System

Abstract

A system is provided.SOLUTION: A system comprising: means for recording a driver's driving behavior in real-time; means for analyzing the recorded driving behavior data by artificial intelligence to generate an analysis result; means for notifying the driver of a driving improvement proposal based on the analysis result; and means for visually displaying the analysis result to a user after the end of driving to prompt a review.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In order to objectively understand a driver's driving behavior and promote safe driving, detailed collection and analysis of driving behavior data is necessary. However, conventional systems have difficulty in collecting driving data insufficiently or analyzing it too slowly, making it difficult to propose improvements in real time. Another issue is that drivers have few opportunities to reflect on their own driving behavior, resulting in low awareness of driving improvement. The purpose of this invention is to solve these issues and provide a safe driving support system with improved immediacy and accuracy. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for recording a driver's driving behavior in real time, a means for analyzing the recorded driving behavior data using artificial intelligence, a means for notifying the driver of driving improvement suggestions based on the analysis results, and a means for visually displaying the analysis results to the user after driving to encourage reflection. This system periodically transmits collected driving behavior data to a server, which analyzes the driving behavior data to identify driving risk factors and generate specific improvement suggestions. Furthermore, the system transmits the generated driving improvement suggestions to the user via push notification, and uses an encrypted communication protocol for securely transmitting the driving data, thereby ensuring data security and displaying the user's driving history as charts and graphs. This allows the driver to reflect on their driving behavior and identify areas for improvement for their next drive, thereby increasing their awareness of safe driving.

[0006] "Driving behavior" refers to the actions and reactions of a driver when operating a vehicle, and specifically includes speed changes, acceleration, deceleration, turning, braking, etc.

[0007] "Real-time recording means" refers to a function or device for continuously and immediately collecting and storing data during operation.

[0008] "Generative artificial intelligence" refers to a machine learning model that uses algorithms to perform pattern recognition and prediction based on large amounts of data.

[0009] "Means for analysis" refers to methods and devices for processing collected data and extracting meaningful information.

[0010] "Driving improvement suggestions" refers to specific advice and instructions to the driver based on the analysis results, which are used to suggest areas for improving their driving behavior.

[0011] "Visual display means" refers to a method or device for presenting data or analytical results to a user in a graphical format that is easy to view.

[0012] "Problems" refer to problems that have not been solved by conventional technologies or areas that require improvement. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, and a means for providing feedback to users at appropriate times.

[0035] System Configuration

[0036] 1. Device (smartphone)

[0037] The device used is a smartphone. The smartphone is equipped with sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0038] 2. Server

[0039] Data recorded during driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results.

[0040] 3. Means of notification

[0041] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0042] Overview of program processing

[0043] Driving behavior recording

[0044] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, etc. in real time. For example, if the user suddenly accelerates the vehicle, the accelerometer immediately records this data and stores it on the device.

[0045] Data transmission and analysis

[0046] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding.

[0047] Generate and notify improvement suggestions

[0048] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, specific advice such as "increase the distance between your vehicle and the vehicle in front" is generated and sent to the device as a push notification. The device receives this and displays it to the user.

[0049] Review and improvement

[0050] Once the drive is over, the device displays the collected data and the server's analysis results in graphs and charts to the user. This allows the user to reflect on their driving style and understand what improvements they need to make next time. For example, based on past driving history, the device may display information such as "10 sudden braking attempts in one week," encouraging the user to be more careful the next time they drive.

[0051] Specific examples

[0052] For example, suppose User A places their smartphone in a car holder and starts driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to the server. The server uses generative AI to analyze the collected data and determines that User A has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends these to the device as a push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing User A to reflect on their driving behavior and identify areas for improvement for their next drive.

[0053] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and make an effort to drive safely based on specific suggestions for improvement.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[0057] Step 2:

[0058] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[0059] Step 3:

[0060] The terminal processes the collected data in batches at regular intervals (for example, every minute) and sends it to the server. The SSL / TLS protocol is used for data transmission to ensure data security, which enables data transmission in real time.

[0061] Step 4:

[0062] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise from the data, preparing it for analysis.

[0063] Step 5:

[0064] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns. The generation AI extracts information from driving behavior, such as the frequency of sudden braking and speeding, and identifies risk factors.

[0065] Step 6:

[0066] Based on the analysis results, the server generates driving improvement suggestions for the driver. For example, if the driver frequently brakes suddenly, the server generates specific improvement advice such as "increase the distance between your vehicle and the vehicle ahead."

[0067] Step 7:

[0068] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements they need to make while driving.

[0069] Step 8:

[0070] Once the drive is complete, the device displays the collected data and analysis results in graphs and charts, allowing the user to review their driving history and check their driving behavior in detail.

[0071] Step 9:

[0072] Based on the data and improvement suggestions displayed on the device, users can reflect on their driving style and identify areas for improvement for their next drive. For example, based on past driving history, the system can check information such as "10 sudden braking incidents in one week" and prompt users to be more careful the next time they drive.

[0073] Through the above steps, the system of the present invention is able to record and analyze the driver's driving behavior in detail and provide appropriate driving improvement suggestions in real time.

[0074] Example 1

[0075] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0076] In conventional driving behavior recording systems, the process of data collection, analysis, and feedback was not performed in real time, making it difficult to take an immediate approach to improving safety while driving. Furthermore, analyzing driving data required specialized knowledge, making it difficult to quickly provide specific improvement suggestions to individual drivers. Therefore, there is a need for a system that can record and analyze driving behavior in real time and provide successive improvement suggestions based on the results.

[0077] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0078] In this invention, the server includes: means for recording driving behavior in real time using a terminal installed in the transportation vehicle in which the user rides; means for temporarily saving the recorded driving behavior data; means for periodically transmitting the saved driving behavior data to the server; means for analyzing the driving behavior data received by the server using a generative artificial intelligence model; means for generating specific driving improvement suggestions based on the analysis results; means for sending the generated driving improvement suggestions to the terminal and notifying the user in the form of a push notification; and means for visually displaying the collected driving data and the analysis results to the user as graphs or charts after the end of the drive to encourage reflection. This makes it possible to immediately analyze driving behavior and provide appropriate feedback to the driver, thereby improving driving safety.

[0079] The "terminal" is a device that is installed in the transportation vehicle that the user rides in and that records the driving behavior in real time.

[0080] "Driving behavior data" refers to data collected by a terminal that indicates driving behavior such as the vehicle's speed, acceleration, direction, and braking operation.

[0081] "Temporary storage" refers to the process of storing the driving behavior data collected by the terminal for a certain period of time.

[0082] A "server" is a remote computer system that receives driving behavior data sent from the terminal, analyzes it, and generates driving improvement suggestions.

[0083] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze driving behavior data on a server and generate driving improvement suggestions for the driver.

[0084] "Analysis results" are information derived from driving behavior data processed by a generative artificial intelligence model.

[0085] "Driving improvement suggestions" are specific advice generated based on the analysis results to improve the driver's driving behavior.

[0086] A "push notification" is a notification message sent to a user in real time via a terminal.

[0087] "Visual display" refers to providing the collected data and analysis results to the user in a visual format such as graphs and charts after the drive is completed.

[0088] "Review" refers to the act of the user reviewing their own driving behavior after driving and identifying areas for improvement for the next drive.

[0089] This invention is a system for promoting safe driving by recording and analyzing a driver's driving behavior in real time. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, and a means for providing feedback to the user at an appropriate time.

[0090] System Configuration

[0091] 1. Terminal

[0092] The device used is a smartphone. Smartphones are equipped with built-in sensors such as GPS, accelerometers, and gyroscopes, which are used to record driving behavior in real time. The user places the device in a vehicle holder, and the device begins recording data. Specifically, when the user launches a dedicated smartphone app, the app activates the built-in sensors and collects driving data such as speed, acceleration, direction, and braking operations in real time.

[0093] 2. Server

[0094] The collected driving behavior data is sent to a server at regular intervals. The server receives this data and analyzes it using a generative AI model. The generative AI model recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. For example, the model generates appropriate feedback using a prompt such as, "If the user generates a lot of data on sudden braking while driving, what kind of improvement suggestions would be appropriate?"

[0095] 3. Means of notification

[0096] The server sends the generated improvement suggestions to the device, which then notifies the user in the form of a push notification. The system is designed to not interfere with the driving operation in progress so that notifications are sent at the appropriate time. Even after the drive is over, the device visually displays the collected data and analysis results, making it easy for the user to reflect on their own driving behavior.

[0097] Specific examples

[0098] When User A places their smartphone in the car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If sudden braking is detected while driving, the data is sent to the server. The server analyzes the collected data using a generative AI model and determines that User A has a driving tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends them to the device as a push notification.

[0099] After finishing driving, the device displays the driving history for the past week in chart form, allowing User A to reflect on his / her driving behavior and identify areas for improvement for the next drive. In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and focus on safe driving based on specific suggestions for improvement.

[0100] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0101] Step 1: Start recording your driving behavior

[0102] When the user places the smartphone in the car holder and launches the dedicated app, the device activates its built-in sensors, including GPS, accelerometer, and gyroscope. Inputs include location information (GPS), vehicle speed, acceleration, direction, and braking operations, which are acquired in real time and temporarily stored. Specifically, the device collects data from the sensors every second and records it in memory.

[0103] Step 2: Temporarily save data

[0104] The device temporarily stores driving behavior data collected by sensors in its internal memory. It receives captured speed, acceleration, direction, and braking data as input and stores them in chronological order in storage. The output is the temporarily stored driving behavior data. Specifically, the device adds new data to the existing dataset and stores it.

[0105] Step 3: Send data to the server

[0106] The terminal assembles the temporarily stored data into packets at regular intervals (for example, every 5 minutes) and sends them to a server over the Internet. As input, it receives the temporarily stored driving behavior data, packets it, and sends it. The output is the data packets sent to the server. Specifically, it converts the data into an appropriate format and sends it to the server over an Internet connection.

[0107] Step 4: Receiving and analyzing data

[0108] The server converts the received driving behavior data into a format suitable for analysis and inputs it into the generative AI model. As input, it receives the transmitted data packets and formats them for analysis. The output is driving behavior data in a format suitable for analysis. Specifically, it preprocesses the data to remove unnecessary information and inputs it into the generative AI model.

[0109] Step 5: Analyze driving patterns

[0110] The server uses a generative AI model to recognize driving patterns and identify risk factors. Preprocessed driving behavior data is received as input and fed into the generative AI model. The output is the analysis results. Specifically, the AI ​​model identifies patterns such as the number of sudden braking attempts and the frequency of speeding, and identifies risk factors.

[0111] Step 6: Generate improvement suggestions

[0112] The server generates specific driving improvement suggestions based on the analysis results. It receives the analysis results of the generative AI model as input and generates improvement suggestions. The output is driving improvement suggestions for the user. As a specific action, it generates advice such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking."

[0113] Step 7: Notification of improvement proposals

[0114] The server sends the generated improvement suggestions to the device, and the device notifies the user of the suggestions in the form of a push notification. The server receives the generated driving improvement suggestions as input and sends a push notification to the user as output. The specific operation is to display the notification at an appropriate time to inform the user.

[0115] Step 8: Review after driving

[0116] Once the drive is over, the device visually displays all collected data and the analysis results from the server. As input, it receives the data collected during the drive and the analysis results, and as output, converts this into graphs and charts and displays them. Specifically, it visually displays information such as "10 hard brakes in one week," allowing the user to reflect on their own driving style.

[0117] Through these processing steps, the system gains a detailed understanding of the driver's driving behavior and provides specific suggestions for improvement, thereby promoting safe driving.

[0118] (Application example 1)

[0119] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0120] Improving the operational efficiency and safety of work robots is an important issue in modern factories. Conventional technology makes it difficult to record and analyze the detailed operation history of individual robots, resulting in a lack of real-time improvement suggestions, limiting accident prevention and efficiency improvement. Furthermore, systems that record drivers' driving behavior in detail to promote safe driving only provide feedback after the drive is completed, resulting in a lack of real-time improvement suggestions. To address these issues, the present invention provides a system that records and analyzes both driving and work behavior in real time and provides immediate feedback.

[0121] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0122] In this invention, the server includes means for periodically transmitting collected driving behavior data to the server, means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results, means for identifying driving risk factors and generating specific improvement proposals, means for periodically transmitting collected work behavior data to the server, means for the server to analyze the work behavior data and create work improvement proposals based on the analysis results, and means for identifying work risk factors and generating specific improvement proposals. This makes it possible to record and analyze the behavior of drivers and factory robots in detail and provide improvement proposals in real time.

[0123] "Driving behavior" refers to the actions and movements of a driver when driving a vehicle, including acceleration, speed, direction, braking, etc.

[0124] "Generative AI" is artificial intelligence that analyzes large amounts of data, performs pattern recognition and predictions, and generates appropriate suggestions and results.

[0125] "Analysis results" refers to information such as conclusions and recommendations obtained by the generative AI after analyzing driving behavior data and work behavior data.

[0126] "Improvement proposals" are specific ideas and advice for improving efficiency and safety derived from the analysis results.

[0127] "Work behavior" refers to the movements and motion patterns of a factory robot when performing work, including its speed, acceleration, position, etc.

[0128] "Notification means" refers to the methods or technologies used to notify users of analysis results and improvement suggestions, and push notifications are an example of this.

[0129] "Real-time" refers to the temporal characteristics that allow processes such as data collection, analysis, and notification to be carried out immediately without delay.

[0130] The "server" refers to the infrastructure or system that receives and analyzes driving behavior data and work behavior data, and is equipped with a generation AI.

[0131] An "encrypted communication protocol" refers to the standards and procedures for encrypting and communicating data in order to ensure secure data transmission.

[0132] "Reflection" refers to the process of reviewing past actions and work based on collected data and analysis results after a drive or task has been completed, and identifying areas for improvement.

[0133] "Visual display" refers to a method of presenting data or analytical results to a user in a visual form such as a graph, chart, or diagram.

[0134] A system for realizing this invention is mainly composed of a terminal (a smartphone or a factory robot control unit), a server, and a notification means.

[0135] Terminal

[0136] The terminals used are smartphones and factory robot control units. These terminals are equipped with multiple sensors, including GPS, accelerometers, and gyroscopes, and record driving and work behavior in real time. The terminal is installed in an in-vehicle holder or on the robot, and data collection begins.

[0137] server

[0138] The server receives the data sent from the device and analyzes it using generative AI. Specifically, it uses AI libraries such as TensorFlow and PyTorch. The server recognizes driving and work patterns, identifies risk factors, and generates improvement suggestions. The analyzed data is important for improving the efficiency and safety of driving or work.

[0139] Notification means

[0140] The generated improvement suggestions are notified to the user in real time using push notification technology. For example, specific advice such as "increase the distance between vehicles" is sent to a driver who frequently brakes suddenly. In the case of factory robots, improvement suggestions such as "inspect tools" and "recalculate loads" are notified.

[0141] Review and improvement

[0142] After a drive or task is completed, the device provides the collected data and analysis results to the user in the form of charts and graphs, allowing the user to reflect on their own behavior and understand what improvements to make next time.

[0143] Specific examples

[0144] For example, when a user places their smartphone in a car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to a server. The server analyzes the data using generative AI and determines that the driver has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking," and sends these via push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing the user to reflect on their driving and identify areas for improvement for their next drive.

[0145] Prompt Sentence Examples

[0146] An example of a prompt sentence to input to the generative AI model is as follows:

[0147] Generate operational improvement proposals for increasing efficiency and ensuring safety based on the operational data of factory robots. Analyze the following data:

[0148] Acceleration: {Acceleration data}

[0149] Location: {location data}

[0150] Speed: {speed data}

[0151] Abnormal behavior frequency: {Abnormal behavior data}

[0152] (example):

[0153] If the robot detects sudden acceleration while working, please suggest the cause and remedial measures.

[0154] By using such a system, it is possible to gain a detailed understanding of the behavior of drivers and factory robots and provide specific improvement suggestions.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] The device records driving or work behavior

[0158] The terminal (a smartphone or a factory robot control unit) collects driving or work behavior data in real time using sensors such as GPS, accelerometers, and gyroscopes. The input is raw data from the sensors, which includes speed, acceleration, location information, etc. The output is a set of recorded raw data.

[0159] Step 2:

[0160] The device periodically sends data to the server.

[0161] The device sends the collected data to the server at regular intervals. The input is the set of recorded raw data, and the output is the data sent to the server. This process uses an encrypted communication protocol to ensure the secure transmission of data.

[0162] Step 3:

[0163] The server analyzes the data

[0164] The server uses the received data to perform analysis using a generative AI model (e.g., TensorFlow or PyTorch). The input is the raw data sent from the device, and the machine learning model analyzes it for behavioral and abnormal patterns. The output is the analysis results, which include specific risk factors and patterns in driving or work.

[0165] Step 4:

[0166] The server generates and notifies improvement suggestions

[0167] Based on the analysis results, the server uses a generative AI to generate specific improvement suggestions. For example, if there are frequent sudden braking attempts, the server generates suggestions such as "increase the distance between vehicles," or "inspect tools" if abnormal robot behavior is detected. The input is the analysis results, and the output is the improvement suggestions. These are sent to the device via push notification, and the user is notified in real time.

[0168] Step 5:

[0169] The device visually displays data after driving or work is completed

[0170] After driving or working, the device provides the user with charts and graphs based on the collected data and the server's analysis results. The input is the analysis results from the server and the collected raw data, and the output is a visual display. This allows the user to reflect on their own driving or working behavior and understand what needs to be improved next time.

[0171] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0172] This invention is a system for promoting safe driving by recording and analyzing the driver's driving behavior in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[0173] System Configuration

[0174] 1. Device (smartphone)

[0175] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. It also has an emotion engine that uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0176] 2. Server

[0177] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. In addition, emotional data obtained by an emotion engine is also used in the analysis.

[0178] 3. Means of notification

[0179] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0180] Overview of program processing

[0181] Recording driving behavior and emotions

[0182] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, recording emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[0183] Data transmission and analysis

[0184] Data collected while driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden brakings and speeding, as well as the user's stress and fatigue levels based on emotional data.

[0185] Generate and notify operational improvement suggestions

[0186] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[0187] Review and improvement

[0188] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[0189] Specific examples

[0190] For example, suppose User A places his or her smartphone in a car mount and begins driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, it uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, that data and User A's emotional data are sent to the server. The server uses generative AI to analyze the collected data and determine that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[0191] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and emotional state, and can make efforts to drive safely based on specific suggestions for improvement.

[0192] The processing flow will be explained below.

[0193] Step 1:

[0194] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[0195] Step 2:

[0196] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[0197] Step 3:

[0198] The device uses a built-in camera and microphone to analyze the user's facial expressions and tone of voice with an emotion engine, and records the user's emotional data in real time. For example, if the user is surprised, the device will detect changes in their facial expressions and voice and record them as emotional data.

[0199] Step 4:

[0200] The device processes driving behavior data and emotion data in batches at regular intervals (for example, every minute) and sends them to the server. The SSL / TLS protocol is used for data transmission to ensure data security, making it possible to transmit data in real time.

[0201] Step 5:

[0202] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise, to prepare the data for analysis.

[0203] Step 6:

[0204] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns and emotional state. The generation AI extracts information such as the frequency of sudden braking and speeding from the driving behavior data, and also identifies the user's stress and fatigue levels based on the emotional data.

[0205] Step 7:

[0206] Based on the analysis results, the server generates specific driving improvement suggestions for the driver. For example, for a driver who frequently brakes suddenly, the server generates specific advice such as "increase the distance between your vehicle and the vehicle in front." In addition, if the user is feeling stressed, the server generates suggestions such as "take appropriate breaks."

[0207] Step 8:

[0208] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements and stress reduction measures they need to take while driving.

[0209] Step 9:

[0210] After the drive is completed, the device presents the collected data and analysis results to the user in the form of graphs and charts, allowing the user to review their past driving history and emotional state and specifically check their own driving behavior and emotional state.

[0211] Step 10:

[0212] Based on the data and improvement suggestions displayed on the device, users can reflect on their own driving style and emotional state and understand what needs to be improved the next time they drive. For example, based on past driving history, the device can confirm information such as "You braked suddenly 10 times in one week, and felt high stress at those times," and provide a warning for the next time you drive.

[0213] Through the above steps, the system of the present invention can record and analyze the driver's driving behavior and emotional state in detail, and provide appropriate driving improvement suggestions and stress reduction measures in real time.

[0214] Example 2

[0215] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0216] Conventional driving management systems have difficulty recording detailed driver behavior in real time and providing specific improvement suggestions to promote safe driving. Furthermore, systems that analyze and provide feedback on users' emotional states are limited, leaving a lack of effective means to support the development of safe driving habits. There was also a need for a method to safely transmit collected driving and emotional data and notify users of specific improvement suggestions in real time based on the analysis results.

[0217] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0218] In this invention, the server includes a terminal means for recording the driver's driving behavior in real time, a means for recording speed, acceleration, direction, and braking operation using the terminal's sensors, a means for recognizing the user's emotions using the terminal's camera and microphone, a means for periodically transmitting the recorded driving behavior and emotional data to the server, a means for the server to analyze the received data using artificial intelligence to identify driving patterns and risk factors, a means for generating and notifying the user of appropriate driving improvement suggestions based on the analysis results, and a means for visually displaying the analysis results to the user after driving has ended to encourage reflection. This makes it possible to record the driver's driving behavior and emotional state in detail and provide specific improvement suggestions based on the results.

[0219] "Driver's driving behavior" refers to a series of actions performed by a driver while driving, such as the vehicle's speed, acceleration, direction, and braking operation.

[0220] "Terminal" refers to a device for recording and transmitting driving behavior and emotional data in real time, and generally refers to a smartphone or tablet device.

[0221] "Sensors" are measurement devices, such as GPS, accelerometers, and gyroscopes, that measure and record the vehicle's driving behavior in real time.

[0222] The "camera" is an image capturing device for capturing images of the user's facial expressions and recognizing their emotions.

[0223] A "microphone" is a voice capture device that picks up the user's voice and analyzes their emotional state.

[0224] "Emotion data" is information about the user's emotional state, such as stress or fatigue, analyzed from their facial expressions and tone of voice.

[0225] The "server" is a computer system that receives data sent from the terminal and generates analysis and driving improvement suggestions.

[0226] "Generative AI" is an AI technology that analyzes large amounts of data to identify driving patterns and risk factors.

[0227] A "driving pattern" is a consistent driving tendency or habit identified from a driver's driving behavior.

[0228] A "risk factor" is a behavior or situation that could potentially pose a risk while driving.

[0229] "Driving improvement suggestions" are specific advice provided to drivers based on the analysis results to promote safe driving.

[0230] "Push notifications" is a technology that sends new information and notifications to users' devices in real time.

[0231] An "encrypted communication protocol" is a communication method that encrypts information to ensure secure data transmission and reception.

[0232] "Driving history" refers to the accumulation of driving behavior data and analysis results recorded in the past.

[0233] "Charts and graphs" are diagrams that visually display information and are a means of presenting driving and emotional data in an easily understandable format.

[0234] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[0235] System Configuration

[0236] 1. Device (smartphone)

[0237] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The smartphone also uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0238] 2. Server

[0239] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generative AI model. Specifically, it recognizes driving patterns and identifies risk factors, and generates specific improvement proposals based on the analysis results. Emotion data obtained by the emotion engine is also used in the analysis.

[0240] 3. Means of notification

[0241] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0242] Specific examples of program processing

[0243] 1. Recording driving behavior and emotions

[0244] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, and records emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[0245] 2. Data submission and analysis

[0246] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses a generative AI model to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding, as well as the user's stress and fatigue levels based on emotional data.

[0247] 3. Generation and notification of driving improvement suggestions

[0248] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[0249] 4. Reflection and Improvement

[0250] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[0251] Prompt Sentence Examples

[0252] "Analyze the driver's driving patterns and generate specific improvement suggestions based on the number of sudden braking and speeding. Also, use emotional data to determine if the user is stressed and suggest appropriate breaks."

[0253] In this way, the system of the present invention allows drivers to understand their own driving behavior and emotional state in detail, and enables them to focus on safe driving based on specific suggestions for improvement.

[0254] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0255] System program processing flow

[0256] Step 1:

[0257] The device activates the smartphone's sensors and records driving behavior.

[0258] Input: GPS, accelerometer, gyroscope data

[0259] Data processing: Real-time acquisition of speed, acceleration, direction, and braking data

[0260] Output: Driving behavior data

[0261] How it works: When a user starts the car engine and places the smartphone in the car mount, the device automatically activates the GPS, accelerometer, gyroscope, etc. This starts recording the car's speed, acceleration, and direction in real time, as well as data on when braking occurs.

[0262] Step 2:

[0263] The device activates the camera and microphone to record the user's emotions.

[0264] Input: Camera video, microphone audio

[0265] Data processing: Analyzing emotional states from facial expressions and tone of voice

[0266] Output: Emotion data

[0267] Specific operation: The device takes a picture of the user's face using the smartphone camera and collects their voice using the microphone. The emotion engine analyzes this data and records the user's emotional state (e.g., stress, fatigue) in real time.

[0268] Step 3:

[0269] The terminal transmits the recorded driving behavior data and emotion data to a server.

[0270] Input: driving behavior data, emotion data

[0271] Data processing: compressing data and preparing it for transmission

[0272] Output: Data sent to the server

[0273] Specific operation: At regular intervals, the terminal compresses the data, checks for errors, and then sends it to the server via the Internet.

[0274] Step 4:

[0275] The server analyzes the received data.

[0276] Input: driving behavior data, emotion data

[0277] Data Computation: Using generative AI models to identify driving patterns and risk factors

[0278] Output: Analysis results (driving patterns, risk factors)

[0279] How it works: The server adds the received data to an analysis queue and analyzes it using a generative AI model, which identifies the number of sudden braking attempts and speeding trends, and analyzes the user's emotional state to determine stress and fatigue levels.

[0280] Step 5:

[0281] The server generates driving improvement suggestions based on the analysis results.

[0282] Input: Analysis results

[0283] Data processing: generating concrete improvement proposals

[0284] Output: Driving improvement suggestions

[0285] Specific operation: Based on the analysis results, the server generates advice such as "increase the distance between your vehicle and the vehicle in front," and if stress is high, makes suggestions such as "take appropriate breaks."

[0286] Step 6:

[0287] The server generates driving improvement suggestions and sends them to the terminal.

[0288] Input: Driving improvement suggestions

[0289] Data processing: Preparing the proposed data for sending

[0290] Output: Proposal data sent to the device

[0291] Specific operation: The server checks the proposed data for errors and sends it to the device as a push notification. The device receives it and notifies the user in real time.

[0292] Step 7:

[0293] The device visually displays the collected data after the drive is completed.

[0294] Input: driving behavior data, emotion data, analysis results

[0295] Data processing: Convert data into graphs and charts

[0296] Output: Visual display

[0297] Specific operation: After the user finishes driving, the device analyzes the collected data and presents it to the user in the form of visual graphs and charts. For example, it may display information such as "10 sudden braking attempts" to encourage caution the next time the user drives. It also displays emotional data to help users understand their stress level and fatigue state.

[0298] Through each step, the system records and analyzes the driver's driving behavior and emotional state in detail, and provides specific improvement suggestions to promote safe driving.

[0299] (Application example 2)

[0300] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] In recent years, systems have been developed to record and analyze driving behavior in real time in order to promote safe driving, but these systems focus only on the driver's driving behavior and do not take into account the emotional state or fatigue level of the driver or passengers. This poses the challenge of making it difficult to develop individual, specific measures to reduce the impact of emotional state and fatigue level on driving.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0303] In this invention, the server includes a means for recognizing and recording emotional data of the user while driving, a means for complementing the analysis results based on the emotional data and generating more accurate driving improvement suggestions, and a means for generating suggestions for driving habits such as taking breaks based on stress and fatigue levels, thereby enabling safe driving support that takes into account the emotional state and fatigue level.

[0304] "Driving behavior" refers to driver actions such as vehicle speed, acceleration, braking, and steering.

[0305] "Real-time" means that all operations and situations during driving are immediately recorded and analyzed and feedback is provided almost simultaneously.

[0306] "Recording means" refers to a device or system that uses sensors or devices to collect and store driving behavior and emotional data.

[0307] "Generative artificial intelligence" refers to machine learning models and data processing algorithms that analyze driving behavior data, recognize patterns, and generate suggestions.

[0308] "Means for analysis" refers to software or hardware that evaluates collected driving behavior data and emotion data and performs computational processing to identify risk factors and areas for improvement.

[0309] "Means of notification" refers to communication methods, such as push notifications or display functions on smartphones, used to inform users of analysis results and suggestions.

[0310] "Visual display means" refers to the display or software that presents the analysis results to the user in the form of graphs, charts, etc.

[0311] "Emotional data" refers to data that indicates a user's emotional or psychological state, derived from facial expressions or tone of voice.

[0312] "Complementary means" refers to methods and systems that use emotion data to further refine the analysis results of driving behavior data and improve the accuracy of suggestions.

[0313] "Driving improvement suggestions" refer to specific advice and instructions to drivers to help them review their driving behavior and encourage safe driving.

[0314] "Rest Suggestion" refers to instructions that encourage drivers and passengers to take appropriate rest breaks if they feel stressed or fatigued while driving.

[0315] This invention is a system for recording and analyzing the driving behavior and emotional state of drivers and passengers to promote safe driving. The system consists of a smartphone, sensors, a camera, a microphone, a server, and a generative AI model.

[0316] Hardware and Software Configuration

[0317] 1. Smartphone

[0318] Smartphones are equipped with built-in sensors such as GPS, accelerometer, gyroscope, camera, and microphone.

[0319] The smartphone is placed in a car holder and data collection begins as soon as the driver starts driving.

[0320] 2. Emotion Engine

[0321] Using data from the built-in camera and microphone, the device analyzes the user's facial expressions and tone of voice to record emotional data.

[0322] The emotion engine uses machine learning algorithms to recognize emotional states in real time.

[0323] 3. Server

[0324] Receives driving behavior data and emotion data periodically sent from a smartphone.

[0325] A generative AI model is used to analyze incoming data, recognize driving patterns, and identify risk factors.

[0326] Based on the analysis results, specific driving improvement suggestions are generated and sent to a smartphone.

[0327] 4. Means of notification

[0328] The smartphone receives the improvement suggestions sent from the server and notifies the user via push notification.

[0329] Even after driving is complete, the collected data and analysis results are visually displayed, making it easier for users to reflect on their own driving behavior.

[0330] A natural language description of the program's processing

[0331] The server first receives driving data sent from the smartphone. This data includes driving behavior data such as speed, acceleration, direction, and braking, as well as emotion data including facial expressions and tone of voice collected by the emotion engine. The server then analyzes this data using a generative AI model.

[0332] The analysis identifies the number of sudden braking and speeding, and also evaluates the user's stress and fatigue state based on emotional data. This allows for more accurate driving improvement suggestions to be made. For example, if sudden braking occurs frequently, the system will generate a suggestion such as "increase the distance between your vehicle and the vehicle ahead," and if stress is high, it will suggest driving habits such as "take appropriate breaks."

[0333] Finally, the generated improvement suggestions are sent to a smartphone in real time via push notification. After the drive is completed, the smartphone displays the collected data and analysis results in charts and graphs, allowing the user to reflect on their driving behavior.

[0334] Examples of specific examples and prompts

[0335] Examples:

[0336] Suppose User A places his or her smartphone in a car mount and begins driving. The smartphone immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, the smartphone uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, the data and User A's emotional data are sent to the server. The server analyzes the collected data using generative AI and determines that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[0337] Example prompt sentence:

[0338] "Driving and emotional data was collected as follows:

[0339] Hard braking: 5 times

[0340] Overspeeding: 2 times

[0341] Stress level: High

[0342] Based on this data, generate specific suggestions to encourage drivers to drive safely.”

[0343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0344] Step 1:

[0345] Data collection

[0346] The device uses a smartphone placed in a car holder to collect driving behavior data from sensors such as GPS, accelerometer, and gyroscope, and also uses the built-in camera and microphone to capture the user's facial expressions and tone of voice, thereby collecting emotional data.

[0347] Input: Vehicle speed, acceleration, braking, steering, user facial expressions and tone of voice.

[0348] Output: Driving behavior data and emotion data.

[0349] Step 2:

[0350] Data transmission

[0351] The device periodically transmits the collected driving behavior data and emotion data to the server using an encrypted communication protocol.

[0352] Input: Driving behavior data and emotion data.

[0353] Output: Data securely sent to the server.

[0354] Step 3:

[0355] Data analysis

[0356] The server analyzes the received driving behavior data and emotional data. Using a generative AI model, it recognizes the driver's driving patterns, such as the number of sudden braking attempts and the frequency of speeding. It also evaluates the driver's stress and fatigue levels based on the emotional data.

[0357] Input: Submitted driving behavior data and emotion data.

[0358] Output: Driving pattern analysis, stress and fatigue assessment results.

[0359] Step 4:

[0360] Generate driving improvement suggestions

[0361] The server generates driving improvement suggestions for the user based on the analysis results. For example, if the driver brakes suddenly frequently, the server may suggest "increase the distance between your vehicle and the vehicle ahead," or if the driver is judged to be under high stress, the server may suggest "take appropriate breaks."

[0362] Input: Driving pattern analysis results, stress and fatigue assessment results.

[0363] Output: Specific driving improvement suggestions.

[0364] Step 5:

[0365] Feedback Notifications

[0366] The server sends the generated driving improvement suggestions to the terminal, and the terminal notifies the user of them in real time via push notification.

[0367] Input: Specific driving improvement suggestions.

[0368] Output: A push notification that arrives to the user.

[0369] Step 6:

[0370] Visual display of results

[0371] After the drive, the server visually displays the collected driving data and emotional data on the device as charts and graphs, allowing the user to reflect on their own driving behavior and emotional state.

[0372] Input: Driving data and emotion data until the end of the drive.

[0373] Output: Charts and graphs that are visually displayed to the user.

[0374] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0375] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0376] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0377] [Second embodiment]

[0378] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0379] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0380] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0381] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0382] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0383] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0384] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0385] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0386] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0387] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0388] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0389] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0390] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, and a means for providing feedback to users at appropriate times.

[0391] System Configuration

[0392] 1. Device (smartphone)

[0393] The device used is a smartphone. The smartphone is equipped with sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0394] 2. Server

[0395] Data recorded during driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results.

[0396] 3. Means of notification

[0397] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0398] Overview of program processing

[0399] Driving behavior recording

[0400] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, etc. in real time. For example, if the user suddenly accelerates the vehicle, the accelerometer immediately records this data and stores it on the device.

[0401] Data transmission and analysis

[0402] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding.

[0403] Generate and notify improvement suggestions

[0404] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, specific advice such as "increase the distance between your vehicle and the vehicle in front" is generated and sent to the device as a push notification. The device receives this and displays it to the user.

[0405] Review and improvement

[0406] Once the drive is over, the device displays the collected data and the server's analysis results in graphs and charts to the user. This allows the user to reflect on their driving style and understand what improvements they need to make next time. For example, based on past driving history, the device may display information such as "10 sudden braking attempts in one week," encouraging the user to be more careful the next time they drive.

[0407] Specific examples

[0408] For example, suppose User A places their smartphone in a car holder and starts driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to the server. The server uses generative AI to analyze the collected data and determines that User A has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends these to the device as a push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing User A to reflect on their driving behavior and identify areas for improvement for their next drive.

[0409] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and make an effort to drive safely based on specific suggestions for improvement.

[0410] The processing flow will be explained below.

[0411] Step 1:

[0412] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[0413] Step 2:

[0414] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[0415] Step 3:

[0416] The terminal processes the collected data in batches at regular intervals (for example, every minute) and sends it to the server. The SSL / TLS protocol is used for data transmission to ensure data security, which enables data transmission in real time.

[0417] Step 4:

[0418] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise from the data, preparing it for analysis.

[0419] Step 5:

[0420] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns. The generation AI extracts information from driving behavior, such as the frequency of sudden braking and speeding, and identifies risk factors.

[0421] Step 6:

[0422] Based on the analysis results, the server generates driving improvement suggestions for the driver. For example, if the driver frequently brakes suddenly, the server generates specific improvement advice such as "increase the distance between your vehicle and the vehicle ahead."

[0423] Step 7:

[0424] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements they need to make while driving.

[0425] Step 8:

[0426] Once the drive is complete, the device displays the collected data and analysis results in graphs and charts, allowing the user to review their driving history and check their driving behavior in detail.

[0427] Step 9:

[0428] Based on the data and improvement suggestions displayed on the device, users can reflect on their driving style and identify areas for improvement for their next drive. For example, based on past driving history, the system can check information such as "10 sudden braking incidents in one week" and prompt users to be more careful the next time they drive.

[0429] Through the above steps, the system of the present invention is able to record and analyze the driver's driving behavior in detail and provide appropriate driving improvement suggestions in real time.

[0430] Example 1

[0431] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0432] In conventional driving behavior recording systems, the process of data collection, analysis, and feedback was not performed in real time, making it difficult to take an immediate approach to improving safety while driving. Furthermore, analyzing driving data required specialized knowledge, making it difficult to quickly provide specific improvement suggestions to individual drivers. Therefore, there is a need for a system that can record and analyze driving behavior in real time and provide successive improvement suggestions based on the results.

[0433] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0434] In this invention, the server includes: means for recording driving behavior in real time using a terminal installed in the transportation vehicle in which the user rides; means for temporarily saving the recorded driving behavior data; means for periodically transmitting the saved driving behavior data to the server; means for analyzing the driving behavior data received by the server using a generative artificial intelligence model; means for generating specific driving improvement suggestions based on the analysis results; means for sending the generated driving improvement suggestions to the terminal and notifying the user in the form of a push notification; and means for visually displaying the collected driving data and the analysis results to the user as graphs or charts after the end of the drive to encourage reflection. This makes it possible to immediately analyze driving behavior and provide appropriate feedback to the driver, thereby improving driving safety.

[0435] The "terminal" is a device that is installed in the transportation vehicle that the user rides in and that records the driving behavior in real time.

[0436] "Driving behavior data" refers to data collected by a terminal that indicates driving behavior such as the vehicle's speed, acceleration, direction, and braking operation.

[0437] "Temporary storage" refers to the process of storing the driving behavior data collected by the terminal for a certain period of time.

[0438] A "server" is a remote computer system that receives driving behavior data sent from the terminal, analyzes it, and generates driving improvement suggestions.

[0439] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze driving behavior data on a server and generate driving improvement suggestions for the driver.

[0440] "Analysis results" are information derived from driving behavior data processed by a generative artificial intelligence model.

[0441] "Driving improvement suggestions" are specific advice generated based on the analysis results to improve the driver's driving behavior.

[0442] A "push notification" is a notification message sent to a user in real time via a terminal.

[0443] "Visual display" refers to providing the collected data and analysis results to the user in a visual format such as graphs and charts after the drive is completed.

[0444] "Review" refers to the act of the user reviewing their own driving behavior after driving and identifying areas for improvement for the next drive.

[0445] This invention is a system for promoting safe driving by recording and analyzing a driver's driving behavior in real time. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, and a means for providing feedback to the user at an appropriate time.

[0446] System Configuration

[0447] 1. Terminal

[0448] The device used is a smartphone. Smartphones are equipped with built-in sensors such as GPS, accelerometers, and gyroscopes, which are used to record driving behavior in real time. The user places the device in a vehicle holder, and the device begins recording data. Specifically, when the user launches a dedicated smartphone app, the app activates the built-in sensors and collects driving data such as speed, acceleration, direction, and braking operations in real time.

[0449] 2. Server

[0450] The collected driving behavior data is sent to a server at regular intervals. The server receives this data and analyzes it using a generative AI model. The generative AI model recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. For example, the model generates appropriate feedback using a prompt such as, "If the user generates a lot of data on sudden braking while driving, what kind of improvement suggestions would be appropriate?"

[0451] 3. Means of notification

[0452] The server sends the generated improvement suggestions to the device, which then notifies the user in the form of a push notification. The system is designed to not interfere with the driving operation in progress so that notifications are sent at the appropriate time. Even after the drive is over, the device visually displays the collected data and analysis results, making it easy for the user to reflect on their own driving behavior.

[0453] Specific examples

[0454] When User A places their smartphone in the car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If sudden braking is detected while driving, the data is sent to the server. The server analyzes the collected data using a generative AI model and determines that User A has a driving tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends them to the device as a push notification.

[0455] After finishing driving, the device displays the driving history for the past week in chart form, allowing User A to reflect on his / her driving behavior and identify areas for improvement for the next drive. In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and focus on safe driving based on specific suggestions for improvement.

[0456] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0457] Step 1: Start recording your driving behavior

[0458] When the user places the smartphone in the car holder and launches the dedicated app, the device activates its built-in sensors, including GPS, accelerometer, and gyroscope. Inputs include location information (GPS), vehicle speed, acceleration, direction, and braking operations, which are acquired in real time and temporarily stored. Specifically, the device collects data from the sensors every second and records it in memory.

[0459] Step 2: Temporarily save data

[0460] The device temporarily stores driving behavior data collected by sensors in its internal memory. It receives captured speed, acceleration, direction, and braking data as input and stores them in chronological order in storage. The output is the temporarily stored driving behavior data. Specifically, the device adds new data to the existing dataset and stores it.

[0461] Step 3: Send data to the server

[0462] The terminal assembles the temporarily stored data into packets at regular intervals (for example, every 5 minutes) and sends them to a server over the Internet. As input, it receives the temporarily stored driving behavior data, packets it, and sends it. The output is the data packets sent to the server. Specifically, it converts the data into an appropriate format and sends it to the server over an Internet connection.

[0463] Step 4: Receiving and analyzing data

[0464] The server converts the received driving behavior data into a format suitable for analysis and inputs it into the generative AI model. As input, it receives the transmitted data packets and formats them for analysis. The output is driving behavior data in a format suitable for analysis. Specifically, it preprocesses the data to remove unnecessary information and inputs it into the generative AI model.

[0465] Step 5: Analyze driving patterns

[0466] The server uses a generative AI model to recognize driving patterns and identify risk factors. Preprocessed driving behavior data is received as input and fed into the generative AI model. The output is the analysis results. Specifically, the AI ​​model identifies patterns such as the number of sudden braking attempts and the frequency of speeding, and identifies risk factors.

[0467] Step 6: Generate improvement suggestions

[0468] The server generates specific driving improvement suggestions based on the analysis results. It receives the analysis results of the generative AI model as input and generates improvement suggestions. The output is driving improvement suggestions for the user. As a specific action, it generates advice such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking."

[0469] Step 7: Notification of improvement proposals

[0470] The server sends the generated improvement suggestions to the device, and the device notifies the user of the suggestions in the form of a push notification. The server receives the generated driving improvement suggestions as input and sends a push notification to the user as output. The specific operation is to display the notification at an appropriate time to inform the user.

[0471] Step 8: Review after driving

[0472] Once the drive is over, the device visually displays all collected data and the analysis results from the server. As input, it receives the data collected during the drive and the analysis results, and as output, converts this into graphs and charts and displays them. Specifically, it visually displays information such as "10 hard brakes in one week," allowing the user to reflect on their own driving style.

[0473] Through these processing steps, the system gains a detailed understanding of the driver's driving behavior and provides specific suggestions for improvement, thereby promoting safe driving.

[0474] (Application example 1)

[0475] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0476] Improving the operational efficiency and safety of work robots is an important issue in modern factories. Conventional technology makes it difficult to record and analyze the detailed operation history of individual robots, resulting in a lack of real-time improvement suggestions, limiting accident prevention and efficiency improvement. Furthermore, systems that record drivers' driving behavior in detail to promote safe driving only provide feedback after the drive is completed, resulting in a lack of real-time improvement suggestions. To address these issues, the present invention provides a system that records and analyzes both driving and work behavior in real time and provides immediate feedback.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0478] In this invention, the server includes means for periodically transmitting collected driving behavior data to the server, means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results, means for identifying driving risk factors and generating specific improvement proposals, means for periodically transmitting collected work behavior data to the server, means for the server to analyze the work behavior data and create work improvement proposals based on the analysis results, and means for identifying work risk factors and generating specific improvement proposals. This makes it possible to record and analyze the behavior of drivers and factory robots in detail and provide improvement proposals in real time.

[0479] "Driving behavior" refers to the actions and movements of a driver when driving a vehicle, including acceleration, speed, direction, braking, etc.

[0480] "Generative AI" is artificial intelligence that analyzes large amounts of data, performs pattern recognition and predictions, and generates appropriate suggestions and results.

[0481] "Analysis results" refers to information such as conclusions and recommendations obtained by the generative AI after analyzing driving behavior data and work behavior data.

[0482] "Improvement proposals" are specific ideas and advice for improving efficiency and safety derived from the analysis results.

[0483] "Work behavior" refers to the movements and motion patterns of a factory robot when performing work, including its speed, acceleration, position, etc.

[0484] "Notification means" refers to the methods or technologies used to notify users of analysis results and improvement suggestions, and push notifications are an example of this.

[0485] "Real-time" refers to the temporal characteristics that allow processes such as data collection, analysis, and notification to be carried out immediately without delay.

[0486] The "server" refers to the infrastructure or system that receives and analyzes driving behavior data and work behavior data, and is equipped with a generation AI.

[0487] An "encrypted communication protocol" refers to the standards and procedures for encrypting and communicating data in order to ensure secure data transmission.

[0488] "Reflection" refers to the process of reviewing past actions and work based on collected data and analysis results after a drive or task has been completed, and identifying areas for improvement.

[0489] "Visual display" refers to a method of presenting data or analytical results to a user in a visual form such as a graph, chart, or diagram.

[0490] A system for realizing this invention is mainly composed of a terminal (a smartphone or a factory robot control unit), a server, and a notification means.

[0491] Terminal

[0492] The terminals used are smartphones and factory robot control units. These terminals are equipped with multiple sensors, including GPS, accelerometers, and gyroscopes, and record driving and work behavior in real time. The terminal is installed in an in-vehicle holder or on the robot, and data collection begins.

[0493] server

[0494] The server receives the data sent from the device and analyzes it using generative AI. Specifically, it uses AI libraries such as TensorFlow and PyTorch. The server recognizes driving and work patterns, identifies risk factors, and generates improvement suggestions. The analyzed data is important for improving the efficiency and safety of driving or work.

[0495] Notification means

[0496] The generated improvement suggestions are notified to the user in real time using push notification technology. For example, specific advice such as "increase the distance between vehicles" is sent to a driver who frequently brakes suddenly. In the case of factory robots, improvement suggestions such as "inspect tools" and "recalculate loads" are notified.

[0497] Review and improvement

[0498] After a drive or task is completed, the device provides the collected data and analysis results to the user in the form of charts and graphs, allowing the user to reflect on their own behavior and understand what improvements to make next time.

[0499] Specific examples

[0500] For example, when a user places their smartphone in a car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to a server. The server analyzes the data using generative AI and determines that the driver has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking," and sends these via push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing the user to reflect on their driving and identify areas for improvement for their next drive.

[0501] Prompt Sentence Examples

[0502] An example of a prompt sentence to input to the generative AI model is as follows:

[0503] Generate operational improvement proposals for increasing efficiency and ensuring safety based on the operational data of factory robots. Analyze the following data:

[0504] Acceleration: {Acceleration data}

[0505] Location: {location data}

[0506] Speed: {speed data}

[0507] Abnormal behavior frequency: {Abnormal behavior data}

[0508] (example):

[0509] If the robot detects sudden acceleration while working, please suggest the cause and remedial measures.

[0510] By using such a system, it is possible to gain a detailed understanding of the behavior of drivers and factory robots and provide specific improvement suggestions.

[0511] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0512] Step 1:

[0513] The device records driving or work behavior

[0514] The terminal (a smartphone or a factory robot control unit) collects driving or work behavior data in real time using sensors such as GPS, accelerometers, and gyroscopes. The input is raw data from the sensors, which includes speed, acceleration, location information, etc. The output is a set of recorded raw data.

[0515] Step 2:

[0516] The device periodically sends data to the server.

[0517] The device sends the collected data to the server at regular intervals. The input is the set of recorded raw data, and the output is the data sent to the server. This process uses an encrypted communication protocol to ensure the secure transmission of data.

[0518] Step 3:

[0519] The server analyzes the data

[0520] The server uses the received data to perform analysis using a generative AI model (e.g., TensorFlow or PyTorch). The input is the raw data sent from the device, and the machine learning model analyzes it for behavioral and abnormal patterns. The output is the analysis results, which include specific risk factors and patterns in driving or work.

[0521] Step 4:

[0522] The server generates and notifies improvement suggestions

[0523] Based on the analysis results, the server uses a generative AI to generate specific improvement suggestions. For example, if there are frequent sudden braking attempts, the server generates suggestions such as "increase the distance between vehicles," or "inspect tools" if abnormal robot behavior is detected. The input is the analysis results, and the output is the improvement suggestions. These are sent to the device via push notification, and the user is notified in real time.

[0524] Step 5:

[0525] The device visually displays data after driving or work is completed

[0526] After driving or working, the device provides the user with charts and graphs based on the collected data and the server's analysis results. The input is the analysis results from the server and the collected raw data, and the output is a visual display. This allows the user to reflect on their own driving or working behavior and understand what needs to be improved next time.

[0527] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0528] This invention is a system for promoting safe driving by recording and analyzing the driver's driving behavior in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[0529] System Configuration

[0530] 1. Device (smartphone)

[0531] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. It also has an emotion engine that uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0532] 2. Server

[0533] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. In addition, emotional data obtained by an emotion engine is also used in the analysis.

[0534] 3. Means of notification

[0535] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0536] Overview of program processing

[0537] Recording driving behavior and emotions

[0538] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, recording emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[0539] Data transmission and analysis

[0540] Data collected while driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden brakings and speeding, as well as the user's stress and fatigue levels based on emotional data.

[0541] Generate and notify operational improvement suggestions

[0542] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[0543] Review and improvement

[0544] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[0545] Specific examples

[0546] For example, suppose User A places his or her smartphone in a car mount and begins driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, it uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, that data and User A's emotional data are sent to the server. The server uses generative AI to analyze the collected data and determine that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[0547] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and emotional state, and can make efforts to drive safely based on specific suggestions for improvement.

[0548] The processing flow will be explained below.

[0549] Step 1:

[0550] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[0551] Step 2:

[0552] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[0553] Step 3:

[0554] The device uses a built-in camera and microphone to analyze the user's facial expressions and tone of voice with an emotion engine, and records the user's emotional data in real time. For example, if the user is surprised, the device will detect changes in their facial expressions and voice and record them as emotional data.

[0555] Step 4:

[0556] The device processes driving behavior data and emotion data in batches at regular intervals (for example, every minute) and sends them to the server. The SSL / TLS protocol is used for data transmission to ensure data security, making it possible to transmit data in real time.

[0557] Step 5:

[0558] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise, to prepare the data for analysis.

[0559] Step 6:

[0560] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns and emotional state. The generation AI extracts information such as the frequency of sudden braking and speeding from the driving behavior data, and also identifies the user's stress and fatigue levels based on the emotional data.

[0561] Step 7:

[0562] Based on the analysis results, the server generates specific driving improvement suggestions for the driver. For example, for a driver who frequently brakes suddenly, the server generates specific advice such as "increase the distance between your vehicle and the vehicle in front." In addition, if the user is feeling stressed, the server generates suggestions such as "take appropriate breaks."

[0563] Step 8:

[0564] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements and stress reduction measures they need to take while driving.

[0565] Step 9:

[0566] After the drive is completed, the device presents the collected data and analysis results to the user in the form of graphs and charts, allowing the user to review their past driving history and emotional state and specifically check their own driving behavior and emotional state.

[0567] Step 10:

[0568] Based on the data and improvement suggestions displayed on the device, users can reflect on their own driving style and emotional state and understand what needs to be improved the next time they drive. For example, based on past driving history, the device can confirm information such as "You braked suddenly 10 times in one week, and felt high stress at those times," and provide a warning for the next time you drive.

[0569] Through the above steps, the system of the present invention can record and analyze the driver's driving behavior and emotional state in detail, and provide appropriate driving improvement suggestions and stress reduction measures in real time.

[0570] Example 2

[0571] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0572] Conventional driving management systems have difficulty recording detailed driver behavior in real time and providing specific improvement suggestions to promote safe driving. Furthermore, systems that analyze and provide feedback on users' emotional states are limited, leaving a lack of effective means to support the development of safe driving habits. There was also a need for a method to safely transmit collected driving and emotional data and notify users of specific improvement suggestions in real time based on the analysis results.

[0573] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0574] In this invention, the server includes a terminal means for recording the driver's driving behavior in real time, a means for recording speed, acceleration, direction, and braking operation using the terminal's sensors, a means for recognizing the user's emotions using the terminal's camera and microphone, a means for periodically transmitting the recorded driving behavior and emotional data to the server, a means for the server to analyze the received data using artificial intelligence to identify driving patterns and risk factors, a means for generating and notifying the user of appropriate driving improvement suggestions based on the analysis results, and a means for visually displaying the analysis results to the user after driving has ended to encourage reflection. This makes it possible to record the driver's driving behavior and emotional state in detail and provide specific improvement suggestions based on the results.

[0575] "Driver's driving behavior" refers to a series of actions performed by a driver while driving, such as the vehicle's speed, acceleration, direction, and braking operation.

[0576] "Terminal" refers to a device for recording and transmitting driving behavior and emotional data in real time, and generally refers to a smartphone or tablet device.

[0577] "Sensors" are measurement devices, such as GPS, accelerometers, and gyroscopes, that measure and record the vehicle's driving behavior in real time.

[0578] The "camera" is an image capturing device for capturing images of the user's facial expressions and recognizing their emotions.

[0579] A "microphone" is a voice capture device that picks up the user's voice and analyzes their emotional state.

[0580] "Emotion data" is information about the user's emotional state, such as stress or fatigue, analyzed from their facial expressions and tone of voice.

[0581] The "server" is a computer system that receives data sent from the terminal and generates analysis and driving improvement suggestions.

[0582] "Generative AI" is an AI technology that analyzes large amounts of data to identify driving patterns and risk factors.

[0583] A "driving pattern" is a consistent driving tendency or habit identified from a driver's driving behavior.

[0584] A "risk factor" is a behavior or situation that could potentially pose a risk while driving.

[0585] "Driving improvement suggestions" are specific advice provided to drivers based on the analysis results to promote safe driving.

[0586] "Push notifications" is a technology that sends new information and notifications to users' devices in real time.

[0587] An "encrypted communication protocol" is a communication method that encrypts information to ensure secure data transmission and reception.

[0588] "Driving history" refers to the accumulation of driving behavior data and analysis results recorded in the past.

[0589] "Charts and graphs" are diagrams that visually display information and are a means of presenting driving and emotional data in an easily understandable format.

[0590] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[0591] System Configuration

[0592] 1. Device (smartphone)

[0593] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The smartphone also uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0594] 2. Server

[0595] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generative AI model. Specifically, it recognizes driving patterns and identifies risk factors, and generates specific improvement proposals based on the analysis results. Emotion data obtained by the emotion engine is also used in the analysis.

[0596] 3. Means of notification

[0597] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0598] Specific examples of program processing

[0599] 1. Recording driving behavior and emotions

[0600] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, and records emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[0601] 2. Data submission and analysis

[0602] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses a generative AI model to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding, as well as the user's stress and fatigue levels based on emotional data.

[0603] 3. Generation and notification of driving improvement suggestions

[0604] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[0605] 4. Reflection and Improvement

[0606] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[0607] Prompt Sentence Examples

[0608] "Analyze the driver's driving patterns and generate specific improvement suggestions based on the number of sudden braking and speeding. Also, use emotional data to determine if the user is stressed and suggest appropriate breaks."

[0609] In this way, the system of the present invention allows drivers to understand their own driving behavior and emotional state in detail, and enables them to focus on safe driving based on specific suggestions for improvement.

[0610] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0611] System program processing flow

[0612] Step 1:

[0613] The device activates the smartphone's sensors and records driving behavior.

[0614] Input: GPS, accelerometer, gyroscope data

[0615] Data processing: Real-time acquisition of speed, acceleration, direction, and braking data

[0616] Output: Driving behavior data

[0617] How it works: When a user starts the car engine and places the smartphone in the car mount, the device automatically activates the GPS, accelerometer, gyroscope, etc. This starts recording the car's speed, acceleration, and direction in real time, as well as data on when braking occurs.

[0618] Step 2:

[0619] The device activates the camera and microphone to record the user's emotions.

[0620] Input: Camera video, microphone audio

[0621] Data processing: Analyzing emotional states from facial expressions and tone of voice

[0622] Output: Emotion data

[0623] Specific operation: The device takes a picture of the user's face using the smartphone camera and collects their voice using the microphone. The emotion engine analyzes this data and records the user's emotional state (e.g., stress, fatigue) in real time.

[0624] Step 3:

[0625] The terminal transmits the recorded driving behavior data and emotion data to a server.

[0626] Input: driving behavior data, emotion data

[0627] Data processing: compressing data and preparing it for transmission

[0628] Output: Data sent to the server

[0629] Specific operation: At regular intervals, the terminal compresses the data, checks for errors, and then sends it to the server via the Internet.

[0630] Step 4:

[0631] The server analyzes the received data.

[0632] Input: driving behavior data, emotion data

[0633] Data Computation: Using generative AI models to identify driving patterns and risk factors

[0634] Output: Analysis results (driving patterns, risk factors)

[0635] How it works: The server adds the received data to an analysis queue and analyzes it using a generative AI model, which identifies the number of sudden braking attempts and speeding trends, and analyzes the user's emotional state to determine stress and fatigue levels.

[0636] Step 5:

[0637] The server generates driving improvement suggestions based on the analysis results.

[0638] Input: Analysis results

[0639] Data processing: generating concrete improvement proposals

[0640] Output: Driving improvement suggestions

[0641] Specific operation: Based on the analysis results, the server generates advice such as "increase the distance between your vehicle and the vehicle in front," and if stress is high, makes suggestions such as "take appropriate breaks."

[0642] Step 6:

[0643] The server generates driving improvement suggestions and sends them to the terminal.

[0644] Input: Driving improvement suggestions

[0645] Data processing: Preparing the proposed data for sending

[0646] Output: Proposal data sent to the device

[0647] Specific operation: The server checks the proposed data for errors and sends it to the device as a push notification. The device receives it and notifies the user in real time.

[0648] Step 7:

[0649] The device visually displays the collected data after the drive is completed.

[0650] Input: driving behavior data, emotion data, analysis results

[0651] Data processing: Convert data into graphs and charts

[0652] Output: Visual display

[0653] Specific operation: After the user finishes driving, the device analyzes the collected data and presents it to the user in the form of visual graphs and charts. For example, it may display information such as "10 sudden braking attempts" to encourage caution the next time the user drives. It also displays emotional data to help users understand their stress level and fatigue state.

[0654] Through each step, the system records and analyzes the driver's driving behavior and emotional state in detail, and provides specific improvement suggestions to promote safe driving.

[0655] (Application example 2)

[0656] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0657] In recent years, systems have been developed to record and analyze driving behavior in real time in order to promote safe driving, but these systems focus only on the driver's driving behavior and do not take into account the emotional state or fatigue level of the driver or passengers. This poses the challenge of making it difficult to develop individual, specific measures to reduce the impact of emotional state and fatigue level on driving.

[0658] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0659] In this invention, the server includes a means for recognizing and recording emotional data of the user while driving, a means for complementing the analysis results based on the emotional data and generating more accurate driving improvement suggestions, and a means for generating suggestions for driving habits such as taking breaks based on stress and fatigue levels, thereby enabling safe driving support that takes into account the emotional state and fatigue level.

[0660] "Driving behavior" refers to driver actions such as vehicle speed, acceleration, braking, and steering.

[0661] "Real-time" means that all operations and situations during driving are immediately recorded and analyzed and feedback is provided almost simultaneously.

[0662] "Recording means" refers to a device or system that uses sensors or devices to collect and store driving behavior and emotional data.

[0663] "Generative artificial intelligence" refers to machine learning models and data processing algorithms that analyze driving behavior data, recognize patterns, and generate suggestions.

[0664] "Means for analysis" refers to software or hardware that evaluates collected driving behavior data and emotion data and performs computational processing to identify risk factors and areas for improvement.

[0665] "Means of notification" refers to communication methods, such as push notifications or display functions on smartphones, used to inform users of analysis results and suggestions.

[0666] "Visual display means" refers to the display or software that presents the analysis results to the user in the form of graphs, charts, etc.

[0667] "Emotional data" refers to data that indicates a user's emotional or psychological state, derived from facial expressions or tone of voice.

[0668] "Complementary means" refers to methods and systems that use emotion data to further refine the analysis results of driving behavior data and improve the accuracy of suggestions.

[0669] "Driving improvement suggestions" refer to specific advice and instructions to drivers to help them review their driving behavior and encourage safe driving.

[0670] "Rest Suggestion" refers to instructions that encourage drivers and passengers to take appropriate rest breaks if they feel stressed or fatigued while driving.

[0671] This invention is a system for recording and analyzing the driving behavior and emotional state of drivers and passengers to promote safe driving. The system consists of a smartphone, sensors, a camera, a microphone, a server, and a generative AI model.

[0672] Hardware and Software Configuration

[0673] 1. Smartphone

[0674] Smartphones are equipped with built-in sensors such as GPS, accelerometer, gyroscope, camera, and microphone.

[0675] The smartphone is placed in a car holder and data collection begins as soon as the driver starts driving.

[0676] 2. Emotion Engine

[0677] Using data from the built-in camera and microphone, the device analyzes the user's facial expressions and tone of voice to record emotional data.

[0678] The emotion engine uses machine learning algorithms to recognize emotional states in real time.

[0679] 3. Server

[0680] Receives driving behavior data and emotion data periodically sent from a smartphone.

[0681] A generative AI model is used to analyze incoming data, recognize driving patterns, and identify risk factors.

[0682] Based on the analysis results, specific driving improvement suggestions are generated and sent to a smartphone.

[0683] 4. Means of notification

[0684] The smartphone receives the improvement suggestions sent from the server and notifies the user via push notification.

[0685] Even after driving is complete, the collected data and analysis results are visually displayed, making it easier for users to reflect on their own driving behavior.

[0686] A natural language description of the program's processing

[0687] The server first receives driving data sent from the smartphone. This data includes driving behavior data such as speed, acceleration, direction, and braking, as well as emotion data including facial expressions and tone of voice collected by the emotion engine. The server then analyzes this data using a generative AI model.

[0688] The analysis identifies the number of sudden braking and speeding, and also evaluates the user's stress and fatigue state based on emotional data. This allows for more accurate driving improvement suggestions to be made. For example, if sudden braking occurs frequently, the system will generate a suggestion such as "increase the distance between your vehicle and the vehicle ahead," and if stress is high, it will suggest driving habits such as "take appropriate breaks."

[0689] Finally, the generated improvement suggestions are sent to a smartphone in real time via push notification. After the drive is completed, the smartphone displays the collected data and analysis results in charts and graphs, allowing the user to reflect on their driving behavior.

[0690] Examples of specific examples and prompts

[0691] Examples:

[0692] Suppose User A places his or her smartphone in a car mount and begins driving. The smartphone immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, the smartphone uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, the data and User A's emotional data are sent to the server. The server analyzes the collected data using generative AI and determines that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[0693] Example prompt sentence:

[0694] "Driving and emotional data was collected as follows:

[0695] Hard braking: 5 times

[0696] Overspeeding: 2 times

[0697] Stress level: High

[0698] Based on this data, generate specific suggestions to encourage drivers to drive safely.”

[0699] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0700] Step 1:

[0701] Data collection

[0702] The device uses a smartphone placed in a car holder to collect driving behavior data from sensors such as GPS, accelerometer, and gyroscope, and also uses the built-in camera and microphone to capture the user's facial expressions and tone of voice, thereby collecting emotional data.

[0703] Input: Vehicle speed, acceleration, braking, steering, user facial expressions and tone of voice.

[0704] Output: Driving behavior data and emotion data.

[0705] Step 2:

[0706] Data transmission

[0707] The device periodically transmits the collected driving behavior data and emotion data to the server using an encrypted communication protocol.

[0708] Input: Driving behavior data and emotion data.

[0709] Output: Data securely sent to the server.

[0710] Step 3:

[0711] Data analysis

[0712] The server analyzes the received driving behavior data and emotional data. Using a generative AI model, it recognizes the driver's driving patterns, such as the number of sudden braking attempts and the frequency of speeding. It also evaluates the driver's stress and fatigue levels based on the emotional data.

[0713] Input: Submitted driving behavior data and emotion data.

[0714] Output: Driving pattern analysis, stress and fatigue assessment results.

[0715] Step 4:

[0716] Generate driving improvement suggestions

[0717] The server generates driving improvement suggestions for the user based on the analysis results. For example, if the driver brakes suddenly frequently, the server may suggest "increase the distance between your vehicle and the vehicle ahead," or if the driver is judged to be under high stress, the server may suggest "take appropriate breaks."

[0718] Input: Driving pattern analysis results, stress and fatigue assessment results.

[0719] Output: Specific driving improvement suggestions.

[0720] Step 5:

[0721] Feedback Notifications

[0722] The server sends the generated driving improvement suggestions to the terminal, and the terminal notifies the user of them in real time via push notification.

[0723] Input: Specific driving improvement suggestions.

[0724] Output: A push notification that arrives to the user.

[0725] Step 6:

[0726] Visual display of results

[0727] After the drive, the server visually displays the collected driving data and emotional data on the device as charts and graphs, allowing the user to reflect on their own driving behavior and emotional state.

[0728] Input: Driving data and emotion data until the end of the drive.

[0729] Output: Charts and graphs that are visually displayed to the user.

[0730] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0731] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0732] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0733] [Third embodiment]

[0734] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0735] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0736] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0737] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0738] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0739] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0740] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0741] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0742] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0743] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0744] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0745] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0746] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, and a means for providing feedback to users at appropriate times.

[0747] System Configuration

[0748] 1. Device (smartphone)

[0749] The device used is a smartphone. The smartphone is equipped with sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0750] 2. Server

[0751] Data recorded during driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results.

[0752] 3. Means of notification

[0753] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0754] Overview of program processing

[0755] Driving behavior recording

[0756] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, etc. in real time. For example, if the user suddenly accelerates the vehicle, the accelerometer immediately records this data and stores it on the device.

[0757] Data transmission and analysis

[0758] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding.

[0759] Generate and notify improvement suggestions

[0760] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, specific advice such as "increase the distance between your vehicle and the vehicle in front" is generated and sent to the device as a push notification. The device receives this and displays it to the user.

[0761] Review and improvement

[0762] Once the drive is over, the device displays the collected data and the server's analysis results in graphs and charts to the user. This allows the user to reflect on their driving style and understand what improvements they need to make next time. For example, based on past driving history, the device may display information such as "10 sudden braking attempts in one week," encouraging the user to be more careful the next time they drive.

[0763] Specific examples

[0764] For example, suppose User A places their smartphone in a car holder and starts driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to the server. The server uses generative AI to analyze the collected data and determines that User A has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends these to the device as a push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing User A to reflect on their driving behavior and identify areas for improvement for their next drive.

[0765] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and make an effort to drive safely based on specific suggestions for improvement.

[0766] The processing flow will be explained below.

[0767] Step 1:

[0768] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[0769] Step 2:

[0770] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[0771] Step 3:

[0772] The terminal processes the collected data in batches at regular intervals (for example, every minute) and sends it to the server. The SSL / TLS protocol is used for data transmission to ensure data security, which enables data transmission in real time.

[0773] Step 4:

[0774] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise from the data, preparing it for analysis.

[0775] Step 5:

[0776] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns. The generation AI extracts information from driving behavior, such as the frequency of sudden braking and speeding, and identifies risk factors.

[0777] Step 6:

[0778] Based on the analysis results, the server generates driving improvement suggestions for the driver. For example, if the driver frequently brakes suddenly, the server generates specific improvement advice such as "increase the distance between your vehicle and the vehicle ahead."

[0779] Step 7:

[0780] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements they need to make while driving.

[0781] Step 8:

[0782] Once the drive is complete, the device displays the collected data and analysis results in graphs and charts, allowing the user to review their driving history and check their driving behavior in detail.

[0783] Step 9:

[0784] Based on the data and improvement suggestions displayed on the device, users can reflect on their driving style and identify areas for improvement for their next drive. For example, based on past driving history, the system can check information such as "10 sudden braking incidents in one week" and prompt users to be more careful the next time they drive.

[0785] Through the above steps, the system of the present invention is able to record and analyze the driver's driving behavior in detail and provide appropriate driving improvement suggestions in real time.

[0786] Example 1

[0787] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0788] In conventional driving behavior recording systems, the process of data collection, analysis, and feedback was not performed in real time, making it difficult to take an immediate approach to improving safety while driving. Furthermore, analyzing driving data required specialized knowledge, making it difficult to quickly provide specific improvement suggestions to individual drivers. Therefore, there is a need for a system that can record and analyze driving behavior in real time and provide successive improvement suggestions based on the results.

[0789] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0790] In this invention, the server includes: means for recording driving behavior in real time using a terminal installed in the transportation vehicle in which the user rides; means for temporarily saving the recorded driving behavior data; means for periodically transmitting the saved driving behavior data to the server; means for analyzing the driving behavior data received by the server using a generative artificial intelligence model; means for generating specific driving improvement suggestions based on the analysis results; means for sending the generated driving improvement suggestions to the terminal and notifying the user in the form of a push notification; and means for visually displaying the collected driving data and the analysis results to the user as graphs or charts after the end of the drive to encourage reflection. This makes it possible to immediately analyze driving behavior and provide appropriate feedback to the driver, thereby improving driving safety.

[0791] The "terminal" is a device that is installed in the transportation vehicle that the user rides in and that records the driving behavior in real time.

[0792] "Driving behavior data" refers to data collected by a terminal that indicates driving behavior such as the vehicle's speed, acceleration, direction, and braking operation.

[0793] "Temporary storage" refers to the process of storing the driving behavior data collected by the terminal for a certain period of time.

[0794] A "server" is a remote computer system that receives driving behavior data sent from the terminal, analyzes it, and generates driving improvement suggestions.

[0795] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze driving behavior data on a server and generate driving improvement suggestions for the driver.

[0796] "Analysis results" are information derived from driving behavior data processed by a generative artificial intelligence model.

[0797] "Driving improvement suggestions" are specific advice generated based on the analysis results to improve the driver's driving behavior.

[0798] A "push notification" is a notification message sent to a user in real time via a terminal.

[0799] "Visual display" refers to providing the collected data and analysis results to the user in a visual format such as graphs and charts after the drive is completed.

[0800] "Review" refers to the act of the user reviewing their own driving behavior after driving and identifying areas for improvement for the next drive.

[0801] This invention is a system for promoting safe driving by recording and analyzing a driver's driving behavior in real time. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, and a means for providing feedback to the user at an appropriate time.

[0802] System Configuration

[0803] 1. Terminal

[0804] The device used is a smartphone. Smartphones are equipped with built-in sensors such as GPS, accelerometers, and gyroscopes, which are used to record driving behavior in real time. The user places the device in a vehicle holder, and the device begins recording data. Specifically, when the user launches a dedicated smartphone app, the app activates the built-in sensors and collects driving data such as speed, acceleration, direction, and braking operations in real time.

[0805] 2. Server

[0806] The collected driving behavior data is sent to a server at regular intervals. The server receives this data and analyzes it using a generative AI model. The generative AI model recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. For example, the model generates appropriate feedback using a prompt such as, "If the user generates a lot of data on sudden braking while driving, what kind of improvement suggestions would be appropriate?"

[0807] 3. Means of notification

[0808] The server sends the generated improvement suggestions to the device, which then notifies the user in the form of a push notification. The system is designed to not interfere with the driving operation in progress so that notifications are sent at the appropriate time. Even after the drive is over, the device visually displays the collected data and analysis results, making it easy for the user to reflect on their own driving behavior.

[0809] Specific examples

[0810] When User A places their smartphone in the car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If sudden braking is detected while driving, the data is sent to the server. The server analyzes the collected data using a generative AI model and determines that User A has a driving tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends them to the device as a push notification.

[0811] After finishing driving, the device displays the driving history for the past week in chart form, allowing User A to reflect on his / her driving behavior and identify areas for improvement for the next drive. In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and focus on safe driving based on specific suggestions for improvement.

[0812] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0813] Step 1: Start recording your driving behavior

[0814] When the user places the smartphone in the car holder and launches the dedicated app, the device activates its built-in sensors, including GPS, accelerometer, and gyroscope. Inputs include location information (GPS), vehicle speed, acceleration, direction, and braking operations, which are acquired in real time and temporarily stored. Specifically, the device collects data from the sensors every second and records it in memory.

[0815] Step 2: Temporarily save data

[0816] The device temporarily stores driving behavior data collected by sensors in its internal memory. It receives captured speed, acceleration, direction, and braking data as input and stores them in chronological order in storage. The output is the temporarily stored driving behavior data. Specifically, the device adds new data to the existing dataset and stores it.

[0817] Step 3: Send data to the server

[0818] The terminal assembles the temporarily stored data into packets at regular intervals (for example, every 5 minutes) and sends them to a server over the Internet. As input, it receives the temporarily stored driving behavior data, packets it, and sends it. The output is the data packets sent to the server. Specifically, it converts the data into an appropriate format and sends it to the server over an Internet connection.

[0819] Step 4: Receiving and analyzing data

[0820] The server converts the received driving behavior data into a format suitable for analysis and inputs it into the generative AI model. As input, it receives the transmitted data packets and formats them for analysis. The output is driving behavior data in a format suitable for analysis. Specifically, it preprocesses the data to remove unnecessary information and inputs it into the generative AI model.

[0821] Step 5: Analyze driving patterns

[0822] The server uses a generative AI model to recognize driving patterns and identify risk factors. Preprocessed driving behavior data is received as input and fed into the generative AI model. The output is the analysis results. Specifically, the AI ​​model identifies patterns such as the number of sudden braking attempts and the frequency of speeding, and identifies risk factors.

[0823] Step 6: Generate improvement suggestions

[0824] The server generates specific driving improvement suggestions based on the analysis results. It receives the analysis results of the generative AI model as input and generates improvement suggestions. The output is driving improvement suggestions for the user. As a specific action, it generates advice such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking."

[0825] Step 7: Notification of improvement proposals

[0826] The server sends the generated improvement suggestions to the device, and the device notifies the user of the suggestions in the form of a push notification. The server receives the generated driving improvement suggestions as input and sends a push notification to the user as output. The specific operation is to display the notification at an appropriate time to inform the user.

[0827] Step 8: Review after driving

[0828] Once the drive is over, the device visually displays all collected data and the analysis results from the server. As input, it receives the data collected during the drive and the analysis results, and as output, converts this into graphs and charts and displays them. Specifically, it visually displays information such as "10 hard brakes in one week," allowing the user to reflect on their own driving style.

[0829] Through these processing steps, the system gains a detailed understanding of the driver's driving behavior and provides specific suggestions for improvement, thereby promoting safe driving.

[0830] (Application example 1)

[0831] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0832] Improving the operational efficiency and safety of work robots is an important issue in modern factories. Conventional technology makes it difficult to record and analyze the detailed operation history of individual robots, resulting in a lack of real-time improvement suggestions, limiting accident prevention and efficiency improvement. Furthermore, systems that record drivers' driving behavior in detail to promote safe driving only provide feedback after the drive is completed, resulting in a lack of real-time improvement suggestions. To address these issues, the present invention provides a system that records and analyzes both driving and work behavior in real time and provides immediate feedback.

[0833] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0834] In this invention, the server includes means for periodically transmitting collected driving behavior data to the server, means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results, means for identifying driving risk factors and generating specific improvement proposals, means for periodically transmitting collected work behavior data to the server, means for the server to analyze the work behavior data and create work improvement proposals based on the analysis results, and means for identifying work risk factors and generating specific improvement proposals. This makes it possible to record and analyze the behavior of drivers and factory robots in detail and provide improvement proposals in real time.

[0835] "Driving behavior" refers to the actions and movements of a driver when driving a vehicle, including acceleration, speed, direction, braking, etc.

[0836] "Generative AI" is artificial intelligence that analyzes large amounts of data, performs pattern recognition and predictions, and generates appropriate suggestions and results.

[0837] "Analysis results" refers to information such as conclusions and recommendations obtained by the generative AI after analyzing driving behavior data and work behavior data.

[0838] "Improvement proposals" are specific ideas and advice for improving efficiency and safety derived from the analysis results.

[0839] "Work behavior" refers to the movements and motion patterns of a factory robot when performing work, including its speed, acceleration, position, etc.

[0840] "Notification means" refers to the methods or technologies used to notify users of analysis results and improvement suggestions, and push notifications are an example of this.

[0841] "Real-time" refers to the temporal characteristics that allow processes such as data collection, analysis, and notification to be carried out immediately without delay.

[0842] The "server" refers to the infrastructure or system that receives and analyzes driving behavior data and work behavior data, and is equipped with a generation AI.

[0843] An "encrypted communication protocol" refers to the standards and procedures for encrypting and communicating data in order to ensure secure data transmission.

[0844] "Reflection" refers to the process of reviewing past actions and work based on collected data and analysis results after a drive or task has been completed, and identifying areas for improvement.

[0845] "Visual display" refers to a method of presenting data or analytical results to a user in a visual form such as a graph, chart, or diagram.

[0846] A system for realizing this invention is mainly composed of a terminal (a smartphone or a factory robot control unit), a server, and a notification means.

[0847] Terminal

[0848] The terminals used are smartphones and factory robot control units. These terminals are equipped with multiple sensors, including GPS, accelerometers, and gyroscopes, and record driving and work behavior in real time. The terminal is installed in an in-vehicle holder or on the robot, and data collection begins.

[0849] server

[0850] The server receives the data sent from the device and analyzes it using generative AI. Specifically, it uses AI libraries such as TensorFlow and PyTorch. The server recognizes driving and work patterns, identifies risk factors, and generates improvement suggestions. The analyzed data is important for improving the efficiency and safety of driving or work.

[0851] Notification means

[0852] The generated improvement suggestions are notified to the user in real time using push notification technology. For example, specific advice such as "increase the distance between vehicles" is sent to a driver who frequently brakes suddenly. In the case of factory robots, improvement suggestions such as "inspect tools" and "recalculate loads" are notified.

[0853] Review and improvement

[0854] After a drive or task is completed, the device provides the collected data and analysis results to the user in the form of charts and graphs, allowing the user to reflect on their own behavior and understand what improvements to make next time.

[0855] Specific examples

[0856] For example, when a user places their smartphone in a car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to a server. The server analyzes the data using generative AI and determines that the driver has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking," and sends these via push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing the user to reflect on their driving and identify areas for improvement for their next drive.

[0857] Prompt Sentence Examples

[0858] An example of a prompt sentence to input to the generative AI model is as follows:

[0859] Generate operational improvement proposals for increasing efficiency and ensuring safety based on the operational data of factory robots. Analyze the following data:

[0860] Acceleration: {Acceleration data}

[0861] Location: {location data}

[0862] Speed: {speed data}

[0863] Abnormal behavior frequency: {Abnormal behavior data}

[0864] (example):

[0865] If the robot detects sudden acceleration while working, please suggest the cause and remedial measures.

[0866] By using such a system, it is possible to gain a detailed understanding of the behavior of drivers and factory robots and provide specific improvement suggestions.

[0867] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0868] Step 1:

[0869] The device records driving or work behavior

[0870] The terminal (a smartphone or a factory robot control unit) collects driving or work behavior data in real time using sensors such as GPS, accelerometers, and gyroscopes. The input is raw data from the sensors, which includes speed, acceleration, location information, etc. The output is a set of recorded raw data.

[0871] Step 2:

[0872] The device periodically sends data to the server.

[0873] The device sends the collected data to the server at regular intervals. The input is the set of recorded raw data, and the output is the data sent to the server. This process uses an encrypted communication protocol to ensure the secure transmission of data.

[0874] Step 3:

[0875] The server analyzes the data

[0876] The server uses the received data to perform analysis using a generative AI model (e.g., TensorFlow or PyTorch). The input is the raw data sent from the device, and the machine learning model analyzes it for behavioral and abnormal patterns. The output is the analysis results, which include specific risk factors and patterns in driving or work.

[0877] Step 4:

[0878] The server generates and notifies improvement suggestions

[0879] Based on the analysis results, the server uses a generative AI to generate specific improvement suggestions. For example, if there are frequent sudden braking attempts, the server generates suggestions such as "increase the distance between vehicles," or "inspect tools" if abnormal robot behavior is detected. The input is the analysis results, and the output is the improvement suggestions. These are sent to the device via push notification, and the user is notified in real time.

[0880] Step 5:

[0881] The device visually displays data after driving or work is completed

[0882] After driving or working, the device provides the user with charts and graphs based on the collected data and the server's analysis results. The input is the analysis results from the server and the collected raw data, and the output is a visual display. This allows the user to reflect on their own driving or working behavior and understand what needs to be improved next time.

[0883] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0884] This invention is a system for promoting safe driving by recording and analyzing the driver's driving behavior in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[0885] System Configuration

[0886] 1. Device (smartphone)

[0887] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. It also has an emotion engine that uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0888] 2. Server

[0889] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. In addition, emotional data obtained by an emotion engine is also used in the analysis.

[0890] 3. Means of notification

[0891] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0892] Overview of program processing

[0893] Recording driving behavior and emotions

[0894] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, recording emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[0895] Data transmission and analysis

[0896] Data collected while driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden brakings and speeding, as well as the user's stress and fatigue levels based on emotional data.

[0897] Generate and notify operational improvement suggestions

[0898] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[0899] Review and improvement

[0900] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[0901] Specific examples

[0902] For example, suppose User A places his or her smartphone in a car mount and begins driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, it uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, that data and User A's emotional data are sent to the server. The server uses generative AI to analyze the collected data and determine that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[0903] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and emotional state, and can make efforts to drive safely based on specific suggestions for improvement.

[0904] The processing flow will be explained below.

[0905] Step 1:

[0906] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[0907] Step 2:

[0908] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[0909] Step 3:

[0910] The device uses a built-in camera and microphone to analyze the user's facial expressions and tone of voice with an emotion engine, and records the user's emotional data in real time. For example, if the user is surprised, the device will detect changes in their facial expressions and voice and record them as emotional data.

[0911] Step 4:

[0912] The device processes driving behavior data and emotion data in batches at regular intervals (for example, every minute) and sends them to the server. The SSL / TLS protocol is used for data transmission to ensure data security, making it possible to transmit data in real time.

[0913] Step 5:

[0914] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise, to prepare the data for analysis.

[0915] Step 6:

[0916] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns and emotional state. The generation AI extracts information such as the frequency of sudden braking and speeding from the driving behavior data, and also identifies the user's stress and fatigue levels based on the emotional data.

[0917] Step 7:

[0918] Based on the analysis results, the server generates specific driving improvement suggestions for the driver. For example, for a driver who frequently brakes suddenly, the server generates specific advice such as "increase the distance between your vehicle and the vehicle in front." In addition, if the user is feeling stressed, the server generates suggestions such as "take appropriate breaks."

[0919] Step 8:

[0920] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements and stress reduction measures they need to take while driving.

[0921] Step 9:

[0922] After the drive is completed, the device presents the collected data and analysis results to the user in the form of graphs and charts, allowing the user to review their past driving history and emotional state and specifically check their own driving behavior and emotional state.

[0923] Step 10:

[0924] Based on the data and improvement suggestions displayed on the device, users can reflect on their own driving style and emotional state and understand what needs to be improved the next time they drive. For example, based on past driving history, the device can confirm information such as "You braked suddenly 10 times in one week, and felt high stress at those times," and provide a warning for the next time you drive.

[0925] Through the above steps, the system of the present invention can record and analyze the driver's driving behavior and emotional state in detail, and provide appropriate driving improvement suggestions and stress reduction measures in real time.

[0926] Example 2

[0927] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0928] Conventional driving management systems have difficulty recording detailed driver behavior in real time and providing specific improvement suggestions to promote safe driving. Furthermore, systems that analyze and provide feedback on users' emotional states are limited, leaving a lack of effective means to support the development of safe driving habits. There was also a need for a method to safely transmit collected driving and emotional data and notify users of specific improvement suggestions in real time based on the analysis results.

[0929] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0930] In this invention, the server includes a terminal means for recording the driver's driving behavior in real time, a means for recording speed, acceleration, direction, and braking operation using the terminal's sensors, a means for recognizing the user's emotions using the terminal's camera and microphone, a means for periodically transmitting the recorded driving behavior and emotional data to the server, a means for the server to analyze the received data using artificial intelligence to identify driving patterns and risk factors, a means for generating and notifying the user of appropriate driving improvement suggestions based on the analysis results, and a means for visually displaying the analysis results to the user after driving has ended to encourage reflection. This makes it possible to record the driver's driving behavior and emotional state in detail and provide specific improvement suggestions based on the results.

[0931] "Driver's driving behavior" refers to a series of actions performed by a driver while driving, such as the vehicle's speed, acceleration, direction, and braking operation.

[0932] "Terminal" refers to a device for recording and transmitting driving behavior and emotional data in real time, and generally refers to a smartphone or tablet device.

[0933] "Sensors" are measurement devices, such as GPS, accelerometers, and gyroscopes, that measure and record the vehicle's driving behavior in real time.

[0934] The "camera" is an image capturing device for capturing images of the user's facial expressions and recognizing their emotions.

[0935] A "microphone" is a voice capture device that picks up the user's voice and analyzes their emotional state.

[0936] "Emotion data" is information about the user's emotional state, such as stress or fatigue, analyzed from their facial expressions and tone of voice.

[0937] The "server" is a computer system that receives data sent from the terminal and generates analysis and driving improvement suggestions.

[0938] "Generative AI" is an AI technology that analyzes large amounts of data to identify driving patterns and risk factors.

[0939] A "driving pattern" is a consistent driving tendency or habit identified from a driver's driving behavior.

[0940] A "risk factor" is a behavior or situation that could potentially pose a risk while driving.

[0941] "Driving improvement suggestions" are specific advice provided to drivers based on the analysis results to promote safe driving.

[0942] "Push notifications" is a technology that sends new information and notifications to users' devices in real time.

[0943] An "encrypted communication protocol" is a communication method that encrypts information to ensure secure data transmission and reception.

[0944] "Driving history" refers to the accumulation of driving behavior data and analysis results recorded in the past.

[0945] "Charts and graphs" are diagrams that visually display information and are a means of presenting driving and emotional data in an easily understandable format.

[0946] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[0947] System Configuration

[0948] 1. Device (smartphone)

[0949] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The smartphone also uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[0950] 2. Server

[0951] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generative AI model. Specifically, it recognizes driving patterns and identifies risk factors, and generates specific improvement proposals based on the analysis results. Emotion data obtained by the emotion engine is also used in the analysis.

[0952] 3. Means of notification

[0953] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[0954] Specific examples of program processing

[0955] 1. Recording driving behavior and emotions

[0956] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, and records emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[0957] 2. Data submission and analysis

[0958] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses a generative AI model to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding, as well as the user's stress and fatigue levels based on emotional data.

[0959] 3. Generation and notification of driving improvement suggestions

[0960] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[0961] 4. Reflection and Improvement

[0962] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[0963] Prompt Sentence Examples

[0964] "Analyze the driver's driving patterns and generate specific improvement suggestions based on the number of sudden braking and speeding. Also, use emotional data to determine if the user is stressed and suggest appropriate breaks."

[0965] In this way, the system of the present invention allows drivers to understand their own driving behavior and emotional state in detail, and enables them to focus on safe driving based on specific suggestions for improvement.

[0966] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0967] System program processing flow

[0968] Step 1:

[0969] The device activates the smartphone's sensors and records driving behavior.

[0970] Input: GPS, accelerometer, gyroscope data

[0971] Data processing: Real-time acquisition of speed, acceleration, direction, and braking data

[0972] Output: Driving behavior data

[0973] How it works: When a user starts the car engine and places the smartphone in the car mount, the device automatically activates the GPS, accelerometer, gyroscope, etc. This starts recording the car's speed, acceleration, and direction in real time, as well as data on when braking occurs.

[0974] Step 2:

[0975] The device activates the camera and microphone to record the user's emotions.

[0976] Input: Camera video, microphone audio

[0977] Data processing: Analyzing emotional states from facial expressions and tone of voice

[0978] Output: Emotion data

[0979] Specific operation: The device takes a picture of the user's face using the smartphone camera and collects their voice using the microphone. The emotion engine analyzes this data and records the user's emotional state (e.g., stress, fatigue) in real time.

[0980] Step 3:

[0981] The terminal transmits the recorded driving behavior data and emotion data to a server.

[0982] Input: driving behavior data, emotion data

[0983] Data processing: compressing data and preparing it for transmission

[0984] Output: Data sent to the server

[0985] Specific operation: At regular intervals, the terminal compresses the data, checks for errors, and then sends it to the server via the Internet.

[0986] Step 4:

[0987] The server analyzes the received data.

[0988] Input: driving behavior data, emotion data

[0989] Data Computation: Using generative AI models to identify driving patterns and risk factors

[0990] Output: Analysis results (driving patterns, risk factors)

[0991] How it works: The server adds the received data to an analysis queue and analyzes it using a generative AI model, which identifies the number of sudden braking attempts and speeding trends, and analyzes the user's emotional state to determine stress and fatigue levels.

[0992] Step 5:

[0993] The server generates driving improvement suggestions based on the analysis results.

[0994] Input: Analysis results

[0995] Data processing: generating concrete improvement proposals

[0996] Output: Driving improvement suggestions

[0997] Specific operation: Based on the analysis results, the server generates advice such as "increase the distance between your vehicle and the vehicle in front," and if stress is high, makes suggestions such as "take appropriate breaks."

[0998] Step 6:

[0999] The server generates driving improvement suggestions and sends them to the terminal.

[1000] Input: Driving improvement suggestions

[1001] Data processing: Preparing the proposed data for sending

[1002] Output: Proposal data sent to the device

[1003] Specific operation: The server checks the proposed data for errors and sends it to the device as a push notification. The device receives it and notifies the user in real time.

[1004] Step 7:

[1005] The device visually displays the collected data after the drive is completed.

[1006] Input: driving behavior data, emotion data, analysis results

[1007] Data processing: Convert data into graphs and charts

[1008] Output: Visual display

[1009] Specific operation: After the user finishes driving, the device analyzes the collected data and presents it to the user in the form of visual graphs and charts. For example, it may display information such as "10 sudden braking attempts" to encourage caution the next time the user drives. It also displays emotional data to help users understand their stress level and fatigue state.

[1010] Through each step, the system records and analyzes the driver's driving behavior and emotional state in detail, and provides specific improvement suggestions to promote safe driving.

[1011] (Application example 2)

[1012] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1013] In recent years, systems have been developed to record and analyze driving behavior in real time in order to promote safe driving, but these systems focus only on the driver's driving behavior and do not take into account the emotional state or fatigue level of the driver or passengers. This poses the challenge of making it difficult to develop individual, specific measures to reduce the impact of emotional state and fatigue level on driving.

[1014] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1015] In this invention, the server includes a means for recognizing and recording emotional data of the user while driving, a means for complementing the analysis results based on the emotional data and generating more accurate driving improvement suggestions, and a means for generating suggestions for driving habits such as taking breaks based on stress and fatigue levels, thereby enabling safe driving support that takes into account the emotional state and fatigue level.

[1016] "Driving behavior" refers to driver actions such as vehicle speed, acceleration, braking, and steering.

[1017] "Real-time" means that all operations and situations during driving are immediately recorded and analyzed and feedback is provided almost simultaneously.

[1018] "Recording means" refers to a device or system that uses sensors or devices to collect and store driving behavior and emotional data.

[1019] "Generative artificial intelligence" refers to machine learning models and data processing algorithms that analyze driving behavior data, recognize patterns, and generate suggestions.

[1020] "Means for analysis" refers to software or hardware that evaluates collected driving behavior data and emotion data and performs computational processing to identify risk factors and areas for improvement.

[1021] "Means of notification" refers to communication methods, such as push notifications or display functions on smartphones, used to inform users of analysis results and suggestions.

[1022] "Visual display means" refers to the display or software that presents the analysis results to the user in the form of graphs, charts, etc.

[1023] "Emotional data" refers to data that indicates a user's emotional or psychological state, derived from facial expressions or tone of voice.

[1024] "Complementary means" refers to methods and systems that use emotion data to further refine the analysis results of driving behavior data and improve the accuracy of suggestions.

[1025] "Driving improvement suggestions" refer to specific advice and instructions to drivers to help them review their driving behavior and encourage safe driving.

[1026] "Rest Suggestion" refers to instructions that encourage drivers and passengers to take appropriate rest breaks if they feel stressed or fatigued while driving.

[1027] This invention is a system for recording and analyzing the driving behavior and emotional state of drivers and passengers to promote safe driving. The system consists of a smartphone, sensors, a camera, a microphone, a server, and a generative AI model.

[1028] Hardware and Software Configuration

[1029] 1. Smartphone

[1030] Smartphones are equipped with built-in sensors such as GPS, accelerometer, gyroscope, camera, and microphone.

[1031] The smartphone is placed in a car holder and data collection begins as soon as the driver starts driving.

[1032] 2. Emotion Engine

[1033] Using data from the built-in camera and microphone, the device analyzes the user's facial expressions and tone of voice to record emotional data.

[1034] The emotion engine uses machine learning algorithms to recognize emotional states in real time.

[1035] 3. Server

[1036] Receives driving behavior data and emotion data periodically sent from a smartphone.

[1037] A generative AI model is used to analyze incoming data, recognize driving patterns, and identify risk factors.

[1038] Based on the analysis results, specific driving improvement suggestions are generated and sent to a smartphone.

[1039] 4. Means of notification

[1040] The smartphone receives the improvement suggestions sent from the server and notifies the user via push notification.

[1041] Even after driving is complete, the collected data and analysis results are visually displayed, making it easier for users to reflect on their own driving behavior.

[1042] A natural language description of the program's processing

[1043] The server first receives driving data sent from the smartphone. This data includes driving behavior data such as speed, acceleration, direction, and braking, as well as emotion data including facial expressions and tone of voice collected by the emotion engine. The server then analyzes this data using a generative AI model.

[1044] The analysis identifies the number of sudden braking and speeding, and also evaluates the user's stress and fatigue state based on emotional data. This allows for more accurate driving improvement suggestions to be made. For example, if sudden braking occurs frequently, the system will generate a suggestion such as "increase the distance between your vehicle and the vehicle ahead," and if stress is high, it will suggest driving habits such as "take appropriate breaks."

[1045] Finally, the generated improvement suggestions are sent to a smartphone in real time via push notification. After the drive is completed, the smartphone displays the collected data and analysis results in charts and graphs, allowing the user to reflect on their driving behavior.

[1046] Examples of specific examples and prompts

[1047] Examples:

[1048] Suppose User A places his or her smartphone in a car mount and begins driving. The smartphone immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, the smartphone uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, the data and User A's emotional data are sent to the server. The server analyzes the collected data using generative AI and determines that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[1049] Example prompt sentence:

[1050] "Driving and emotional data was collected as follows:

[1051] Hard braking: 5 times

[1052] Overspeeding: 2 times

[1053] Stress level: High

[1054] Based on this data, generate specific suggestions to encourage drivers to drive safely.”

[1055] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1056] Step 1:

[1057] Data collection

[1058] The device uses a smartphone placed in a car holder to collect driving behavior data from sensors such as GPS, accelerometer, and gyroscope, and also uses the built-in camera and microphone to capture the user's facial expressions and tone of voice, thereby collecting emotional data.

[1059] Input: Vehicle speed, acceleration, braking, steering, user facial expressions and tone of voice.

[1060] Output: Driving behavior data and emotion data.

[1061] Step 2:

[1062] Data transmission

[1063] The device periodically transmits the collected driving behavior data and emotion data to the server using an encrypted communication protocol.

[1064] Input: Driving behavior data and emotion data.

[1065] Output: Data securely sent to the server.

[1066] Step 3:

[1067] Data analysis

[1068] The server analyzes the received driving behavior data and emotional data. Using a generative AI model, it recognizes the driver's driving patterns, such as the number of sudden braking attempts and the frequency of speeding. It also evaluates the driver's stress and fatigue levels based on the emotional data.

[1069] Input: Submitted driving behavior data and emotion data.

[1070] Output: Driving pattern analysis, stress and fatigue assessment results.

[1071] Step 4:

[1072] Generate driving improvement suggestions

[1073] The server generates driving improvement suggestions for the user based on the analysis results. For example, if the driver brakes suddenly frequently, the server may suggest "increase the distance between your vehicle and the vehicle ahead," or if the driver is judged to be under high stress, the server may suggest "take appropriate breaks."

[1074] Input: Driving pattern analysis results, stress and fatigue assessment results.

[1075] Output: Specific driving improvement suggestions.

[1076] Step 5:

[1077] Feedback Notifications

[1078] The server sends the generated driving improvement suggestions to the terminal, and the terminal notifies the user of them in real time via push notification.

[1079] Input: Specific driving improvement suggestions.

[1080] Output: A push notification that arrives to the user.

[1081] Step 6:

[1082] Visual display of results

[1083] After the drive, the server visually displays the collected driving data and emotional data on the device as charts and graphs, allowing the user to reflect on their own driving behavior and emotional state.

[1084] Input: Driving data and emotion data until the end of the drive.

[1085] Output: Charts and graphs that are visually displayed to the user.

[1086] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1088] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1089] [Fourth embodiment]

[1090] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1091] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1093] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1094] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1097] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1098] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1099] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1101] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1102] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1103] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, and a means for providing feedback to users at appropriate times.

[1104] System Configuration

[1105] 1. Device (smartphone)

[1106] The device used is a smartphone. The smartphone is equipped with sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[1107] 2. Server

[1108] Data recorded during driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results.

[1109] 3. Means of notification

[1110] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[1111] Overview of program processing

[1112] Driving behavior recording

[1113] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, etc. in real time. For example, if the user suddenly accelerates the vehicle, the accelerometer immediately records this data and stores it on the device.

[1114] Data transmission and analysis

[1115] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding.

[1116] Generate and notify improvement suggestions

[1117] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, specific advice such as "increase the distance between your vehicle and the vehicle in front" is generated and sent to the device as a push notification. The device receives this and displays it to the user.

[1118] Review and improvement

[1119] Once the drive is over, the device displays the collected data and the server's analysis results in graphs and charts to the user. This allows the user to reflect on their driving style and understand what improvements they need to make next time. For example, based on past driving history, the device may display information such as "10 sudden braking attempts in one week," encouraging the user to be more careful the next time they drive.

[1120] Specific examples

[1121] For example, suppose User A places their smartphone in a car holder and starts driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to the server. The server uses generative AI to analyze the collected data and determines that User A has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends these to the device as a push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing User A to reflect on their driving behavior and identify areas for improvement for their next drive.

[1122] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and make an effort to drive safely based on specific suggestions for improvement.

[1123] The processing flow will be explained below.

[1124] Step 1:

[1125] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[1126] Step 2:

[1127] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[1128] Step 3:

[1129] The terminal processes the collected data in batches at regular intervals (for example, every minute) and sends it to the server. The SSL / TLS protocol is used for data transmission to ensure data security, which enables data transmission in real time.

[1130] Step 4:

[1131] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise from the data, preparing it for analysis.

[1132] Step 5:

[1133] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns. The generation AI extracts information from driving behavior, such as the frequency of sudden braking and speeding, and identifies risk factors.

[1134] Step 6:

[1135] Based on the analysis results, the server generates driving improvement suggestions for the driver. For example, if the driver frequently brakes suddenly, the server generates specific improvement advice such as "increase the distance between your vehicle and the vehicle ahead."

[1136] Step 7:

[1137] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements they need to make while driving.

[1138] Step 8:

[1139] Once the drive is complete, the device displays the collected data and analysis results in graphs and charts, allowing the user to review their driving history and check their driving behavior in detail.

[1140] Step 9:

[1141] Based on the data and improvement suggestions displayed on the device, users can reflect on their driving style and identify areas for improvement for their next drive. For example, based on past driving history, the system can check information such as "10 sudden braking incidents in one week" and prompt users to be more careful the next time they drive.

[1142] Through the above steps, the system of the present invention is able to record and analyze the driver's driving behavior in detail and provide appropriate driving improvement suggestions in real time.

[1143] Example 1

[1144] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1145] In conventional driving behavior recording systems, the process of data collection, analysis, and feedback was not performed in real time, making it difficult to take an immediate approach to improving safety while driving. Furthermore, analyzing driving data required specialized knowledge, making it difficult to quickly provide specific improvement suggestions to individual drivers. Therefore, there is a need for a system that can record and analyze driving behavior in real time and provide successive improvement suggestions based on the results.

[1146] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1147] In this invention, the server includes: means for recording driving behavior in real time using a terminal installed in the transportation vehicle in which the user rides; means for temporarily saving the recorded driving behavior data; means for periodically transmitting the saved driving behavior data to the server; means for analyzing the driving behavior data received by the server using a generative artificial intelligence model; means for generating specific driving improvement suggestions based on the analysis results; means for sending the generated driving improvement suggestions to the terminal and notifying the user in the form of a push notification; and means for visually displaying the collected driving data and the analysis results to the user as graphs or charts after the end of the drive to encourage reflection. This makes it possible to immediately analyze driving behavior and provide appropriate feedback to the driver, thereby improving driving safety.

[1148] The "terminal" is a device that is installed in the transportation vehicle that the user rides in and that records the driving behavior in real time.

[1149] "Driving behavior data" refers to data collected by a terminal that indicates driving behavior such as the vehicle's speed, acceleration, direction, and braking operation.

[1150] "Temporary storage" refers to the process of storing the driving behavior data collected by the terminal for a certain period of time.

[1151] A "server" is a remote computer system that receives driving behavior data sent from the terminal, analyzes it, and generates driving improvement suggestions.

[1152] A "generative artificial intelligence model" is an artificial intelligence technology used to analyze driving behavior data on a server and generate driving improvement suggestions for the driver.

[1153] "Analysis results" are information derived from driving behavior data processed by a generative artificial intelligence model.

[1154] "Driving improvement suggestions" are specific advice generated based on the analysis results to improve the driver's driving behavior.

[1155] A "push notification" is a notification message sent to a user in real time via a terminal.

[1156] "Visual display" refers to providing the collected data and analysis results to the user in a visual format such as graphs and charts after the drive is completed.

[1157] "Review" refers to the act of the user reviewing their own driving behavior after driving and identifying areas for improvement for the next drive.

[1158] This invention is a system for promoting safe driving by recording and analyzing a driver's driving behavior in real time. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, and a means for providing feedback to the user at an appropriate time.

[1159] System Configuration

[1160] 1. Terminal

[1161] The device used is a smartphone. Smartphones are equipped with built-in sensors such as GPS, accelerometers, and gyroscopes, which are used to record driving behavior in real time. The user places the device in a vehicle holder, and the device begins recording data. Specifically, when the user launches a dedicated smartphone app, the app activates the built-in sensors and collects driving data such as speed, acceleration, direction, and braking operations in real time.

[1162] 2. Server

[1163] The collected driving behavior data is sent to a server at regular intervals. The server receives this data and analyzes it using a generative AI model. The generative AI model recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. For example, the model generates appropriate feedback using a prompt such as, "If the user generates a lot of data on sudden braking while driving, what kind of improvement suggestions would be appropriate?"

[1164] 3. Means of notification

[1165] The server sends the generated improvement suggestions to the device, which then notifies the user in the form of a push notification. The system is designed to not interfere with the driving operation in progress so that notifications are sent at the appropriate time. Even after the drive is over, the device visually displays the collected data and analysis results, making it easy for the user to reflect on their own driving behavior.

[1166] Specific examples

[1167] When User A places their smartphone in the car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If sudden braking is detected while driving, the data is sent to the server. The server analyzes the collected data using a generative AI model and determines that User A has a driving tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "Increase the distance between your vehicle and the vehicle ahead to reduce sudden braking," and sends them to the device as a push notification.

[1168] After finishing driving, the device displays the driving history for the past week in chart form, allowing User A to reflect on his / her driving behavior and identify areas for improvement for the next drive. In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and focus on safe driving based on specific suggestions for improvement.

[1169] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1170] Step 1: Start recording your driving behavior

[1171] When the user places the smartphone in the car holder and launches the dedicated app, the device activates its built-in sensors, including GPS, accelerometer, and gyroscope. Inputs include location information (GPS), vehicle speed, acceleration, direction, and braking operations, which are acquired in real time and temporarily stored. Specifically, the device collects data from the sensors every second and records it in memory.

[1172] Step 2: Temporarily save data

[1173] The device temporarily stores driving behavior data collected by sensors in its internal memory. It receives captured speed, acceleration, direction, and braking data as input and stores them in chronological order in storage. The output is the temporarily stored driving behavior data. Specifically, the device adds new data to the existing dataset and stores it.

[1174] Step 3: Send data to the server

[1175] The terminal assembles the temporarily stored data into packets at regular intervals (for example, every 5 minutes) and sends them to a server over the Internet. As input, it receives the temporarily stored driving behavior data, packets it, and sends it. The output is the data packets sent to the server. Specifically, it converts the data into an appropriate format and sends it to the server over an Internet connection.

[1176] Step 4: Receiving and analyzing data

[1177] The server converts the received driving behavior data into a format suitable for analysis and inputs it into the generative AI model. As input, it receives the transmitted data packets and formats them for analysis. The output is driving behavior data in a format suitable for analysis. Specifically, it preprocesses the data to remove unnecessary information and inputs it into the generative AI model.

[1178] Step 5: Analyze driving patterns

[1179] The server uses a generative AI model to recognize driving patterns and identify risk factors. Preprocessed driving behavior data is received as input and fed into the generative AI model. The output is the analysis results. Specifically, the AI ​​model identifies patterns such as the number of sudden braking attempts and the frequency of speeding, and identifies risk factors.

[1180] Step 6: Generate improvement suggestions

[1181] The server generates specific driving improvement suggestions based on the analysis results. It receives the analysis results of the generative AI model as input and generates improvement suggestions. The output is driving improvement suggestions for the user. As a specific action, it generates advice such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking."

[1182] Step 7: Notification of improvement proposals

[1183] The server sends the generated improvement suggestions to the device, and the device notifies the user of the suggestions in the form of a push notification. The server receives the generated driving improvement suggestions as input and sends a push notification to the user as output. The specific operation is to display the notification at an appropriate time to inform the user.

[1184] Step 8: Review after driving

[1185] Once the drive is over, the device visually displays all collected data and the analysis results from the server. As input, it receives the data collected during the drive and the analysis results, and as output, converts this into graphs and charts and displays them. Specifically, it visually displays information such as "10 hard brakes in one week," allowing the user to reflect on their own driving style.

[1186] Through these processing steps, the system gains a detailed understanding of the driver's driving behavior and provides specific suggestions for improvement, thereby promoting safe driving.

[1187] (Application example 1)

[1188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1189] Improving the operational efficiency and safety of work robots is an important issue in modern factories. Conventional technology makes it difficult to record and analyze the detailed operation history of individual robots, resulting in a lack of real-time improvement suggestions, limiting accident prevention and efficiency improvement. Furthermore, systems that record drivers' driving behavior in detail to promote safe driving only provide feedback after the drive is completed, resulting in a lack of real-time improvement suggestions. To address these issues, the present invention provides a system that records and analyzes both driving and work behavior in real time and provides immediate feedback.

[1190] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1191] In this invention, the server includes means for periodically transmitting collected driving behavior data to the server, means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results, means for identifying driving risk factors and generating specific improvement proposals, means for periodically transmitting collected work behavior data to the server, means for the server to analyze the work behavior data and create work improvement proposals based on the analysis results, and means for identifying work risk factors and generating specific improvement proposals. This makes it possible to record and analyze the behavior of drivers and factory robots in detail and provide improvement proposals in real time.

[1192] "Driving behavior" refers to the actions and movements of a driver when driving a vehicle, including acceleration, speed, direction, braking, etc.

[1193] "Generative AI" is artificial intelligence that analyzes large amounts of data, performs pattern recognition and predictions, and generates appropriate suggestions and results.

[1194] "Analysis results" refers to information such as conclusions and recommendations obtained by the generative AI after analyzing driving behavior data and work behavior data.

[1195] "Improvement proposals" are specific ideas and advice for improving efficiency and safety derived from the analysis results.

[1196] "Work behavior" refers to the movements and motion patterns of a factory robot when performing work, including its speed, acceleration, position, etc.

[1197] "Notification means" refers to the methods or technologies used to notify users of analysis results and improvement suggestions, and push notifications are an example of this.

[1198] "Real-time" refers to the temporal characteristics that allow processes such as data collection, analysis, and notification to be carried out immediately without delay.

[1199] The "server" refers to the infrastructure or system that receives and analyzes driving behavior data and work behavior data, and is equipped with a generation AI.

[1200] An "encrypted communication protocol" refers to the standards and procedures for encrypting and communicating data in order to ensure secure data transmission.

[1201] "Reflection" refers to the process of reviewing past actions and work based on collected data and analysis results after a drive or task has been completed, and identifying areas for improvement.

[1202] "Visual display" refers to a method of presenting data or analytical results to a user in a visual form such as a graph, chart, or diagram.

[1203] A system for realizing this invention is mainly composed of a terminal (a smartphone or a factory robot control unit), a server, and a notification means.

[1204] Terminal

[1205] The terminals used are smartphones and factory robot control units. These terminals are equipped with multiple sensors, including GPS, accelerometers, and gyroscopes, and record driving and work behavior in real time. The terminal is installed in an in-vehicle holder or on the robot, and data collection begins.

[1206] server

[1207] The server receives the data sent from the device and analyzes it using generative AI. Specifically, it uses AI libraries such as TensorFlow and PyTorch. The server recognizes driving and work patterns, identifies risk factors, and generates improvement suggestions. The analyzed data is important for improving the efficiency and safety of driving or work.

[1208] Notification means

[1209] The generated improvement suggestions are notified to the user in real time using push notification technology. For example, specific advice such as "increase the distance between vehicles" is sent to a driver who frequently brakes suddenly. In the case of factory robots, improvement suggestions such as "inspect tools" and "recalculate loads" are notified.

[1210] Review and improvement

[1211] After a drive or task is completed, the device provides the collected data and analysis results to the user in the form of charts and graphs, allowing the user to reflect on their own behavior and understand what improvements to make next time.

[1212] Specific examples

[1213] For example, when a user places their smartphone in a car holder and starts driving, the device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. If it detects sudden braking while driving, the data is sent to a server. The server analyzes the data using generative AI and determines that the driver has a tendency to frequently brake suddenly. The server then generates improvement suggestions, such as "increase the distance between your vehicle and the vehicle in front to reduce sudden braking," and sends these via push notification. After finishing the drive, the device displays the week's driving history in chart form, allowing the user to reflect on their driving and identify areas for improvement for their next drive.

[1214] Prompt Sentence Examples

[1215] An example of a prompt sentence to input to the generative AI model is as follows:

[1216] Generate operational improvement proposals for increasing efficiency and ensuring safety based on the operational data of factory robots. Analyze the following data:

[1217] Acceleration: {Acceleration data}

[1218] Location: {location data}

[1219] Speed: {speed data}

[1220] Abnormal behavior frequency: {Abnormal behavior data}

[1221] (example):

[1222] If the robot detects sudden acceleration while working, please suggest the cause and remedial measures.

[1223] By using such a system, it is possible to gain a detailed understanding of the behavior of drivers and factory robots and provide specific improvement suggestions.

[1224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1225] Step 1:

[1226] The device records driving or work behavior

[1227] The terminal (a smartphone or a factory robot control unit) collects driving or work behavior data in real time using sensors such as GPS, accelerometers, and gyroscopes. The input is raw data from the sensors, which includes speed, acceleration, location information, etc. The output is a set of recorded raw data.

[1228] Step 2:

[1229] The device periodically sends data to the server.

[1230] The device sends the collected data to the server at regular intervals. The input is the set of recorded raw data, and the output is the data sent to the server. This process uses an encrypted communication protocol to ensure the secure transmission of data.

[1231] Step 3:

[1232] The server analyzes the data

[1233] The server uses the received data to perform analysis using a generative AI model (e.g., TensorFlow or PyTorch). The input is the raw data sent from the device, and the machine learning model analyzes it for behavioral and abnormal patterns. The output is the analysis results, which include specific risk factors and patterns in driving or work.

[1234] Step 4:

[1235] The server generates and notifies improvement suggestions

[1236] Based on the analysis results, the server uses a generative AI to generate specific improvement suggestions. For example, if there are frequent sudden braking attempts, the server generates suggestions such as "increase the distance between vehicles," or "inspect tools" if abnormal robot behavior is detected. The input is the analysis results, and the output is the improvement suggestions. These are sent to the device via push notification, and the user is notified in real time.

[1237] Step 5:

[1238] The device visually displays data after driving or work is completed

[1239] After driving or working, the device provides the user with charts and graphs based on the collected data and the server's analysis results. The input is the analysis results from the server and the collected raw data, and the output is a visual display. This allows the user to reflect on their own driving or working behavior and understand what needs to be improved next time.

[1240] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1241] This invention is a system for promoting safe driving by recording and analyzing the driver's driving behavior in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement proposals based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[1242] System Configuration

[1243] 1. Device (smartphone)

[1244] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. It also has an emotion engine that uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[1245] 2. Server

[1246] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generation AI. The generation AI recognizes driving patterns and identifies risk factors, and generates specific improvement suggestions based on the analysis results. In addition, emotional data obtained by an emotion engine is also used in the analysis.

[1247] 3. Means of notification

[1248] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[1249] Overview of program processing

[1250] Recording driving behavior and emotions

[1251] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, recording emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[1252] Data transmission and analysis

[1253] Data collected while driving is sent to a server at regular intervals. The server analyzes the received data and uses generative AI to recognize the driver's driving patterns. This analysis identifies the number of sudden brakings and speeding, as well as the user's stress and fatigue levels based on emotional data.

[1254] Generate and notify operational improvement suggestions

[1255] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[1256] Review and improvement

[1257] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[1258] Specific examples

[1259] For example, suppose User A places his or her smartphone in a car mount and begins driving. The device immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, it uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, that data and User A's emotional data are sent to the server. The server uses generative AI to analyze the collected data and determine that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[1260] In this way, by using the system of the present invention, drivers can gain a detailed understanding of their own driving behavior and emotional state, and can make efforts to drive safely based on specific suggestions for improvement.

[1261] The processing flow will be explained below.

[1262] Step 1:

[1263] As soon as the smartphone is placed in the car mount, the device initializes sensors such as the GPS, accelerometer, and gyroscope, preparing to acquire location information, speed, acceleration, direction, and other information while driving.

[1264] Step 2:

[1265] The device begins collecting data from sensors in real time, such as GPS to record the vehicle's position and speed every second, an accelerometer to record acceleration and deceleration to 0.1 second intervals, and a gyroscope to record turns and vehicle rotations to 0.1 second intervals.

[1266] Step 3:

[1267] The device uses a built-in camera and microphone to analyze the user's facial expressions and tone of voice with an emotion engine, and records the user's emotional data in real time. For example, if the user is surprised, the device will detect changes in their facial expressions and voice and record them as emotional data.

[1268] Step 4:

[1269] The device processes driving behavior data and emotion data in batches at regular intervals (for example, every minute) and sends them to the server. The SSL / TLS protocol is used for data transmission to ensure data security, making it possible to transmit data in real time.

[1270] Step 5:

[1271] The server performs preprocessing on the received data to immediately analyze it, including filling in missing data and removing noise, to prepare the data for analysis.

[1272] Step 6:

[1273] The server inputs the preprocessed data into the generation AI, which analyzes the driver's driving patterns and emotional state. The generation AI extracts information such as the frequency of sudden braking and speeding from the driving behavior data, and also identifies the user's stress and fatigue levels based on the emotional data.

[1274] Step 7:

[1275] Based on the analysis results, the server generates specific driving improvement suggestions for the driver. For example, for a driver who frequently brakes suddenly, the server generates specific advice such as "increase the distance between your vehicle and the vehicle in front." In addition, if the user is feeling stressed, the server generates suggestions such as "take appropriate breaks."

[1276] Step 8:

[1277] The server sends the generated improvement suggestions to the device via push notification. When the device receives the notification, it displays it to the user in real time. The user can check it and understand the improvements and stress reduction measures they need to take while driving.

[1278] Step 9:

[1279] After the drive is completed, the device presents the collected data and analysis results to the user in the form of graphs and charts, allowing the user to review their past driving history and emotional state and specifically check their own driving behavior and emotional state.

[1280] Step 10:

[1281] Based on the data and improvement suggestions displayed on the device, users can reflect on their own driving style and emotional state and understand what needs to be improved the next time they drive. For example, based on past driving history, the device can confirm information such as "You braked suddenly 10 times in one week, and felt high stress at those times," and provide a warning for the next time you drive.

[1282] Through the above steps, the system of the present invention can record and analyze the driver's driving behavior and emotional state in detail, and provide appropriate driving improvement suggestions and stress reduction measures in real time.

[1283] Example 2

[1284] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1285] Conventional driving management systems have difficulty recording detailed driver behavior in real time and providing specific improvement suggestions to promote safe driving. Furthermore, systems that analyze and provide feedback on users' emotional states are limited, leaving a lack of effective means to support the development of safe driving habits. There was also a need for a method to safely transmit collected driving and emotional data and notify users of specific improvement suggestions in real time based on the analysis results.

[1286] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1287] In this invention, the server includes a terminal means for recording the driver's driving behavior in real time, a means for recording speed, acceleration, direction, and braking operation using the terminal's sensors, a means for recognizing the user's emotions using the terminal's camera and microphone, a means for periodically transmitting the recorded driving behavior and emotional data to the server, a means for the server to analyze the received data using artificial intelligence to identify driving patterns and risk factors, a means for generating and notifying the user of appropriate driving improvement suggestions based on the analysis results, and a means for visually displaying the analysis results to the user after driving has ended to encourage reflection. This makes it possible to record the driver's driving behavior and emotional state in detail and provide specific improvement suggestions based on the results.

[1288] "Driver's driving behavior" refers to a series of actions performed by a driver while driving, such as the vehicle's speed, acceleration, direction, and braking operation.

[1289] "Terminal" refers to a device for recording and transmitting driving behavior and emotional data in real time, and generally refers to a smartphone or tablet device.

[1290] "Sensors" are measurement devices, such as GPS, accelerometers, and gyroscopes, that measure and record the vehicle's driving behavior in real time.

[1291] The "camera" is an image capturing device for capturing images of the user's facial expressions and recognizing their emotions.

[1292] A "microphone" is a voice capture device that picks up the user's voice and analyzes their emotional state.

[1293] "Emotion data" is information about the user's emotional state, such as stress or fatigue, analyzed from their facial expressions and tone of voice.

[1294] The "server" is a computer system that receives data sent from the terminal and generates analysis and driving improvement suggestions.

[1295] "Generative AI" is an AI technology that analyzes large amounts of data to identify driving patterns and risk factors.

[1296] A "driving pattern" is a consistent driving tendency or habit identified from a driver's driving behavior.

[1297] A "risk factor" is a behavior or situation that could potentially pose a risk while driving.

[1298] "Driving improvement suggestions" are specific advice provided to drivers based on the analysis results to promote safe driving.

[1299] "Push notifications" is a technology that sends new information and notifications to users' devices in real time.

[1300] An "encrypted communication protocol" is a communication method that encrypts information to ensure secure data transmission and reception.

[1301] "Driving history" refers to the accumulation of driving behavior data and analysis results recorded in the past.

[1302] "Charts and graphs" are diagrams that visually display information and are a means of presenting driving and emotional data in an easily understandable format.

[1303] This invention is a system for promoting safe driving by recording and analyzing the driving behavior of drivers in detail. This system includes a terminal that records driving behavior in real time, a server that creates improvement suggestions based on the analysis results, a means for providing feedback to the user at appropriate times, and an emotion engine that recognizes the user's emotions.

[1304] System Configuration

[1305] 1. Device (smartphone)

[1306] The device used is a smartphone. The smartphone is equipped with built-in sensors such as a GPS, accelerometer, and gyroscope, which are used to record driving behavior in real time. The smartphone also uses a camera and microphone to recognize the user's emotions. The device is placed in an in-car holder, and data collection begins as soon as the driver starts driving.

[1307] 2. Server

[1308] Data recorded while driving is periodically sent to a server. The server receives this data and analyzes it using a generative AI model. Specifically, it recognizes driving patterns and identifies risk factors, and generates specific improvement proposals based on the analysis results. Emotion data obtained by the emotion engine is also used in the analysis.

[1309] 3. Means of notification

[1310] The server sends the generated improvement suggestions to the device, which then notifies the user. Notifications are provided to the user in real time using push notifications. Even after driving is complete, the system visually displays the collected data and analysis results, making it easy for users to reflect on their own driving behavior.

[1311] Specific examples of program processing

[1312] 1. Recording driving behavior and emotions

[1313] The device uses smartphone sensors to record the vehicle's speed, acceleration, direction, braking, and other data in real time. It also uses data from the built-in camera and microphone to analyze the user's facial expressions and tone of voice, and records emotional data using an emotion engine. For example, if the device determines that the user is feeling stressed, that data will be recorded along with driving behavior data.

[1314] 2. Data submission and analysis

[1315] Data collected during driving is sent to a server at regular intervals. The server analyzes the received data and uses a generative AI model to recognize the driver's driving patterns. This analysis identifies the number of sudden braking attempts and the frequency of speeding, as well as the user's stress and fatigue levels based on emotional data.

[1316] 3. Generation and notification of driving improvement suggestions

[1317] The server generates appropriate driving improvement suggestions based on the analysis results. For example, for a driver who frequently brakes suddenly, it generates specific advice such as "increase the distance between your vehicle and the vehicle in front" and sends it to the device as a push notification. In addition, if the user is feeling stressed, it also generates suggestions such as "take appropriate breaks." The device receives these and displays them to the user.

[1318] 4. Reflection and Improvement

[1319] After the drive is completed, the device displays the collected data and analysis results in the form of graphs and charts to the user. This allows the user to reflect on their driving style and understand areas for improvement for the next drive. For example, based on past driving history, information such as "10 sudden braking attempts in one week" can be displayed, encouraging more caution the next time the user drives. In addition, the device displays the user's stress level and fatigue state based on emotional data, allowing the user to develop appropriate driving habits.

[1320] Prompt Sentence Examples

[1321] "Analyze the driver's driving patterns and generate specific improvement suggestions based on the number of sudden braking and speeding. Also, use emotional data to determine if the user is stressed and suggest appropriate breaks."

[1322] In this way, the system of the present invention allows drivers to understand their own driving behavior and emotional state in detail, and enables them to focus on safe driving based on specific suggestions for improvement.

[1323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1324] System program processing flow

[1325] Step 1:

[1326] The device activates the smartphone's sensors and records driving behavior.

[1327] Input: GPS, accelerometer, gyroscope data

[1328] Data processing: Real-time acquisition of speed, acceleration, direction, and braking data

[1329] Output: Driving behavior data

[1330] How it works: When a user starts the car engine and places the smartphone in the car mount, the device automatically activates the GPS, accelerometer, gyroscope, etc. This starts recording the car's speed, acceleration, and direction in real time, as well as data on when braking occurs.

[1331] Step 2:

[1332] The device activates the camera and microphone to record the user's emotions.

[1333] Input: Camera video, microphone audio

[1334] Data processing: Analyzing emotional states from facial expressions and tone of voice

[1335] Output: Emotion data

[1336] Specific operation: The device takes a picture of the user's face using the smartphone camera and collects their voice using the microphone. The emotion engine analyzes this data and records the user's emotional state (e.g., stress, fatigue) in real time.

[1337] Step 3:

[1338] The terminal transmits the recorded driving behavior data and emotion data to a server.

[1339] Input: driving behavior data, emotion data

[1340] Data processing: compressing data and preparing it for transmission

[1341] Output: Data sent to the server

[1342] Specific operation: At regular intervals, the terminal compresses the data, checks for errors, and then sends it to the server via the Internet.

[1343] Step 4:

[1344] The server analyzes the received data.

[1345] Input: driving behavior data, emotion data

[1346] Data Computation: Using generative AI models to identify driving patterns and risk factors

[1347] Output: Analysis results (driving patterns, risk factors)

[1348] How it works: The server adds the received data to an analysis queue and analyzes it using a generative AI model, which identifies the number of sudden braking attempts and speeding trends, and analyzes the user's emotional state to determine stress and fatigue levels.

[1349] Step 5:

[1350] The server generates driving improvement suggestions based on the analysis results.

[1351] Input: Analysis results

[1352] Data processing: generating concrete improvement proposals

[1353] Output: Driving improvement suggestions

[1354] Specific operation: Based on the analysis results, the server generates advice such as "increase the distance between your vehicle and the vehicle in front," and if stress is high, makes suggestions such as "take appropriate breaks."

[1355] Step 6:

[1356] The server generates driving improvement suggestions and sends them to the terminal.

[1357] Input: Driving improvement suggestions

[1358] Data processing: Preparing the proposed data for sending

[1359] Output: Proposal data sent to the device

[1360] Specific operation: The server checks the proposed data for errors and sends it to the device as a push notification. The device receives it and notifies the user in real time.

[1361] Step 7:

[1362] The device visually displays the collected data after the drive is completed.

[1363] Input: driving behavior data, emotion data, analysis results

[1364] Data processing: Convert data into graphs and charts

[1365] Output: Visual display

[1366] Specific operation: After the user finishes driving, the device analyzes the collected data and presents it to the user in the form of visual graphs and charts. For example, it may display information such as "10 sudden braking attempts" to encourage caution the next time the user drives. It also displays emotional data to help users understand their stress level and fatigue state.

[1367] Through each step, the system records and analyzes the driver's driving behavior and emotional state in detail, and provides specific improvement suggestions to promote safe driving.

[1368] (Application example 2)

[1369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1370] In recent years, systems have been developed to record and analyze driving behavior in real time in order to promote safe driving, but these systems focus only on the driver's driving behavior and do not take into account the emotional state or fatigue level of the driver or passengers. This poses the challenge of making it difficult to develop individual, specific measures to reduce the impact of emotional state and fatigue level on driving.

[1371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1372] In this invention, the server includes a means for recognizing and recording emotional data of the user while driving, a means for complementing the analysis results based on the emotional data and generating more accurate driving improvement suggestions, and a means for generating suggestions for driving habits such as taking breaks based on stress and fatigue levels, thereby enabling safe driving support that takes into account the emotional state and fatigue level.

[1373] "Driving behavior" refers to driver actions such as vehicle speed, acceleration, braking, and steering.

[1374] "Real-time" means that all operations and situations during driving are immediately recorded and analyzed and feedback is provided almost simultaneously.

[1375] "Recording means" refers to a device or system that uses sensors or devices to collect and store driving behavior and emotional data.

[1376] "Generative artificial intelligence" refers to machine learning models and data processing algorithms that analyze driving behavior data, recognize patterns, and generate suggestions.

[1377] "Means for analysis" refers to software or hardware that evaluates collected driving behavior data and emotion data and performs computational processing to identify risk factors and areas for improvement.

[1378] "Means of notification" refers to communication methods, such as push notifications or display functions on smartphones, used to inform users of analysis results and suggestions.

[1379] "Visual display means" refers to the display or software that presents the analysis results to the user in the form of graphs, charts, etc.

[1380] "Emotional data" refers to data that indicates a user's emotional or psychological state, derived from facial expressions or tone of voice.

[1381] "Complementary means" refers to methods and systems that use emotion data to further refine the analysis results of driving behavior data and improve the accuracy of suggestions.

[1382] "Driving improvement suggestions" refer to specific advice and instructions to drivers to help them review their driving behavior and encourage safe driving.

[1383] "Rest Suggestion" refers to instructions that encourage drivers and passengers to take appropriate rest breaks if they feel stressed or fatigued while driving.

[1384] This invention is a system for recording and analyzing the driving behavior and emotional state of drivers and passengers to promote safe driving. The system consists of a smartphone, sensors, a camera, a microphone, a server, and a generative AI model.

[1385] Hardware and Software Configuration

[1386] 1. Smartphone

[1387] Smartphones are equipped with built-in sensors such as GPS, accelerometer, gyroscope, camera, and microphone.

[1388] The smartphone is placed in a car holder and data collection begins as soon as the driver starts driving.

[1389] 2. Emotion Engine

[1390] Using data from the built-in camera and microphone, the device analyzes the user's facial expressions and tone of voice to record emotional data.

[1391] The emotion engine uses machine learning algorithms to recognize emotional states in real time.

[1392] 3. Server

[1393] Receives driving behavior data and emotion data periodically sent from a smartphone.

[1394] A generative AI model is used to analyze incoming data, recognize driving patterns, and identify risk factors.

[1395] Based on the analysis results, specific driving improvement suggestions are generated and sent to a smartphone.

[1396] 4. Means of notification

[1397] The smartphone receives the improvement suggestions sent from the server and notifies the user via push notification.

[1398] Even after driving is complete, the collected data and analysis results are visually displayed, making it easier for users to reflect on their own driving behavior.

[1399] A natural language description of the program's processing

[1400] The server first receives driving data sent from the smartphone. This data includes driving behavior data such as speed, acceleration, direction, and braking, as well as emotion data including facial expressions and tone of voice collected by the emotion engine. The server then analyzes this data using a generative AI model.

[1401] The analysis identifies the number of sudden braking and speeding, and also evaluates the user's stress and fatigue state based on emotional data. This allows for more accurate driving improvement suggestions to be made. For example, if sudden braking occurs frequently, the system will generate a suggestion such as "increase the distance between your vehicle and the vehicle ahead," and if stress is high, it will suggest driving habits such as "take appropriate breaks."

[1402] Finally, the generated improvement suggestions are sent to a smartphone in real time via push notification. After the drive is completed, the smartphone displays the collected data and analysis results in charts and graphs, allowing the user to reflect on their driving behavior.

[1403] Examples of specific examples and prompts

[1404] Examples:

[1405] Suppose User A places his or her smartphone in a car mount and begins driving. The smartphone immediately activates its sensors and records data such as GPS information, speed, acceleration, and direction. At the same time, the smartphone uses a camera and microphone to collect User A's facial expressions and tone of voice, which are then analyzed by an emotion engine. If sudden braking is detected while driving, the data and User A's emotional data are sent to the server. The server analyzes the collected data using generative AI and determines that User A frequently brakes suddenly and is feeling stressed. The server then generates improvement suggestions, such as "increase the distance between you and the vehicle ahead to reduce sudden braking" and "take appropriate breaks as you are feeling stressed," and sends these suggestions to the device as push notifications. After finishing the drive, the device displays the week's driving history and emotional data in chart format, allowing User A to reflect on his or her driving behavior and emotional state and identify specific areas for improvement for the next drive.

[1406] Example prompt sentence:

[1407] "Driving and emotional data was collected as follows:

[1408] Hard braking: 5 times

[1409] Overspeeding: 2 times

[1410] Stress level: High

[1411] Based on this data, generate specific suggestions to encourage drivers to drive safely.”

[1412] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1413] Step 1:

[1414] Data collection

[1415] The device uses a smartphone placed in a car holder to collect driving behavior data from sensors such as GPS, accelerometer, and gyroscope, and also uses the built-in camera and microphone to capture the user's facial expressions and tone of voice, thereby collecting emotional data.

[1416] Input: Vehicle speed, acceleration, braking, steering, user facial expressions and tone of voice.

[1417] Output: Driving behavior data and emotion data.

[1418] Step 2:

[1419] Data transmission

[1420] The device periodically transmits the collected driving behavior data and emotion data to the server using an encrypted communication protocol.

[1421] Input: Driving behavior data and emotion data.

[1422] Output: Data securely sent to the server.

[1423] Step 3:

[1424] Data analysis

[1425] The server analyzes the received driving behavior data and emotional data. Using a generative AI model, it recognizes the driver's driving patterns, such as the number of sudden braking attempts and the frequency of speeding. It also evaluates the driver's stress and fatigue levels based on the emotional data.

[1426] Input: Submitted driving behavior data and emotion data.

[1427] Output: Driving pattern analysis, stress and fatigue assessment results.

[1428] Step 4:

[1429] Generate driving improvement suggestions

[1430] The server generates driving improvement suggestions for the user based on the analysis results. For example, if the driver brakes suddenly frequently, the server may suggest "increase the distance between your vehicle and the vehicle ahead," or if the driver is judged to be under high stress, the server may suggest "take appropriate breaks."

[1431] Input: Driving pattern analysis results, stress and fatigue assessment results.

[1432] Output: Specific driving improvement suggestions.

[1433] Step 5:

[1434] Feedback Notifications

[1435] The server sends the generated driving improvement suggestions to the terminal, and the terminal notifies the user of them in real time via push notification.

[1436] Input: Specific driving improvement suggestions.

[1437] Output: A push notification that arrives to the user.

[1438] Step 6:

[1439] Visual display of results

[1440] After the drive, the server visually displays the collected driving data and emotional data on the device as charts and graphs, allowing the user to reflect on their own driving behavior and emotional state.

[1441] Input: Driving data and emotion data until the end of the drive.

[1442] Output: Charts and graphs that are visually displayed to the user.

[1443] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1444] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1445] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1446] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1447] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1448] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1449] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1450] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1451] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1452] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1453] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1454] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1455] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1456] 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.

[1457] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1458] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1459] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1460] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1461] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1462] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1463] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1464] The following is further disclosed regarding the above embodiment.

[1465] (Claim 1)

[1466] a means for recording the driving behavior of the driver in real time;

[1467] means for analyzing the recorded driving behavior data by generating artificial intelligence;

[1468] A means of notifying the driver of driving improvement suggestions based on the analysis results;

[1469] A method to visually display the analysis results to the user after driving to encourage reflection,

[1470] A system including:

[1471] (Claim 2)

[1472] a means for periodically transmitting the collected driving behavior data to a server;

[1473] A means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results;

[1474] A means of identifying operational risk factors and generating specific improvement recommendations;

[1475] 10. The system of claim 1, comprising:

[1476] (Claim 3)

[1477] a means for sending the generated driving improvement suggestions to the user by push notification;

[1478] a means for using an encrypted communication protocol for securely transmitting the driving data;

[1479] means for displaying the user's driving history as a chart or graph after the driving is completed;

[1480] 10. The system of claim 1, comprising:

[1481] "Example 1"

[1482] (Claim 1)

[1483] a means for recording driving behavior in real time using a terminal installed in a transportation vehicle in which a user rides;

[1484] a means for temporarily storing the recorded driving behavior data;

[1485] means for periodically transmitting the stored driving behavior data to a server;

[1486] A means for analyzing the driving behavior data received by the server using a generative artificial intelligence model;

[1487] A means for generating specific operational improvement proposals based on the analysis results;

[1488] a means for transmitting the generated driving improvement proposal to a terminal and notifying the user in the form of a push notification;

[1489] After driving, the collected driving data and analysis results are visually displayed to the user as graphs and charts to encourage reflection.

[1490] A system including:

[1491] (Claim 2)

[1492] 2. The system according to claim 1, wherein the driving behavior data is compiled into packets at specific times during driving and transmitted to the server.

[1493] (Claim 3)

[1494] 2. The system according to claim 1, wherein the received driving behavior data is converted into a format suitable for analysis and input into a generative artificial intelligence model.

[1495] "Application Example 1"

[1496] (Claim 1)

[1497] a means for recording the driving behavior of the driver in real time;

[1498] means for analyzing the recorded driving behavior data by generating artificial intelligence;

[1499] A means of notifying the driver of driving improvement suggestions based on the analysis results;

[1500] A method to visually display the analysis results to the user after driving to encourage reflection,

[1501] A means for recording the work behavior of a factory robot in real time;

[1502] means for analyzing the recorded work behavior data by artificial intelligence to generate the data;

[1503] A means of notifying suggestions for work improvement based on the analysis results;

[1504] A method to visually display the analysis results to the user after completing the work and encourage reflection,

[1505] A system including:

[1506] (Claim 2)

[1507] a means for periodically transmitting the collected driving behavior data to a server;

[1508] A means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results;

[1509] A means of identifying operational risk factors and generating specific improvement recommendations;

[1510] a means for periodically transmitting the collected work behavior data to a server;

[1511] A means for the server to analyze the work behavior data and create work improvement proposals based on the analysis results;

[1512] A means of identifying operational risk factors and generating specific improvement proposals;

[1513] 10. The system of claim 1, comprising:

[1514] (Claim 3)

[1515] a means for sending the generated driving improvement suggestions to the user by push notification;

[1516] a means for using an encrypted communication protocol for securely transmitting the driving data;

[1517] means for displaying the user's driving history as a chart or graph after the driving is completed;

[1518] a means for sending the generated work improvement proposal to the user by push notification;

[1519] means for using an encrypted communication protocol for securely transmitting work data;

[1520] means for displaying the user's work history as charts or graphs after the work is completed;

[1521] 10. The system of claim 1, comprising:

[1522] "Example 2: Combining Emotion Engines"

[1523] (Claim 1)

[1524] a terminal means for recording the driving behavior of the driver in real time;

[1525] a means for recording speed, acceleration, direction, and braking operation using sensors on the device;

[1526] means for recognizing a user's emotion using a camera and a microphone of the terminal;

[1527] means for periodically transmitting the recorded driving behavior and emotion data to a server;

[1528] A means for the server to analyze the received data using artificial intelligence to identify driving patterns and risk factors;

[1529] A means for generating and notifying appropriate operational improvement proposals based on the analysis results;

[1530] A method to visually display the analysis results to the user after driving to encourage reflection,

[1531] A system including:

[1532] (Claim 2)

[1533] a means for sending the generated driving improvement suggestions to the user by push notification;

[1534] means for securely transmitting driving data and emotional data using an encrypted communication protocol;

[1535] means for displaying the user's driving history and emotion data as charts or graphs after the driving is completed;

[1536] 10. The system of claim 1, comprising:

[1537] (Claim 3)

[1538] When a driver suddenly brakes or exceeds the speed limit, the system analyzes that data and emotional data, and generates specific improvement proposals in real time.

[1539] A means of analyzing the user's stress level and fatigue state while driving and suggesting appropriate breaks;

[1540] A means for displaying the analyzed driving history and emotion data so that the user can self-evaluate and use it for the next driving.

[1541] 10. The system of claim 1, comprising:

[1542] "Application example 2 when combining emotion engines"

[1543] (Claim 1)

[1544] a means for recording the driving behavior of the driver in real time;

[1545] A means for analyzing the recorded driving behavior data by a generating artificial intelligence;

[1546] A means of notifying the driver of driving improvement suggestions based on the analysis results;

[1547] After the operation is completed, the analysis results are visually displayed to the user to encourage reflection.

[1548] means for recognizing and recording user emotion data while driving;

[1549] A means to complement the analysis results based on emotion data and generate more accurate driving improvement suggestions;

[1550] A system including:

[1551] (Claim 2)

[1552] a means for periodically transmitting the collected driving behavior data to a server;

[1553] A means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results;

[1554] A means of identifying operational risk factors and generating specific improvement recommendations;

[1555] means for generating driving habit suggestions, such as rest breaks, based on stress and fatigue states;

[1556] 10. The system of claim 1, comprising:

[1557] (Claim 3)

[1558] A means for sending the generated driving improvement suggestions to the user by push notification;

[1559] a means for using an encrypted communication protocol for securely transmitting the driving data;

[1560] a means for displaying the driving history including the user's emotional data as a chart or graph after the driving is completed;

[1561] 10. The system of claim 1, comprising: [Explanation of symbols]

[1562] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for recording the driving behavior of the driver in real time; means for analyzing the recorded driving behavior data by generating artificial intelligence; A means of notifying the driver of driving improvement suggestions based on the analysis results; A method to visually display the analysis results to the user after driving to encourage reflection, A system including:

2. a means for periodically transmitting the collected driving behavior data to a server; A means for the server to analyze the driving behavior data and create driving improvement proposals based on the analysis results; A means of identifying operational risk factors and generating specific improvement recommendations; The system of claim 1 , comprising:

3. a means for sending the generated driving improvement suggestions to the user by push notification; a means for using an encrypted communication protocol for securely transmitting the driving data; means for displaying the user's driving history as a chart or graph after the driving is completed; The system of claim 1 , comprising:

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