

[0757] First, the terminal uses emotion sensors and camera devices mounted in the vehicle to collect the driver's physiological data (heart rate, skin temperature, etc.) and facial expressions in real time. These sensors have high-precision biometric measurement capabilities and enable precise facial recognition. Biosensors and camera devices can be selected from a wide range of existing products.

[0758] The terminal collects data and simultaneously transmits it to a server using wireless communication technology. Mobile communication technologies such as Wi-Fi, 4G / LTE, or 5G are used for communication. Based on the received data, the server analyzes the driver's emotional state using machine learning algorithms and artificial intelligence (AI) technology. Specifically, deep learning frameworks (e.g., TensorFlow and PyTorch) are used for the analysis. The AI ​​algorithms are pre-trained to accurately determine the driver's emotional state.

[0759] Based on the analysis results, the server automatically generates driving advice. This advice is generated using natural language processing (NLP) techniques and converted into a format that is easy for the driver to understand. OpenAI's GPT-3 is used as a representative generative AI model. In this process, an example of a prompt message to the user is, "Stress has been detected while driving. Please generate advice to encourage the driver to relax."

[0760] Ultimately, the device provides the generated driving advice to the user through the in-car voice system or display. This allows the driver to receive personalized advice based on their emotional state, enabling calm and safe driving.

[0761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0762] Step 1:

[0763] Data collection by devices

[0764] The device uses emotion sensors and a camera to acquire the driver's physiological data and facial expressions in real time. This input data includes heart rate, skin temperature, and facial expression images. Based on this physiological data, the device preprocesses the data to estimate the driver's provisional emotional tendencies and converts the raw data into a format that is easy to organize. The output is processed sensor data.

[0765] Step 2:

[0766] Data transmission by terminal

[0767] The terminal transfers the organized sensor data to the server using wireless communication (e.g., Wi-Fi, 4G / LTE, 5G). The input to this step is pre-processed sensor data. The terminal packets this data and applies a protocol to send it to the server efficiently and securely. The output is the data arriving at the server.

[0768] Step 3:

[0769] Server-based data analysis

[0770] The server receives sensor data as input. It then uses machine learning algorithms and artificial intelligence techniques to analyze the driver's emotional state. This analysis uses deep learning models (e.g., TensorFlow, PyTorch) to evaluate the data multidimensionally. The resulting output is a quantitative indicator of the driver's emotional state.

[0771] Step 4:

[0772] Server-based generation of driving advice

[0773] The server uses the analyzed emotional state as input to generate advice for the driver. Natural language processing techniques are employed here. Specifically, a generative AI model (e.g., GPT-3) generates the most appropriate advice for the driver's state in natural language. The output is a text of advice optimized for the driver.

[0774] Step 5:

[0775] Providing advice via devices

[0776] The terminal receives advice messages sent from the server and conveys them to the user through the in-car voice assistant or display. The input for this step is the advice message from the server. The terminal sets this up on the voice output device or visual display and presents it to the driver in an easily understandable format. As output, the advice is provided in a format that the driver can recognize.

[0777] Step 6:

[0778] Server-based operational evaluation and feedback generation

[0779] The server integrates driver behavioral and emotional data to score driving performance. It uses analyzed driving and emotional data as input. The server considers past data, quantifies driving performance, and generates feedback based on the results. The output is an evaluation report that includes areas for improvement for the driver.

[0780] This enables the entire system to provide advice based on the driver's emotional state.

[0781] (Application Example 2)

[0782] 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".

[0783] Current autonomous driving systems prioritize safety and efficiency, but they lack the ability to respond to passenger comfort and changes in emotional state. As a result, they fail to make fine adjustments based on passengers' emotional state, making it difficult to provide a comfortable travel experience.

[0784] 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.

[0785] In this invention, the server includes means for receiving data in real time from a device that collects vehicle operation information, means for applying an artificial intelligence algorithm for analyzing the driving situation based on the data, and means for detecting the emotional state of passengers and adjusting the vehicle's route and in-vehicle environment based on that. This makes it possible to provide a comfortable travel experience that is adapted to the emotional state of passengers.

[0786] "Devices for collecting vehicle operation information" refer to sensors and communication devices installed to acquire data such as the vehicle's location, speed, and fuel consumption.

[0787] "Means of receiving data in real time" refers to software or hardware functions that enable the receipt of information transmitted from a vehicle without delay.

[0788] An "artificial intelligence algorithm for analyzing driving conditions" is a computational method that evaluates and analyzes driving behavior based on accumulated operational data, and derives methods to improve efficiency and safety.

[0789] "Means of providing information to users using natural language processing" refers to technologies that convert analysis results into language that is easy for humans to understand and present them as audio or text.

[0790] "External environmental data" refers to external information that affects vehicle operation, such as traffic conditions and weather conditions.

[0791] "Means of quantifying and providing opinions" refers to a system for representing driving behavior with standardized indicators and providing advice based on those results.

[0792] "Means of proposing driving methods aimed at improving energy efficiency" refers to a function that presents driving techniques to reduce fuel consumption and electricity usage.

[0793] "Means for detecting passengers' emotional states and adjusting the vehicle's route and in-vehicle environment based on those states" refers to a system for identifying changes in passengers' psychological state during operation and taking appropriate action.

[0794] To implement this invention, it is first necessary to collect operational information and passenger emotional states in real time using sensors and camera systems installed in the vehicle. This will allow the server to constantly understand the vehicle's location, speed, and the situation influenced by the external environment. Specifically, emotional states will be analyzed using data obtained by combining facial recognition technology using OpenCV and biosensors.

[0795] Next, this data is sent to a server, where it is analyzed using machine learning libraries such as TensorFlow and Keras. Based on this information, the server suggests appropriate driving routes and adjustments to the in-car environment. For example, if passengers are feeling stressed, it may suggest playing relaxing music.

[0796] The analyzed information is transformed into a user-friendly format using natural language processing technology. Throughout this process, the server uses a generative AI model to generate guidelines for providing the optimal user experience. Personalized advice and route adjustments are also provided in real time based on user actions.

[0797] One concrete example is a scenario where, during a long-distance journey, if passengers begin to feel fatigued, the system automatically adjusts the vehicle's temperature and seat angle, and suggests the optimal rest stop. An example of a prompt message used to generate these suggestions is: "Please explain how to analyze passenger emotions in real time in a home-use autonomous vehicle and provide a comfortable ride. Please provide examples based on specific methods and technologies." This would allow passengers to have a comfortable and safe travel experience.

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] The terminal collects operational information and passenger sentiment data using sensors and cameras mounted on the vehicle. This includes location information, speed, heart rate, and facial expressions. The collected data is temporarily stored on the terminal as raw data.

[0801] Step 2:

[0802] The terminal formats the collected raw data and sends it to the server in real time. The data is converted to JSON format and sent to the server via the communication network.

[0803] Step 3:

[0804] To analyze the received data, the server first uses OpenCV to identify the passenger's emotional state from the camera data. It then uses a facial recognition algorithm to quantify the facial expression data and classify it into specific emotional categories.

[0805] Step 4:

[0806] In parallel, the server analyzes data from biosensors and assesses passengers' stress levels based on physiological indicators such as heart rate and temperature. At this stage, a statistical model is used to weight each indicator.

[0807] Step 5:

[0808] Based on the analyzed emotional data, the server runs a generative AI model using TensorFlow and Keras to propose the optimal driving route and in-vehicle environment adjustments for passengers. This proposal is formed by a scoring algorithm based on emotional state and operational information.

[0809] Step 6:

[0810] The server translates the proposal results into language using natural language processing techniques and sends them to the terminal. Libraries such as NLTK and spaCy are used here, and the generated text message is formatted for the user to see as audio output or on a display.

[0811] Step 7:

[0812] The user reviews the provided information and takes action on the device as needed. If a new selection is made, that data is sent back from the device to the server, which then uses it for the next analysis.

[0813] 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.

[0814] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0815] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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."

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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.

[0833] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0834] The following is further disclosed regarding the embodiments described above.

[0835] (Claim 1)

[0836] A means for receiving data in real time from a device that collects vehicle operation information,

[0837] A means for applying an artificial intelligence algorithm to analyze the driving conditions based on the aforementioned data,

[0838] A means for generating driving advice based on the aforementioned analysis results and providing it to the user using natural language processing,

[0839] A means for predicting danger from the aforementioned operational information and external environmental data and providing warnings to the user,

[0840] A means for evaluating, scoring, and providing feedback on the user's driving behavior,

[0841] Means for proposing driving methods aimed at improving energy efficiency,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, which learns user driving behavior data using a machine learning model and provides personalized support.

[0845] (Claim 3)

[0846] The system according to claim 1, wherein the external environmental data includes traffic information and weather information.

[0847] "Example 1"

[0848] (Claim 1)

[0849] A means of collecting operational information,

[0850] A means of receiving the aforementioned operational information in real time,

[0851] A means for applying an artificial intelligence program to analyze the operating status based on the aforementioned operational information,

[0852] A means for generating operational advice based on the aforementioned analysis results and supplying it to the end user using natural language processing technology,

[0853] A means for inferring risks from the aforementioned operational information and external situation data, and supplying warnings to end users,

[0854] A means of evaluating end-user behavior, quantifying it, and providing feedback,

[0855] As external situational data, a means of incorporating traffic information and weather conditions,

[0856] A means of proposing an operating method aimed at improving energy efficiency,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, which learns end-user behavior data using a machine learning model in order to provide personalized assistance.

[0860] (Claim 3)

[0861] The system according to claim 1, which provides the generated warnings and advice to the end user visually and audibly.

[0862] "Application Example 1"

[0863] (Claim 1)

[0864] A means for receiving information in real time from a device that collects vehicle operation information,

[0865] A means for applying a machine intelligence algorithm to analyze the driving conditions based on the aforementioned information,

[0866] A means for generating driving advice based on the aforementioned analysis results and providing it to the user using natural language processing,

[0867] A means for predicting danger from the aforementioned operational information and external environmental information and providing warnings to users,

[0868] A means of evaluating the user's driving behavior, scoring it, and providing feedback,

[0869] A means of proposing optimal driving adjustments to users, including weather conditions in the aforementioned external environmental information,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, which learns the user's driving behavior information using a data analysis model and provides personalized support.

[0873] (Claim 3)

[0874] The system according to claim 1, wherein the external environmental information includes traffic information and marine information.

[0875] "Example 2 of combining an emotion engine"

[0876] (Claim 1)

[0877] A means for collecting the driver's physiological data and emotional state from emotion sensors and camera devices mounted on the vehicle,

[0878] Means for transmitting the aforementioned data and vehicle operation information to a server in real time,

[0879] A means for applying an artificial intelligence algorithm to analyze the driver's emotional state based on the aforementioned data,

[0880] A means for generating driving advice that takes into account the impact of analyzed emotional states on driving,

[0881] A means of providing generated driving advice in a format easily understood by the user using natural language processing technology,

[0882] A means for evaluating driving behavior that takes into account the driver's emotional state, scoring it, and providing feedback,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, which generates personalized driving advice based on the user's emotional state.

[0886] (Claim 3)

[0887] The system according to claim 1, which provides advice to promote calm and safe driving by analyzing the emotional state.

[0888] "Application example 2 when combining with an emotional engine"

[0889] (Claim 1)

[0890] A means for receiving data in real time from a device that collects vehicle operation information,

[0891] A means for applying an artificial intelligence algorithm to analyze the driving conditions based on the aforementioned data,

[0892] A means for generating driving advice based on the aforementioned analysis results and providing it to the user using natural language processing,

[0893] A means for predicting danger from the aforementioned operational information and external environmental data and providing warnings to users,

[0894] A means of evaluating, quantifying, and providing feedback on the user's driving behavior,

[0895] Means for proposing driving methods aimed at improving energy efficiency,

[0896] A means for detecting the emotional state of passengers and adjusting the vehicle's route and in-vehicle environment based on that,

[0897] A system that includes this.

[0898] (Claim 2)

[0899] The system according to claim 1, which learns user driving behavior data and passenger emotional states using a machine learning model and provides personalized support.

[0900] (Claim 3)

[0901] The system according to claim 1, wherein the external environmental data includes traffic information and weather information. [Explanation of symbols]

[0902] 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 means for receiving information in real time from a device that collects vehicle operation information, A means for applying a machine intelligence algorithm to analyze the driving conditions based on the aforementioned information, A means for generating driving advice based on the aforementioned analysis results and providing it to the user using natural language processing, A means for predicting danger from the aforementioned operational information and external environmental information and providing warnings to users, A means of evaluating the user's driving behavior, scoring it, and providing feedback, A means of proposing optimal driving adjustments to users, including weather conditions in the aforementioned external environmental information, A system that includes this.

2. The system according to claim 1, which learns the user's driving behavior information using a data analysis model and provides personalized support.

3. The system according to claim 1, wherein the external environmental information includes traffic information and marine information.

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