System

Abstract

A system is provided.SOLUTION: A system, comprising: means for collecting driving data; means for transmitting the collected driving data to a server; means for analyzing the driving data received at the server and generating driving safety tips and personalized driving improvements; means for transmitting the generated feedback to a terminal; and means for displaying the feedback to a user at the terminal.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] Modern drivers face difficulties in obtaining objective feedback on safe driving and fuel efficiency, making it difficult for them to improve their driving skills. While utilizing structured driving data could potentially enable discounts on automobile insurance grades, the lack of an appropriate system for this remains a challenge. [Means for solving the problem]

[0005] The present invention is a system including a means for collecting driving data, a means for transmitting the collected driving data to a server, a means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, a means for transmitting the generated feedback to a terminal, and a means for displaying the feedback to a user on the terminal. The analysis means uses an AI model to identify driving patterns, and the terminal displays safe driving tips and driving improvement points as visual graphs or text messages, allowing drivers to easily improve their driving techniques and fuel efficiency. The collected data may also be used to qualify for discounts on car insurance grades.

[0006] "Driving data" is a collection of information that indicates driving-related actions and conditions such as speed, acceleration, braking, and handling.

[0007] "Collection means" means a device or system for collecting driving data using sensors or devices attached to a vehicle.

[0008] "Transmitting means" refers to a device or system for transmitting collected driving data to a server.

[0009] "Server" means a central processing unit for receiving, storing, and analyzing driving data.

[0010] "Analysis means" refers to a device or program that analyzes the received driving data and generates safe driving tips and individual driving improvements.

[0011] The "generating means" is a device or program for generating feedback based on the analysis results.

[0012] "Feedback" refers to safe driving tips and information on individual driving improvement points provided based on the analysis of driving data.

[0013] A "display means" is a device or system for displaying feedback to a user.

[0014] An "AI model" is an algorithm or program that uses machine learning and artificial intelligence technology to analyze data and identify driving patterns. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. This system is composed of sensors attached to the vehicle and a user terminal. The following describes in detail the embodiments of the present invention.

[0037] Data collection

[0038] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[0039] Data transmission

[0040] The devices transmit data collected from the sensors to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[0041] Data reception and storage

[0042] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[0043] Data analysis

[0044] The server uses AI models to analyze the received driving data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors.

[0045] Feedback Generation

[0046] Based on the analysis results, the server generates feedback such as safe driving tips and individual driving improvement points. The generated feedback is based on templates and includes specific, actionable advice.

[0047] Send Feedback

[0048] The generated feedback is sent from the server to the user's device via push notification or email.

[0049] Feedback Display

[0050] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific ways to improve their driving.

[0051] Specific examples

[0052] Here, we will explain a specific example in which a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by sensors and sent from the device to the server. The server analyzes this data and determines that sudden acceleration is having a negative impact on fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[0053] In this way, the present invention allows users to improve their driving skills in real time and increase fuel efficiency. In addition, the collected data can be used in cooperation with insurance companies to receive discounts on car insurance premiums.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[0057] Step 2:

[0058] The device transmits the buffered data to the server at regular intervals. Data transmission is performed in real time, and data confidentiality and integrity are ensured using a secure communication protocol (e.g., HTTPS).

[0059] Step 3:

[0060] The server receives the data sent from the terminal, stores the received data in a database, and adjusts the data format as necessary.

[0061] Step 4:

[0062] The server uses a generative AI model to analyze the stored data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and identify specific unsafe behaviors (e.g., sudden acceleration, hard braking, sharp turns).

[0063] Step 5:

[0064] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The feedback content is based on templates and includes specific, actionable advice.

[0065] Step 6:

[0066] The server sends the generated feedback to the user's device, and the feedback is delivered reliably via push notification or email.

[0067] Step 7:

[0068] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[0069] Example 1

[0070] 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."

[0071] Current vehicle driving data collection and analysis systems lack the ability to collect driving data in real time and provide effective feedback. Furthermore, they do not provide specific advice for driving improvement, preventing users from appropriately improving their driving skills. Furthermore, there is a need for systems that can efficiently analyze collected data and provide feedback to users while ensuring the confidentiality and integrity of the data.

[0072] 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.

[0073] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to a communication terminal, means for transmitting the driving data from the communication terminal to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the communication terminal, and means for displaying the feedback to the user on the communication terminal, thereby making it possible to collect driving data in real time, efficiently analyze it, and provide specific feedback for driving improvement.

[0074] "Driving data" is a general term for various data related to the driving state of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[0075] A "communication terminal" is a device used to transmit data collected from sensors installed in a vehicle to a server, and generally includes smartphones, tablets, in-vehicle computers, etc.

[0076] The "server" is a computer system that receives driving data transmitted from the communication terminal, analyzes the data, and generates feedback.

[0077] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to analyze driving data, identify driving patterns, and pinpoint specific unsafe behaviors.

[0078] "Feedback" refers to specific advice on improving driving and hints for safe driving that are provided to the user based on the results of analyzing driving data.

[0079] "Visual graphs" are data display formats such as bar graphs, line graphs, and histograms that visually show operational data and analytical results.

[0080] A "text message" is a written message that provides analysis results and feedback to the user in text format.

[0081] A "driving pattern" indicates a tendency or characteristic of driving data under a specific time or situation, and includes, for example, actions such as sudden acceleration or sudden braking.

[0082] This system collects and analyzes vehicle driving data in real time, providing users with tips for safe driving and individual driving improvements. The system consists of sensors installed in the vehicle, a communication terminal for sending and receiving data, and a server for analyzing the data.

[0083] Data collection

[0084] First, driving data is collected by sensors attached to the user's vehicle. These sensors are connected to the vehicle's OBD-II port and capture real-time data such as speed, acceleration, braking, and handling. This data is then transmitted to a communication device via Bluetooth or Wi-Fi.

[0085] Data transmission

[0086] The communication terminal transmits the driving data collected from the sensors to the server using the HTTPS protocol, with SSL / TLS encryption applied to ensure data confidentiality and integrity.

[0087] Data reception and storage

[0088] The server receives the data sent from the communication terminal and temporarily stores it in its memory. The data is received in JSON format, and is then converted into an appropriate format and saved in a database (MySQL, PostgreSQL, etc.).

[0089] Data analysis

[0090] The server then analyzes the received driving data using a generative AI model to identify driving patterns and pinpoint specific unsafe behaviors (e.g., sudden acceleration, hard braking). This analysis uses machine learning frameworks such as TensorFlow and PyTorch.

[0091] Feedback Generation

[0092] Based on the analysis results, the server generates feedback, including tips for safe driving and individual driving improvements. The generated feedback is based on templates and includes specific, actionable advice. For example, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy."

[0093] Send Feedback

[0094] The generated feedback is sent from the server to the communication device via push notification, email, or other methods. The communication protocol used is Firebase Cloud Messaging (FCM) or the SMTP protocol.

[0095] Feedback Display

[0096] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to intuitively understand specific ways to improve their driving. For example, a graph showing driving improvement points or a text message such as "Number of sudden accelerations: 5 times / day" is displayed.

[0097] Specific examples

[0098] For example, consider a case where a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by a sensor and sent from the communication device to the server. The server analyzes this data and determines that sudden acceleration is negatively impacting fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the communication device via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[0099] Prompt Sentence Examples

[0100] Below are some example prompts for a generative AI model to analyze driving data and generate safe driving tips.

[0101] "We will provide you with your driving data in the following format to generate feedback for safe driving.

[0102] Speed: 50km / h

[0103] Acceleration: 3 m / s^2

[0104] Braking: 4 m / s^2

[0105] Handling: Left turn

[0106] Example of feedback to generate: "Next time you start at a traffic light, accelerate slowly over a 3-second period to improve fuel economy."

[0107] In this way, the present invention allows users to collect driving data in real time and receive specific feedback for improving their driving by analyzing it efficiently.

[0108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0109] Step 1: Data collection

[0110] Sensors installed in the user's vehicle collect driving data, specifically speed, acceleration, braking, handling, and other data in real time, which is then transmitted to a communication device via Bluetooth.

[0111] Input: User's vehicle driving data

[0112] Output: Operation data sent to the communication terminal

[0113] Specific operation: The user drives the vehicle, and the sensor detects when the vehicle accelerates to 60 km / h and records this as acceleration data.

[0114] Step 2: Send data

[0115] The device transmits driving data collected from sensors to the server using a secure protocol (HTTPS), encrypted with SSL / TLS to ensure data integrity and confidentiality.

[0116] Input: Driving data sent to the terminal

[0117] Output: Driving data sent to the server

[0118] Specific operation: The terminal collects data and sends it to the server once per second in packets.

[0119] Step 3: Receiving and storing data

[0120] The server receives the data sent from the device, temporarily stores it in memory, then formats the data in JSON format appropriately and saves it to a database.

[0121] Input: Driving data sent to the server

[0122] Output: Operation data stored in the database

[0123] Specific operation: The server parses the received JSON data and inserts speed data, acceleration data, braking data, etc. into the corresponding columns in the database.

[0124] Step 4: Data analysis

[0125] The server analyzes the driving data stored in the database using a generative AI model. Specifically, it uses TensorFlow and PyTorch to analyze driving patterns and identify behaviors that pose safety issues.

[0126] Input: Operation data stored in the database

[0127] Output: Analysis results of driving patterns

[0128] How it works: The server inputs driving data into the AI ​​model and detects patterns of sudden acceleration. For example, it identifies that sudden acceleration is particularly common during rush hour on Mondays.

[0129] Step 5: Feedback generation

[0130] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback includes specific and actionable advice based on templates.

[0131] Input: Analysis results of driving patterns

[0132] Output: Generated feedback

[0133] Specific behavior: Feedback such as "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy" is generated.

[0134] Step 6: Send your feedback

[0135] The generated feedback is sent from the server to the communication device, and is delivered to the user's device via push notification or email.

[0136] Input: Generated feedback

[0137] Output: Feedback sent to the communication device

[0138] Specific operation: The feedback generated by the server is sent to the device as a push notification, which displays the message, "You can expect to improve fuel efficiency by refraining from sudden acceleration when starting from a traffic light."

[0139] Step 7: Feedback display

[0140] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to review the feedback and understand specific ways to improve their driving.

[0141] Input: Feedback sent to communication terminal

[0142] Output: Feedback displayed as visual graphs and text messages

[0143] Specific operation: The device receives the feedback and displays a text message with a graph on the app screen, such as "Number of sudden accelerations: 5 times / day."

[0144] (Application example 1)

[0145] 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."

[0146] Although conventional driving data analysis systems exist that collect and analyze driving data to provide individual driving improvement points, it is difficult to support special vehicles such as self-driving vehicles. Furthermore, these systems were unable to provide real-time feedback or monitor the driving data of self-driving vehicles, which meant that improvements in driving safety and efficiency were not fully achieved.

[0147] 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.

[0148] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, means for displaying the feedback to the user on the terminal, and means for monitoring the driving data of the autonomous vehicle and providing feedback in real time, thereby enabling the user to monitor the driving data of the autonomous vehicle in real time and immediately receive safe driving tips and improvement points.

[0149] "Driving data" refers to various data relating to the driving state of the vehicle, such as the vehicle's speed, acceleration, braking, and handling.

[0150] An "autonomous vehicle" is a vehicle that can be driven automatically using dedicated hardware and software.

[0151] "Monitoring" is the act of continuously observing data in real time and detecting anomalies or specific patterns.

[0152] "Feedback" refers to hints and specific advice for improving driving that are generated based on the analysis results.

[0153] A "terminal" is a device that receives feedback and displays it to the user, such as a smartphone or tablet.

[0154] The "server" is a centralized management system for receiving, storing, analyzing, and transmitting the results of driving data.

[0155] "Analysis" is the process of deriving safe driving tips and areas for improvement based on collected driving data.

[0156] An "AI model" is an artificial intelligence technology algorithm that learns from large amounts of driving data and identifies and analyzes driving patterns.

[0157] "Real time" refers to a situation where processing and feedback are provided the moment an event occurs.

[0158] "Graph or text message" refers to a visual representation or textual information in a format that presents analysis results or feedback to a user.

[0159] This invention relates to a specific system for collecting driving data and providing users with safe driving tips and individual driving improvement points in real time. This system is composed of various sensors installed in vehicles, a terminal held by the user, and a server that analyzes the data.

[0160] Data collection

[0161] First, various sensors installed in the vehicle collect driving data in real time, including speed sensors, acceleration sensors, braking sensors, and handling sensors, which continuously record various data points generated while driving.

[0162] Data transmission

[0163] The driving data collected by the sensors is then transmitted to a server via the user's device (e.g., a smartphone), using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[0164] Data reception and storage

[0165] The server receives the driving data sent from the terminal and stores it in a database, where it is properly formatted before analysis to ensure data consistency.

[0166] Data analysis

[0167] The server uses machine learning and artificial intelligence (AI) models to analyze the stored driving data. These AI models identify driving patterns and identify unsafe driving behaviors and specific areas for improvement. Based on these analysis results, the server uses templates to generate specific, actionable feedback.

[0168] Feedback generation and submission

[0169] The generated feedback is instantly sent to the user's device, and includes specific tips for safe driving and specific areas for individual driving improvement.

[0170] Feedback Display

[0171] The user's device (such as a smartphone or tablet) displays the submitted feedback as visual graphs and text messages, allowing the user to easily understand specific ways to improve their driving.

[0172] Specific examples

[0173] As a concrete example, consider a situation where a user is driving an autonomous vehicle. For example, if the vehicle accelerates more than normal when going around a curve, this data is collected by sensors and sent to a server. The server analyzes this data and generates feedback suggesting that the vehicle handle the curve more gently. This feedback, such as "The next time you come around a curve, steering more gently will improve safety," is sent to the user's device as a push notification.

[0174] Prompt Sentence Examples

[0175] To give an example to the generative AI model, we use the following prompt:

[0176] Generate feedback like, "When the user was driving around a curve, the acceleration was too high, so next time you drive, you can improve safety by handling the curve more gently."

[0177] The present invention aims to promote safe driving and improve driving efficiency by monitoring driving data of an autonomous vehicle in real time and providing immediate feedback to the user.

[0178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0179] Step 1:

[0180] Sensors installed in the user's vehicle collect driving data (speed, acceleration, braking, handling, etc.) in real time. Specifically, the various sensors detect data and format it for transmission to the user's device. The input is raw data from the sensors, and the output is formatted driving data.

[0181] Step 2:

[0182] The terminal transmits the collected driving data to the server using a secure communication protocol (e.g., HTTPS). In specific operations, the terminal receives the formatted data and transmits it to the server via the Internet. The input is the formatted driving data, and the output is the driving data transmitted to the server.

[0183] Step 3:

[0184] The server receives the driving data sent from the terminal and stores it in an internal database. Specifically, the server converts the received data into a format for proper analysis and writes it to the database. The input is the driving data sent to the server, and the output is the data stored in the database.

[0185] Step 4:

[0186] The server uses AI models to analyze the stored driving data. Specifically, the server applies machine learning algorithms to identify driving patterns and identify specific unsafe behaviors and areas for improvement. The input is the driving data stored in the database, and the output is the analyzed feedback information.

[0187] Step 5:

[0188] The server generates specific feedback based on the analysis results. Specifically, the server uses templates to generate text containing safe driving tips and driving improvement points. In this process, a generative AI model is used to create appropriate feedback according to the driving scenario. The input is the analysis results, and the output is the generated feedback.

[0189] Step 6:

[0190] The server sends the generated feedback to the user's device. Specifically, the server sends the feedback to the device by push notification or email. The input is the generated feedback, and the output is the feedback sent to the user's device.

[0191] Step 7:

[0192] The terminal receives the sent feedback and displays it to the user. Specifically, the terminal displays the feedback as a visual graph or a text message. The input is the feedback from the server, and the output is the feedback displayed to the user.

[0193] Step 8:

[0194] The user checks the displayed feedback and practices improvements to their driving. Specifically, the user understands the feedback and incorporates the safe driving tips and improvements into their next drive. The input is the feedback displayed on the device, and the output is the user's driving improvements.

[0195] In this way, by clarifying the specific operations performed at each processing step and their inputs and outputs, the flow of the entire system becomes easier to understand and implementation becomes easier.

[0196] 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.

[0197] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced feedback. This system is composed of sensors attached to the vehicle, a user's terminal, and an emotion engine. The following describes in detail the embodiments of the present invention.

[0198] Data collection

[0199] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[0200] Emotional Data Collection

[0201] Furthermore, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This data is also sent to the terminal in real time, just like the driving data.

[0202] Data transmission

[0203] The device transmits data collected from sensors and the emotion engine to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[0204] Data reception and storage

[0205] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[0206] Data analysis

[0207] The server uses AI models to analyze the received driving and emotional data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors and emotional states.

[0208] Feedback Generation

[0209] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback is adjusted to provide appropriate wording and advice based on the user's emotional state. For example, if the user is feeling stressed, the server will provide gentle, encouraging feedback.

[0210] Send Feedback

[0211] The generated feedback is sent from the server to the user's device via push notification or email.

[0212] Feedback Display

[0213] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific driving improvement methods and tips based on their emotional state.

[0214] Specific examples

[0215] As a concrete example, let us consider a case where a user uses this system during their commute. Data on the user's sudden acceleration at a traffic light is collected by a sensor and sent from the device to the server. At the same time, the emotion engine analyzes the user's facial expressions and recognizes that the user is feeling stressed. The server analyzes the driving data and emotion data, determines that the sudden acceleration is negatively affecting fuel economy, and generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music while driving." The server then sends this feedback to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills, fuel efficiency, and stress reduction.

[0216] In this way, this invention allows users to improve their driving skills in real time and increase fuel efficiency. Furthermore, the collected data can be used in cooperation with insurance companies to provide discounts on car insurance grades. Providing feedback that takes into account the user's emotional state is expected to lead to more appropriate and effective driving improvements.

[0217] The processing flow will be explained below.

[0218] Step 1:

[0219] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[0220] Step 2:

[0221] The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice, generating emotion data, which is also sent to the device and stored in memory.

[0222] Step 3:

[0223] The device transmits the buffered driving and emotion data to a server at regular intervals. Data transmission is performed in real time, using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[0224] Step 4:

[0225] The server receives the driving and emotion data sent from the device, stores the data in a database, and formats it appropriately before analyzing it.

[0226] Step 5:

[0227] The server uses a generative AI model to analyze the stored driving and emotional data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and the user's emotional state.

[0228] Step 6:

[0229] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The generated feedback is adjusted to the appropriate wording and advice content based on the user's emotional state.

[0230] Step 7:

[0231] The server sends the generated feedback to the user's device via push notification or email.

[0232] Step 8:

[0233] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[0234] Specific examples

[0235] As a specific example, a case where a user uses this system during his / her commute will be described.

[0236] Step 1:

[0237] The device collects speed and acceleration data when it suddenly accelerates at traffic lights.

[0238] Step 2:

[0239] The emotion engine analyzes the user's facial expressions and recognizes when the user is feeling stressed.

[0240] Step 3:

[0241] The device transmits driving data and emotion data to a server at regular intervals.

[0242] Step 4:

[0243] The server receives the transmitted driving data and emotion data and stores them in a database.

[0244] Step 5:

[0245] The server analyzes the driving data and emotional data and identifies that sudden acceleration is having a negative effect on fuel economy and that the user is feeling stressed.

[0246] Step 6:

[0247] The server generates feedback such as, "Next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be better to listen to relaxing music while driving."

[0248] Step 7:

[0249] The server sends the generated feedback to the user's smartphone via push notification.

[0250] Step 8:

[0251] The device displays the feedback on the smartphone and provides the user with specific tips on how to improve their driving and hints based on their emotions.

[0252] Example 2

[0253] 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."

[0254] Conventional driver assistance systems focus on collecting and analyzing driving data, but it is difficult to provide feedback that takes into account the driver's emotional state. As a result, they lack appropriate improvement advice based on the driver's actual attention state and stress, and are unable to maximize the effectiveness of safe driving. Furthermore, there is a lack of technological means to provide specific advice based on the driver's emotional state.

[0255] 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.

[0256] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for collecting emotion data, means for transmitting the collected emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide specific and effective safe driving advice according to the driver's emotional state by comprehensively analyzing both the driving data and the emotion data.

[0257] "Driving data" is a set of data relating to the driving of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[0258] A "server" is a computing system for receiving, storing, and analyzing data, and generating and sending feedback.

[0259] The "terminal" is a device that transmits data collected from sensors and emotion engines installed in the vehicle to a server and displays feedback to the user.

[0260] A "sensor" is a measuring device used to collect driving data such as vehicle speed, acceleration, braking, and handling.

[0261] The "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize emotional data.

[0262] "Emotion data" is data that represents the user's emotional state, and is information collected from facial expressions, tone of voice, and the like.

[0263] A "generative AI model" is an algorithm or model that uses machine learning and artificial intelligence techniques to analyze data and generate feedback based on that data.

[0264] "Feedback" refers to safe driving tips and individual driving improvement points generated as a result of analyzing driving data and emotional data.

[0265] The present invention is a system that collects and analyzes driving data and emotion data, and provides hints for safe driving and driving improvement points. Below, specific embodiments of the invention will be described in detail.

[0266] Hardware and software used

[0267] The system consists of sensors installed in the vehicle, a device used by the user, a server that analyzes and stores data, and an emotion engine that recognizes the vehicle's emotional state. The sensors used include speed sensors, acceleration sensors, brake sensors, and handling sensors. The emotion engine uses a facial recognition camera and a voice analysis microphone. A generative AI model is also used to analyze the data.

[0268] Data collection

[0269] Sensors attached to the device collect driving data, including speed, acceleration, braking, and handling.

[0270] At the same time, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This allows the user's emotional state while driving to be collected in real time.

[0271] Data transmission

[0272] The device transmits the collected driving and emotion data to a server in real time, using secure communication protocols such as TLS / SSL to ensure data confidentiality and integrity.

[0273] Data reception and storage

[0274] The server receives data sent from the terminal and stores it in a database. The received data is first stored in a buffer area and then stored in the database according to a predefined format.

[0275] Data analysis

[0276] The server analyzes the stored driving and emotional data, using machine learning algorithms and generative AI models to extract features from the data and identify specific driving patterns and emotional states, such as frequent sudden braking or stressful facial expressions.

[0277] Feedback Generation

[0278] The server generates feedback based on the analysis results. This feedback includes suggestions for improving the user's driving skills and tips for safe driving. The generative AI model takes into account the user's emotional state and provides appropriate language and advice. For example, it may generate feedback such as, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be a good idea to listen to relaxing music while driving."

[0279] Send Feedback

[0280] The server sends the generated feedback to the user's device via push notification or email. For example, Firebase Cloud Messaging (FCM) is used to send push notifications.

[0281] Feedback Display

[0282] The device displays the received feedback to the user as visual graphs or text messages, and the application provides the feedback to the user in summary or detailed views, such as a section titled "Driving Improvements" on the app's dashboard.

[0283] Specific examples

[0284] When a user uses this system during their commute, sensors collect data on sudden acceleration at traffic lights and send it from the device to the server. At the same time, an emotion engine analyzes the user's facial expressions and recognizes their stress level. The server then analyzes this data using an AI model to determine that sudden acceleration is negatively impacting fuel economy, and generates feedback such as, "Next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music." This feedback is sent to the user's smartphone in real time and displayed as a push notification. The user can review this feedback and put it into practice the next time they drive to improve their driving technique, fuel efficiency, and reduce stress.

[0285] Specific prompt examples:

[0286] "Based on the user's driving data and emotional data, please analyze their driving patterns and generate tips for safe driving and improving fuel efficiency. Please also include advice that takes into account the user's stress level."

[0287] The above is a specific embodiment of the present invention. This system allows users to improve their driving skills in real time and increase fuel efficiency. In addition, by providing appropriate feedback according to the user's emotional state, more effective driving assistance can be achieved.

[0288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0289] Step 1:

[0290] The device collects driving data from sensors installed in the vehicle. The input data is the vehicle's speed, acceleration, braking, and handling. The data is sent from the sensors to the device in real time, and the device temporarily stores it in a buffer. Specifically, the speed sensor measures data in meters per second, and the acceleration sensor obtains data in m / s².

[0291] Step 2:

[0292] The terminal and emotion engine collect the user's emotional data through cameras and microphones installed inside the vehicle. The input is the user's facial expressions and tone of voice. The data is sent to the terminal in real time, and the terminal temporarily stores it in a buffer. The emotion engine uses facial expression recognition technology to recognize emotional states such as smiling or anger. It also uses voice analysis technology to analyze stress or relaxation from the tone of voice.

[0293] Step 3:

[0294] The terminal transmits the collected driving data and emotion data to the server in real time. The input is the driving data and emotion data stored in the buffer, and the output is the data sent to the server via a secure communication protocol. Specifically, the data is encrypted with TLS / SSL to ensure end-to-end security.

[0295] Step 4:

[0296] The server receives data sent from the device and stores it in a database. The input is driving data and emotion data sent from the device, and the output is data converted into a format that can be stored in the database. The received data is first stored in a buffer area, and then stored in the database according to a predefined format. For example, JSON format data is converted into an SQL database.

[0297] Step 5:

[0298] The server analyzes the stored driving data and emotional data using a generative AI model. The input is the driving data and emotional data stored in the database, and the output is the driving pattern and emotional state as the analysis results. Specifically, a machine learning algorithm is used to extract data features and identify specific driving patterns and emotional states. For example, if a high frequency of sudden braking is determined, this is identified as dangerous driving.

[0299] Step 6:

[0300] The server generates feedback based on the analysis results. The input is the driving pattern and emotional state obtained through the analysis, and the output is a feedback message to be provided to the user. The generative AI model creates advice based on driving improvements and the emotional state. For example, it generates a message such as, "The next time you start at a traffic light, accelerating slowly over three seconds will improve fuel efficiency. It would also be better to listen to relaxing music."

[0301] Step 7:

[0302] The server sends the generated feedback to the user's device. The input is the generated feedback message, and the output is the data to be sent to the user's device. The sending method can be push notification or email. For example, push notifications can be sent using Firebase Cloud Messaging (FCM).

[0303] Step 8:

[0304] The device receives feedback and displays it to the user as a visual graph or text message. The input is the feedback data sent from the server, and the output is the visual feedback information. A dedicated application provides the feedback to the user in a list or detailed view. For example, a section titled "Driving Improvements" may appear on the app's dashboard.

[0305] In this way, specific data processing and actions are performed at each step, providing the user with feedback based on driving improvements and emotional state.

[0306] (Application example 2)

[0307] 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."

[0308] Conventional driving assistance systems only analyze driving data, which means they are unable to provide feedback that takes into account the emotional state of the driver while driving. Furthermore, in store operations, there is a lack of a way to grasp the emotional state of staff in real time and provide appropriate support. As a result, stress on drivers and staff is not reduced, which prevents safe driving and improved work efficiency.

[0309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data, means for transmitting the collected driving data and emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide feedback that takes into account the emotional state of the driver and store staff, thereby improving driving skills and work efficiency.

[0310] "Driving data" is information about vehicle behavior collected while driving, such as vehicle speed, acceleration, braking, and handling.

[0311] "Emotion data" is information relating to the emotional state of the user that is analyzed from facial expressions, tone of voice, and the like.

[0312] A "server" is a computer system for analyzing collected driving and emotion data and generating feedback.

[0313] An "artificial intelligence model" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and identify specific patterns or conditions.

[0314] A "terminal" is a device through which a user receives feedback, such as a smartphone, tablet, or head-mounted display.

[0315] "Feedback" refers to hints and advice for improving driving provided based on the analysis results.

[0316] A "visual graph" is a graphic that displays the analysis results of driving data and emotion data in a format that is visually easy to understand.

[0317] "Text messages" are a means of conveying advice and hints based on analysis results to users in the form of text information.

[0318] The present invention is a system that collects and analyzes driving data and emotional data in real time, and provides drivers with hints for safe driving and feedback for driving improvement. This system is composed of sensors, cameras, and microphones attached to the vehicle, the user's smartphone or head-mounted display (HMD), and a cloud server. Specific embodiments of the present invention are described below.

[0319] Data collection

[0320] First, driving data is collected by various sensors installed in the vehicle (speed sensor, acceleration sensor, brake sensor, handling sensor, etc.), while emotion data is collected by analyzing the user's facial expressions and tone of voice using a camera and microphone.

[0321] Data transmission

[0322] The collected driving and emotion data is transmitted in real time via smartphone or HMD to a cloud server using the MQTT protocol to ensure data confidentiality and integrity.

[0323] Data reception and storage

[0324] The cloud server automatically stores the received driving and emotion data in a cloud database (e.g., Amazon RDS), where the data is formatted appropriately before analysis.

[0325] Data analysis

[0326] The cloud server uses artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis) to analyze the received data, which enables it to identify driving patterns and the user's emotional state and generate appropriate feedback based on driving technique and emotional state.

[0327] Feedback Generation

[0328] Based on the analysis results, the cloud server generates specific driving improvement tips and advice. This feedback is tailored to the user's emotional state. For example, if the user is feeling stressed, a gentle, encouraging message will be provided.

[0329] Send and view feedback

[0330] The generated feedback is sent from the cloud server to the user's smartphone or HMD in the form of push notifications or text messages. Users can easily understand the analysis results through visual graphs and text information and put them into practice the next time they drive.

[0331] Specific examples

[0332] If the emotion engine detects that a store staff member is feeling stressed while arranging shelves, the cloud server generates feedback such as "Take a slow, deep breath. Take a five-minute break," and displays it on the HMD.

[0333] Sensors collect data on the driver's sudden acceleration at traffic lights and send it from the device to a server. At the same time, if the emotion engine analyzes the driver's facial expressions and recognizes that the driver is feeling stressed, the server generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be better to listen to relaxing music while driving," and sends it to the smartphone.

[0334] Prompt Sentence Examples

[0335] "Analyze behavioral and emotional data to generate business improvement tips and advice based on emotional state."

[0336] "Staff are stressed, so please display a message encouraging them to take deep breaths to relax."

[0337] "A particular section of the sales floor is in disarray, please tell us to straighten up the displays."

[0338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0339] Step 1:

[0340] When a user drives a vehicle or starts work, sensors, cameras, and microphones installed in the vehicle are activated to collect driving and emotional data. Inputs include the vehicle's speed, acceleration, braking, handling, and the user's facial expressions and tone of voice. The output is information collected from these data in real time. The specific operation of this step is that various sensor devices collect data while driving and send it to the user terminal in real time.

[0341] Step 2:

[0342] The collected driving data and emotion data are sent to a cloud server via the smartphone or HMD. At this time, the MQTT protocol is used to ensure data confidentiality and integrity. The input is the data collected in real time in step 1. The output is the transmitted driving data and emotion data. The specific operation of this step is for the smartphone or HMD to securely transmit the data to the cloud server using the MQTT protocol.

[0343] Step 3:

[0344] The cloud server automatically stores the received driving data and emotion data in a cloud database (e.g., Amazon RDS). The input is the data sent in step 2. The output is the driving data and emotion data stored in the cloud database. The specific operation of this step is that the server receives the data and stores it in the database.

[0345] Step 4:

[0346] The cloud server processes the stored data and performs analysis using artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis). The input is the driving data and emotion data stored in the cloud database. The output is the analysis results. The specific operations of this step are that the server converts the data into an appropriate format and uses the AI ​​model to identify driving patterns and emotional states.

[0347] Step 5:

[0348] The cloud server generates safe driving tips and individual driving improvement points based on the analysis results. The input is the analysis results obtained in step 4. The output is the generated feedback message. The specific operation of this step is to use the analysis results to generate feedback on driving and work improvements suitable for the user.

[0349] Step 6:

[0350] The generated feedback message is sent from the cloud server to the user's smartphone or HMD in the form of a push notification or text message. The input is the feedback message generated in step 5. The output is the feedback message sent to the user's device. The specific operation of this step is to send the feedback message from the server to the device.

[0351] Step 7:

[0352] The user checks the feedback displayed on the smartphone or HMD and puts it into practice the next time they drive or work. The input is the feedback message sent in step 6. The output is the feedback information received by the user. The specific operation of this step is for the user to check the feedback displayed on the device and reflect it in their driving or work.

[0353] 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.

[0354] 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.

[0355] 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.

[0356] [Second embodiment]

[0357] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0358] 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.

[0359] 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).

[0360] 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.

[0361] 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.

[0362] 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).

[0363] 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.

[0364] 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.

[0365] 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.

[0366] 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.

[0367] 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.

[0368] 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."

[0369] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. This system is composed of sensors attached to the vehicle and a user terminal. The following describes in detail the embodiments of the present invention.

[0370] Data collection

[0371] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[0372] Data transmission

[0373] The devices transmit data collected from the sensors to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[0374] Data reception and storage

[0375] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[0376] Data analysis

[0377] The server uses AI models to analyze the received driving data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors.

[0378] Feedback Generation

[0379] Based on the analysis results, the server generates feedback such as safe driving tips and individual driving improvement points. The generated feedback is based on templates and includes specific, actionable advice.

[0380] Send Feedback

[0381] The generated feedback is sent from the server to the user's device via push notification or email.

[0382] Feedback Display

[0383] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific ways to improve their driving.

[0384] Specific examples

[0385] Here, we will explain a specific example in which a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by sensors and sent from the device to the server. The server analyzes this data and determines that sudden acceleration is having a negative impact on fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[0386] In this way, the present invention allows users to improve their driving skills in real time and increase fuel efficiency. In addition, the collected data can be used in cooperation with insurance companies to receive discounts on car insurance premiums.

[0387] The processing flow will be explained below.

[0388] Step 1:

[0389] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[0390] Step 2:

[0391] The device transmits the buffered data to the server at regular intervals. Data transmission is performed in real time, and data confidentiality and integrity are ensured using a secure communication protocol (e.g., HTTPS).

[0392] Step 3:

[0393] The server receives the data sent from the terminal, stores the received data in a database, and adjusts the data format as necessary.

[0394] Step 4:

[0395] The server uses a generative AI model to analyze the stored data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and identify specific unsafe behaviors (e.g., sudden acceleration, hard braking, sharp turns).

[0396] Step 5:

[0397] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The feedback content is based on templates and includes specific, actionable advice.

[0398] Step 6:

[0399] The server sends the generated feedback to the user's device, and the feedback is delivered reliably via push notification or email.

[0400] Step 7:

[0401] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[0402] Example 1

[0403] 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."

[0404] Current vehicle driving data collection and analysis systems lack the ability to collect driving data in real time and provide effective feedback. Furthermore, they do not provide specific advice for driving improvement, preventing users from appropriately improving their driving skills. Furthermore, there is a need for systems that can efficiently analyze collected data and provide feedback to users while ensuring the confidentiality and integrity of the data.

[0405] 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.

[0406] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to a communication terminal, means for transmitting the driving data from the communication terminal to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the communication terminal, and means for displaying the feedback to the user on the communication terminal, thereby making it possible to collect driving data in real time, efficiently analyze it, and provide specific feedback for driving improvement.

[0407] "Driving data" is a general term for various data related to the driving state of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[0408] A "communication terminal" is a device used to transmit data collected from sensors installed in a vehicle to a server, and generally includes smartphones, tablets, in-vehicle computers, etc.

[0409] The "server" is a computer system that receives driving data transmitted from the communication terminal, analyzes the data, and generates feedback.

[0410] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to analyze driving data, identify driving patterns, and pinpoint specific unsafe behaviors.

[0411] "Feedback" refers to specific advice on improving driving and hints for safe driving that are provided to the user based on the results of analyzing driving data.

[0412] "Visual graphs" are data display formats such as bar graphs, line graphs, and histograms that visually show operational data and analytical results.

[0413] A "text message" is a written message that provides analysis results and feedback to the user in text format.

[0414] A "driving pattern" indicates a tendency or characteristic of driving data under a specific time or situation, and includes, for example, actions such as sudden acceleration or sudden braking.

[0415] This system collects and analyzes vehicle driving data in real time, providing users with tips for safe driving and individual driving improvements. The system consists of sensors installed in the vehicle, a communication terminal for sending and receiving data, and a server for analyzing the data.

[0416] Data collection

[0417] First, driving data is collected by sensors attached to the user's vehicle. These sensors are connected to the vehicle's OBD-II port and capture real-time data such as speed, acceleration, braking, and handling. This data is then transmitted to a communication device via Bluetooth or Wi-Fi.

[0418] Data transmission

[0419] The communication terminal transmits the driving data collected from the sensors to the server using the HTTPS protocol, with SSL / TLS encryption applied to ensure data confidentiality and integrity.

[0420] Data reception and storage

[0421] The server receives the data sent from the communication terminal and temporarily stores it in its memory. The data is received in JSON format, and is then converted into an appropriate format and saved in a database (MySQL, PostgreSQL, etc.).

[0422] Data analysis

[0423] The server then analyzes the received driving data using a generative AI model to identify driving patterns and pinpoint specific unsafe behaviors (e.g., sudden acceleration, hard braking). This analysis uses machine learning frameworks such as TensorFlow and PyTorch.

[0424] Feedback Generation

[0425] Based on the analysis results, the server generates feedback, including tips for safe driving and individual driving improvements. The generated feedback is based on templates and includes specific, actionable advice. For example, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy."

[0426] Send Feedback

[0427] The generated feedback is sent from the server to the communication device via push notification, email, or other methods. The communication protocol used is Firebase Cloud Messaging (FCM) or the SMTP protocol.

[0428] Feedback Display

[0429] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to intuitively understand specific ways to improve their driving. For example, a graph showing driving improvement points or a text message such as "Number of sudden accelerations: 5 times / day" is displayed.

[0430] Specific examples

[0431] For example, consider a case where a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by a sensor and sent from the communication device to the server. The server analyzes this data and determines that sudden acceleration is negatively impacting fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the communication device via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[0432] Prompt Sentence Examples

[0433] Below are some example prompts for a generative AI model to analyze driving data and generate safe driving tips.

[0434] "We will provide you with your driving data in the following format to generate feedback for safe driving.

[0435] Speed: 50km / h

[0436] Acceleration: 3 m / s^2

[0437] Braking: 4 m / s^2

[0438] Handling: Left turn

[0439] Example of feedback to generate: "Next time you start at a traffic light, accelerate slowly over a 3-second period to improve fuel economy."

[0440] In this way, the present invention allows users to collect driving data in real time and receive specific feedback for improving their driving by analyzing it efficiently.

[0441] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0442] Step 1: Data collection

[0443] Sensors installed in the user's vehicle collect driving data, specifically speed, acceleration, braking, handling, and other data in real time, which is then transmitted to a communication device via Bluetooth.

[0444] Input: User's vehicle driving data

[0445] Output: Operation data sent to the communication terminal

[0446] Specific operation: The user drives the vehicle, and the sensor detects when the vehicle accelerates to 60 km / h and records this as acceleration data.

[0447] Step 2: Send data

[0448] The device transmits driving data collected from sensors to the server using a secure protocol (HTTPS), encrypted with SSL / TLS to ensure data integrity and confidentiality.

[0449] Input: Driving data sent to the terminal

[0450] Output: Driving data sent to the server

[0451] Specific operation: The terminal collects data and sends it to the server once per second in packets.

[0452] Step 3: Receiving and storing data

[0453] The server receives the data sent from the device, temporarily stores it in memory, then formats the data in JSON format appropriately and saves it to a database.

[0454] Input: Driving data sent to the server

[0455] Output: Operation data stored in the database

[0456] Specific operation: The server parses the received JSON data and inserts speed data, acceleration data, braking data, etc. into the corresponding columns in the database.

[0457] Step 4: Data analysis

[0458] The server analyzes the driving data stored in the database using a generative AI model. Specifically, it uses TensorFlow and PyTorch to analyze driving patterns and identify behaviors that pose safety issues.

[0459] Input: Operation data stored in the database

[0460] Output: Analysis results of driving patterns

[0461] How it works: The server inputs driving data into the AI ​​model and detects patterns of sudden acceleration. For example, it identifies that sudden acceleration is particularly common during rush hour on Mondays.

[0462] Step 5: Feedback generation

[0463] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback includes specific and actionable advice based on templates.

[0464] Input: Analysis results of driving patterns

[0465] Output: Generated feedback

[0466] Specific behavior: Feedback such as "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy" is generated.

[0467] Step 6: Send your feedback

[0468] The generated feedback is sent from the server to the communication device, and is delivered to the user's device via push notification or email.

[0469] Input: Generated feedback

[0470] Output: Feedback sent to the communication device

[0471] Specific operation: The feedback generated by the server is sent to the device as a push notification, which displays the message, "You can expect to improve fuel efficiency by refraining from sudden acceleration when starting from a traffic light."

[0472] Step 7: Feedback display

[0473] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to review the feedback and understand specific ways to improve their driving.

[0474] Input: Feedback sent to communication terminal

[0475] Output: Feedback displayed as visual graphs and text messages

[0476] Specific operation: The device receives the feedback and displays a text message with a graph on the app screen, such as "Number of sudden accelerations: 5 times / day."

[0477] (Application example 1)

[0478] 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."

[0479] Although conventional driving data analysis systems exist that collect and analyze driving data to provide individual driving improvement points, it is difficult to support special vehicles such as self-driving vehicles. Furthermore, these systems were unable to provide real-time feedback or monitor the driving data of self-driving vehicles, which meant that improvements in driving safety and efficiency were not fully achieved.

[0480] 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.

[0481] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, means for displaying the feedback to the user on the terminal, and means for monitoring the driving data of the autonomous vehicle and providing feedback in real time, thereby enabling the user to monitor the driving data of the autonomous vehicle in real time and immediately receive safe driving tips and improvement points.

[0482] "Driving data" refers to various data relating to the driving state of the vehicle, such as the vehicle's speed, acceleration, braking, and handling.

[0483] An "autonomous vehicle" is a vehicle that can be driven automatically using dedicated hardware and software.

[0484] "Monitoring" is the act of continuously observing data in real time and detecting anomalies or specific patterns.

[0485] "Feedback" refers to hints and specific advice for improving driving that are generated based on the analysis results.

[0486] A "terminal" is a device that receives feedback and displays it to the user, such as a smartphone or tablet.

[0487] The "server" is a centralized management system for receiving, storing, analyzing, and transmitting the results of driving data.

[0488] "Analysis" is the process of deriving safe driving tips and areas for improvement based on collected driving data.

[0489] An "AI model" is an artificial intelligence technology algorithm that learns from large amounts of driving data and identifies and analyzes driving patterns.

[0490] "Real time" refers to a situation where processing and feedback are provided the moment an event occurs.

[0491] "Graph or text message" refers to a visual representation or textual information in a format that presents analysis results or feedback to a user.

[0492] This invention relates to a specific system for collecting driving data and providing users with safe driving tips and individual driving improvement points in real time. This system is composed of various sensors installed in vehicles, a terminal held by the user, and a server that analyzes the data.

[0493] Data collection

[0494] First, various sensors installed in the vehicle collect driving data in real time, including speed sensors, acceleration sensors, braking sensors, and handling sensors, which continuously record various data points generated while driving.

[0495] Data transmission

[0496] The driving data collected by the sensors is then transmitted to a server via the user's device (e.g., a smartphone), using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[0497] Data reception and storage

[0498] The server receives the driving data sent from the terminal and stores it in a database, where it is properly formatted before analysis to ensure data consistency.

[0499] Data analysis

[0500] The server uses machine learning and artificial intelligence (AI) models to analyze the stored driving data. These AI models identify driving patterns and identify unsafe driving behaviors and specific areas for improvement. Based on these analysis results, the server uses templates to generate specific, actionable feedback.

[0501] Feedback generation and submission

[0502] The generated feedback is instantly sent to the user's device, and includes specific tips for safe driving and specific areas for individual driving improvement.

[0503] Feedback Display

[0504] The user's device (such as a smartphone or tablet) displays the submitted feedback as visual graphs and text messages, allowing the user to easily understand specific ways to improve their driving.

[0505] Specific examples

[0506] As a concrete example, consider a situation where a user is driving an autonomous vehicle. For example, if the vehicle accelerates more than normal when going around a curve, this data is collected by sensors and sent to a server. The server analyzes this data and generates feedback suggesting that the vehicle handle the curve more gently. This feedback, such as "The next time you come around a curve, steering more gently will improve safety," is sent to the user's device as a push notification.

[0507] Prompt Sentence Examples

[0508] To give an example to the generative AI model, we use the following prompt:

[0509] Generate feedback like, "When the user was driving around a curve, the acceleration was too high, so next time you drive, you can improve safety by handling the curve more gently."

[0510] The present invention aims to promote safe driving and improve driving efficiency by monitoring driving data of an autonomous vehicle in real time and providing immediate feedback to the user.

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

[0512] Step 1:

[0513] Sensors installed in the user's vehicle collect driving data (speed, acceleration, braking, handling, etc.) in real time. Specifically, the various sensors detect data and format it for transmission to the user's device. The input is raw data from the sensors, and the output is formatted driving data.

[0514] Step 2:

[0515] The terminal transmits the collected driving data to the server using a secure communication protocol (e.g., HTTPS). In specific operations, the terminal receives the formatted data and transmits it to the server via the Internet. The input is the formatted driving data, and the output is the driving data transmitted to the server.

[0516] Step 3:

[0517] The server receives the driving data sent from the terminal and stores it in an internal database. Specifically, the server converts the received data into a format for proper analysis and writes it to the database. The input is the driving data sent to the server, and the output is the data stored in the database.

[0518] Step 4:

[0519] The server uses AI models to analyze the stored driving data. Specifically, the server applies machine learning algorithms to identify driving patterns and identify specific unsafe behaviors and areas for improvement. The input is the driving data stored in the database, and the output is the analyzed feedback information.

[0520] Step 5:

[0521] The server generates specific feedback based on the analysis results. Specifically, the server uses templates to generate text containing safe driving tips and driving improvement points. In this process, a generative AI model is used to create appropriate feedback according to the driving scenario. The input is the analysis results, and the output is the generated feedback.

[0522] Step 6:

[0523] The server sends the generated feedback to the user's device. Specifically, the server sends the feedback to the device by push notification or email. The input is the generated feedback, and the output is the feedback sent to the user's device.

[0524] Step 7:

[0525] The terminal receives the sent feedback and displays it to the user. Specifically, the terminal displays the feedback as a visual graph or a text message. The input is the feedback from the server, and the output is the feedback displayed to the user.

[0526] Step 8:

[0527] The user checks the displayed feedback and practices improvements to their driving. Specifically, the user understands the feedback and incorporates the safe driving tips and improvements into their next drive. The input is the feedback displayed on the device, and the output is the user's driving improvements.

[0528] In this way, by clarifying the specific operations performed at each processing step and their inputs and outputs, the flow of the entire system becomes easier to understand and implementation becomes easier.

[0529] 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.

[0530] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced feedback. This system is composed of sensors attached to the vehicle, a user's terminal, and an emotion engine. The following describes in detail the embodiments of the present invention.

[0531] Data collection

[0532] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[0533] Emotional Data Collection

[0534] Furthermore, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This data is also sent to the terminal in real time, just like the driving data.

[0535] Data transmission

[0536] The device transmits data collected from sensors and the emotion engine to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[0537] Data reception and storage

[0538] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[0539] Data analysis

[0540] The server uses AI models to analyze the received driving and emotional data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors and emotional states.

[0541] Feedback Generation

[0542] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback is adjusted to provide appropriate wording and advice based on the user's emotional state. For example, if the user is feeling stressed, the server will provide gentle, encouraging feedback.

[0543] Send Feedback

[0544] The generated feedback is sent from the server to the user's device via push notification or email.

[0545] Feedback Display

[0546] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific driving improvement methods and tips based on their emotional state.

[0547] Specific examples

[0548] As a concrete example, let us consider a case where a user uses this system during their commute. Data on the user's sudden acceleration at a traffic light is collected by a sensor and sent from the device to the server. At the same time, the emotion engine analyzes the user's facial expressions and recognizes that the user is feeling stressed. The server analyzes the driving data and emotion data, determines that the sudden acceleration is negatively affecting fuel economy, and generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music while driving." The server then sends this feedback to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills, fuel efficiency, and stress reduction.

[0549] In this way, this invention allows users to improve their driving skills in real time and increase fuel efficiency. Furthermore, the collected data can be used in cooperation with insurance companies to provide discounts on car insurance grades. Providing feedback that takes into account the user's emotional state is expected to lead to more appropriate and effective driving improvements.

[0550] The processing flow will be explained below.

[0551] Step 1:

[0552] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[0553] Step 2:

[0554] The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice, generating emotion data, which is also sent to the device and stored in memory.

[0555] Step 3:

[0556] The device transmits the buffered driving and emotion data to a server at regular intervals. Data transmission is performed in real time, using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[0557] Step 4:

[0558] The server receives the driving and emotion data sent from the device, stores the data in a database, and formats it appropriately before analyzing it.

[0559] Step 5:

[0560] The server uses a generative AI model to analyze the stored driving and emotional data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and the user's emotional state.

[0561] Step 6:

[0562] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The generated feedback is adjusted to the appropriate wording and advice content based on the user's emotional state.

[0563] Step 7:

[0564] The server sends the generated feedback to the user's device via push notification or email.

[0565] Step 8:

[0566] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[0567] Specific examples

[0568] As a specific example, a case where a user uses this system during his / her commute will be described.

[0569] Step 1:

[0570] The device collects speed and acceleration data when it suddenly accelerates at traffic lights.

[0571] Step 2:

[0572] The emotion engine analyzes the user's facial expressions and recognizes when the user is feeling stressed.

[0573] Step 3:

[0574] The device transmits driving data and emotion data to a server at regular intervals.

[0575] Step 4:

[0576] The server receives the transmitted driving data and emotion data and stores them in a database.

[0577] Step 5:

[0578] The server analyzes the driving data and emotional data and identifies that sudden acceleration is having a negative effect on fuel economy and that the user is feeling stressed.

[0579] Step 6:

[0580] The server generates feedback such as, "Next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be better to listen to relaxing music while driving."

[0581] Step 7:

[0582] The server sends the generated feedback to the user's smartphone via push notification.

[0583] Step 8:

[0584] The device displays the feedback on the smartphone and provides the user with specific tips on how to improve their driving and hints based on their emotions.

[0585] Example 2

[0586] 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."

[0587] Conventional driver assistance systems focus on collecting and analyzing driving data, but it is difficult to provide feedback that takes into account the driver's emotional state. As a result, they lack appropriate improvement advice based on the driver's actual attention state and stress, and are unable to maximize the effectiveness of safe driving. Furthermore, there is a lack of technological means to provide specific advice based on the driver's emotional state.

[0588] 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.

[0589] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for collecting emotion data, means for transmitting the collected emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide specific and effective safe driving advice according to the driver's emotional state by comprehensively analyzing both the driving data and the emotion data.

[0590] "Driving data" is a set of data relating to the driving of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[0591] A "server" is a computing system for receiving, storing, and analyzing data, and generating and sending feedback.

[0592] The "terminal" is a device that transmits data collected from sensors and emotion engines installed in the vehicle to a server and displays feedback to the user.

[0593] A "sensor" is a measuring device used to collect driving data such as vehicle speed, acceleration, braking, and handling.

[0594] The "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize emotional data.

[0595] "Emotion data" is data that represents the user's emotional state, and is information collected from facial expressions, tone of voice, and the like.

[0596] A "generative AI model" is an algorithm or model that uses machine learning and artificial intelligence techniques to analyze data and generate feedback based on that data.

[0597] "Feedback" refers to safe driving tips and individual driving improvement points generated as a result of analyzing driving data and emotional data.

[0598] The present invention is a system that collects and analyzes driving data and emotion data, and provides hints for safe driving and driving improvement points. Below, specific embodiments of the invention will be described in detail.

[0599] Hardware and software used

[0600] The system consists of sensors installed in the vehicle, a device used by the user, a server that analyzes and stores data, and an emotion engine that recognizes the vehicle's emotional state. The sensors used include speed sensors, acceleration sensors, brake sensors, and handling sensors. The emotion engine uses a facial recognition camera and a voice analysis microphone. A generative AI model is also used to analyze the data.

[0601] Data collection

[0602] Sensors attached to the device collect driving data, including speed, acceleration, braking, and handling.

[0603] At the same time, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This allows the user's emotional state while driving to be collected in real time.

[0604] Data transmission

[0605] The device transmits the collected driving and emotion data to a server in real time, using secure communication protocols such as TLS / SSL to ensure data confidentiality and integrity.

[0606] Data reception and storage

[0607] The server receives data sent from the terminal and stores it in a database. The received data is first stored in a buffer area and then stored in the database according to a predefined format.

[0608] Data analysis

[0609] The server analyzes the stored driving and emotional data, using machine learning algorithms and generative AI models to extract features from the data and identify specific driving patterns and emotional states, such as frequent sudden braking or stressful facial expressions.

[0610] Feedback Generation

[0611] The server generates feedback based on the analysis results. This feedback includes suggestions for improving the user's driving skills and tips for safe driving. The generative AI model takes into account the user's emotional state and provides appropriate language and advice. For example, it may generate feedback such as, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be a good idea to listen to relaxing music while driving."

[0612] Send Feedback

[0613] The server sends the generated feedback to the user's device via push notification or email. For example, Firebase Cloud Messaging (FCM) is used to send push notifications.

[0614] Feedback Display

[0615] The device displays the received feedback to the user as visual graphs or text messages, and the application provides the feedback to the user in summary or detailed views, such as a section titled "Driving Improvements" on the app's dashboard.

[0616] Specific examples

[0617] When a user uses this system during their commute, sensors collect data on sudden acceleration at traffic lights and send it from the device to the server. At the same time, an emotion engine analyzes the user's facial expressions and recognizes their stress level. The server then analyzes this data using an AI model to determine that sudden acceleration is negatively impacting fuel economy, and generates feedback such as, "Next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music." This feedback is sent to the user's smartphone in real time and displayed as a push notification. The user can review this feedback and put it into practice the next time they drive to improve their driving technique, fuel efficiency, and reduce stress.

[0618] Specific prompt examples:

[0619] "Based on the user's driving data and emotional data, please analyze their driving patterns and generate tips for safe driving and improving fuel efficiency. Please also include advice that takes into account the user's stress level."

[0620] The above is a specific embodiment of the present invention. This system allows users to improve their driving skills in real time and increase fuel efficiency. In addition, by providing appropriate feedback according to the user's emotional state, more effective driving assistance can be achieved.

[0621] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0622] Step 1:

[0623] The device collects driving data from sensors installed in the vehicle. The input data is the vehicle's speed, acceleration, braking, and handling. The data is sent from the sensors to the device in real time, and the device temporarily stores it in a buffer. Specifically, the speed sensor measures data in meters per second, and the acceleration sensor obtains data in m / s².

[0624] Step 2:

[0625] The terminal and emotion engine collect the user's emotional data through cameras and microphones installed inside the vehicle. The input is the user's facial expressions and tone of voice. The data is sent to the terminal in real time, and the terminal temporarily stores it in a buffer. The emotion engine uses facial expression recognition technology to recognize emotional states such as smiling or anger. It also uses voice analysis technology to analyze stress or relaxation from the tone of voice.

[0626] Step 3:

[0627] The terminal transmits the collected driving data and emotion data to the server in real time. The input is the driving data and emotion data stored in the buffer, and the output is the data sent to the server via a secure communication protocol. Specifically, the data is encrypted with TLS / SSL to ensure end-to-end security.

[0628] Step 4:

[0629] The server receives data sent from the device and stores it in a database. The input is driving data and emotion data sent from the device, and the output is data converted into a format that can be stored in the database. The received data is first stored in a buffer area, and then stored in the database according to a predefined format. For example, JSON format data is converted into an SQL database.

[0630] Step 5:

[0631] The server analyzes the stored driving data and emotional data using a generative AI model. The input is the driving data and emotional data stored in the database, and the output is the driving pattern and emotional state as the analysis results. Specifically, a machine learning algorithm is used to extract data features and identify specific driving patterns and emotional states. For example, if a high frequency of sudden braking is determined, this is identified as dangerous driving.

[0632] Step 6:

[0633] The server generates feedback based on the analysis results. The input is the driving pattern and emotional state obtained through the analysis, and the output is a feedback message to be provided to the user. The generative AI model creates advice based on driving improvements and the emotional state. For example, it generates a message such as, "The next time you start at a traffic light, accelerating slowly over three seconds will improve fuel efficiency. It would also be better to listen to relaxing music."

[0634] Step 7:

[0635] The server sends the generated feedback to the user's device. The input is the generated feedback message, and the output is the data to be sent to the user's device. The sending method can be push notification or email. For example, push notifications can be sent using Firebase Cloud Messaging (FCM).

[0636] Step 8:

[0637] The device receives feedback and displays it to the user as a visual graph or text message. The input is the feedback data sent from the server, and the output is the visual feedback information. A dedicated application provides the feedback to the user in a list or detailed view. For example, a section titled "Driving Improvements" may appear on the app's dashboard.

[0638] In this way, specific data processing and actions are performed at each step, providing the user with feedback based on driving improvements and emotional state.

[0639] (Application example 2)

[0640] 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."

[0641] Conventional driving assistance systems only analyze driving data, which means they are unable to provide feedback that takes into account the emotional state of the driver while driving. Furthermore, in store operations, there is a lack of a way to grasp the emotional state of staff in real time and provide appropriate support. As a result, stress on drivers and staff is not reduced, which prevents safe driving and improved work efficiency.

[0642] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data, means for transmitting the collected driving data and emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide feedback that takes into account the emotional state of the driver and store staff, thereby improving driving skills and work efficiency.

[0643] "Driving data" is information about vehicle behavior collected while driving, such as vehicle speed, acceleration, braking, and handling.

[0644] "Emotion data" is information relating to the emotional state of the user that is analyzed from facial expressions, tone of voice, and the like.

[0645] A "server" is a computer system for analyzing collected driving and emotion data and generating feedback.

[0646] An "artificial intelligence model" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and identify specific patterns or conditions.

[0647] A "terminal" is a device through which a user receives feedback, such as a smartphone, tablet, or head-mounted display.

[0648] "Feedback" refers to hints and advice for improving driving provided based on the analysis results.

[0649] A "visual graph" is a graphic that displays the analysis results of driving data and emotion data in a format that is visually easy to understand.

[0650] "Text messages" are a means of conveying advice and hints based on analysis results to users in the form of text information.

[0651] The present invention is a system that collects and analyzes driving data and emotional data in real time, and provides drivers with hints for safe driving and feedback for driving improvement. This system is composed of sensors, cameras, and microphones attached to the vehicle, the user's smartphone or head-mounted display (HMD), and a cloud server. Specific embodiments of the present invention are described below.

[0652] Data collection

[0653] First, driving data is collected by various sensors installed in the vehicle (speed sensor, acceleration sensor, brake sensor, handling sensor, etc.), while emotion data is collected by analyzing the user's facial expressions and tone of voice using a camera and microphone.

[0654] Data transmission

[0655] The collected driving and emotion data is transmitted in real time via smartphone or HMD to a cloud server using the MQTT protocol to ensure data confidentiality and integrity.

[0656] Data reception and storage

[0657] The cloud server automatically stores the received driving and emotion data in a cloud database (e.g., Amazon RDS), where the data is formatted appropriately before analysis.

[0658] Data analysis

[0659] The cloud server uses artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis) to analyze the received data, which enables it to identify driving patterns and the user's emotional state and generate appropriate feedback based on driving technique and emotional state.

[0660] Feedback Generation

[0661] Based on the analysis results, the cloud server generates specific driving improvement tips and advice. This feedback is tailored to the user's emotional state. For example, if the user is feeling stressed, a gentle, encouraging message will be provided.

[0662] Send and view feedback

[0663] The generated feedback is sent from the cloud server to the user's smartphone or HMD in the form of push notifications or text messages. Users can easily understand the analysis results through visual graphs and text information and put them into practice the next time they drive.

[0664] Specific examples

[0665] If the emotion engine detects that a store staff member is feeling stressed while arranging shelves, the cloud server generates feedback such as "Take a slow, deep breath. Take a five-minute break," and displays it on the HMD.

[0666] Sensors collect data on the driver's sudden acceleration at traffic lights and send it from the device to a server. At the same time, if the emotion engine analyzes the driver's facial expressions and recognizes that the driver is feeling stressed, the server generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be better to listen to relaxing music while driving," and sends it to the smartphone.

[0667] Prompt Sentence Examples

[0668] "Analyze behavioral and emotional data to generate business improvement tips and advice based on emotional state."

[0669] "Staff are stressed, so please display a message encouraging them to take deep breaths to relax."

[0670] "A particular section of the sales floor is in disarray, please tell us to straighten up the displays."

[0671] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0672] Step 1:

[0673] When a user drives a vehicle or starts work, sensors, cameras, and microphones installed in the vehicle are activated to collect driving and emotional data. Inputs include the vehicle's speed, acceleration, braking, handling, and the user's facial expressions and tone of voice. The output is information collected from these data in real time. The specific operation of this step is that various sensor devices collect data while driving and send it to the user terminal in real time.

[0674] Step 2:

[0675] The collected driving data and emotion data are sent to a cloud server via the smartphone or HMD. At this time, the MQTT protocol is used to ensure data confidentiality and integrity. The input is the data collected in real time in step 1. The output is the transmitted driving data and emotion data. The specific operation of this step is for the smartphone or HMD to securely transmit the data to the cloud server using the MQTT protocol.

[0676] Step 3:

[0677] The cloud server automatically stores the received driving data and emotion data in a cloud database (e.g., Amazon RDS). The input is the data sent in step 2. The output is the driving data and emotion data stored in the cloud database. The specific operation of this step is that the server receives the data and stores it in the database.

[0678] Step 4:

[0679] The cloud server processes the stored data and performs analysis using artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis). The input is the driving data and emotion data stored in the cloud database. The output is the analysis results. The specific operations of this step are that the server converts the data into an appropriate format and uses the AI ​​model to identify driving patterns and emotional states.

[0680] Step 5:

[0681] The cloud server generates safe driving tips and individual driving improvement points based on the analysis results. The input is the analysis results obtained in step 4. The output is the generated feedback message. The specific operation of this step is to use the analysis results to generate feedback on driving and work improvements suitable for the user.

[0682] Step 6:

[0683] The generated feedback message is sent from the cloud server to the user's smartphone or HMD in the form of a push notification or text message. The input is the feedback message generated in step 5. The output is the feedback message sent to the user's device. The specific operation of this step is to send the feedback message from the server to the device.

[0684] Step 7:

[0685] The user checks the feedback displayed on the smartphone or HMD and puts it into practice the next time they drive or work. The input is the feedback message sent in step 6. The output is the feedback information received by the user. The specific operation of this step is for the user to check the feedback displayed on the device and reflect it in their driving or work.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] [Third embodiment]

[0690] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0691] 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.

[0692] 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).

[0693] 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.

[0694] 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.

[0695] 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).

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] 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."

[0702] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. This system is composed of sensors attached to the vehicle and a user terminal. The following describes in detail the embodiments of the present invention.

[0703] Data collection

[0704] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[0705] Data transmission

[0706] The devices transmit data collected from the sensors to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[0707] Data reception and storage

[0708] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[0709] Data analysis

[0710] The server uses AI models to analyze the received driving data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors.

[0711] Feedback Generation

[0712] Based on the analysis results, the server generates feedback such as safe driving tips and individual driving improvement points. The generated feedback is based on templates and includes specific, actionable advice.

[0713] Send Feedback

[0714] The generated feedback is sent from the server to the user's device via push notification or email.

[0715] Feedback Display

[0716] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific ways to improve their driving.

[0717] Specific examples

[0718] Here, we will explain a specific example in which a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by sensors and sent from the device to the server. The server analyzes this data and determines that sudden acceleration is having a negative impact on fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[0719] In this way, the present invention allows users to improve their driving skills in real time and increase fuel efficiency. In addition, the collected data can be used in cooperation with insurance companies to receive discounts on car insurance premiums.

[0720] The processing flow will be explained below.

[0721] Step 1:

[0722] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[0723] Step 2:

[0724] The device transmits the buffered data to the server at regular intervals. Data transmission is performed in real time, and data confidentiality and integrity are ensured using a secure communication protocol (e.g., HTTPS).

[0725] Step 3:

[0726] The server receives the data sent from the terminal, stores the received data in a database, and adjusts the data format as necessary.

[0727] Step 4:

[0728] The server uses a generative AI model to analyze the stored data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and identify specific unsafe behaviors (e.g., sudden acceleration, hard braking, sharp turns).

[0729] Step 5:

[0730] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The feedback content is based on templates and includes specific, actionable advice.

[0731] Step 6:

[0732] The server sends the generated feedback to the user's device, and the feedback is delivered reliably via push notification or email.

[0733] Step 7:

[0734] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[0735] Example 1

[0736] 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."

[0737] Current vehicle driving data collection and analysis systems lack the ability to collect driving data in real time and provide effective feedback. Furthermore, they do not provide specific advice for driving improvement, preventing users from appropriately improving their driving skills. Furthermore, there is a need for systems that can efficiently analyze collected data and provide feedback to users while ensuring the confidentiality and integrity of the data.

[0738] 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.

[0739] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to a communication terminal, means for transmitting the driving data from the communication terminal to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the communication terminal, and means for displaying the feedback to the user on the communication terminal, thereby making it possible to collect driving data in real time, efficiently analyze it, and provide specific feedback for driving improvement.

[0740] "Driving data" is a general term for various data related to the driving state of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[0741] A "communication terminal" is a device used to transmit data collected from sensors installed in a vehicle to a server, and generally includes smartphones, tablets, in-vehicle computers, etc.

[0742] The "server" is a computer system that receives driving data transmitted from the communication terminal, analyzes the data, and generates feedback.

[0743] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to analyze driving data, identify driving patterns, and pinpoint specific unsafe behaviors.

[0744] "Feedback" refers to specific advice on improving driving and hints for safe driving that are provided to the user based on the results of analyzing driving data.

[0745] "Visual graphs" are data display formats such as bar graphs, line graphs, and histograms that visually show operational data and analytical results.

[0746] A "text message" is a written message that provides analysis results and feedback to the user in text format.

[0747] A "driving pattern" indicates a tendency or characteristic of driving data under a specific time or situation, and includes, for example, actions such as sudden acceleration or sudden braking.

[0748] This system collects and analyzes vehicle driving data in real time, providing users with tips for safe driving and individual driving improvements. The system consists of sensors installed in the vehicle, a communication terminal for sending and receiving data, and a server for analyzing the data.

[0749] Data collection

[0750] First, driving data is collected by sensors attached to the user's vehicle. These sensors are connected to the vehicle's OBD-II port and capture real-time data such as speed, acceleration, braking, and handling. This data is then transmitted to a communication device via Bluetooth or Wi-Fi.

[0751] Data transmission

[0752] The communication terminal transmits the driving data collected from the sensors to the server using the HTTPS protocol, with SSL / TLS encryption applied to ensure data confidentiality and integrity.

[0753] Data reception and storage

[0754] The server receives the data sent from the communication terminal and temporarily stores it in its memory. The data is received in JSON format, and is then converted into an appropriate format and saved in a database (MySQL, PostgreSQL, etc.).

[0755] Data analysis

[0756] The server then analyzes the received driving data using a generative AI model to identify driving patterns and pinpoint specific unsafe behaviors (e.g., sudden acceleration, hard braking). This analysis uses machine learning frameworks such as TensorFlow and PyTorch.

[0757] Feedback Generation

[0758] Based on the analysis results, the server generates feedback, including tips for safe driving and individual driving improvements. The generated feedback is based on templates and includes specific, actionable advice. For example, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy."

[0759] Send Feedback

[0760] The generated feedback is sent from the server to the communication device via push notification, email, or other methods. The communication protocol used is Firebase Cloud Messaging (FCM) or the SMTP protocol.

[0761] Feedback Display

[0762] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to intuitively understand specific ways to improve their driving. For example, a graph showing driving improvement points or a text message such as "Number of sudden accelerations: 5 times / day" is displayed.

[0763] Specific examples

[0764] For example, consider a case where a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by a sensor and sent from the communication device to the server. The server analyzes this data and determines that sudden acceleration is negatively impacting fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the communication device via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[0765] Prompt Sentence Examples

[0766] Below are some example prompts for a generative AI model to analyze driving data and generate safe driving tips.

[0767] "We will provide you with your driving data in the following format to generate feedback for safe driving.

[0768] Speed: 50km / h

[0769] Acceleration: 3 m / s^2

[0770] Braking: 4 m / s^2

[0771] Handling: Left turn

[0772] Example of feedback to generate: "Next time you start at a traffic light, accelerate slowly over a 3-second period to improve fuel economy."

[0773] In this way, the present invention allows users to collect driving data in real time and receive specific feedback for improving their driving by analyzing it efficiently.

[0774] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0775] Step 1: Data collection

[0776] Sensors installed in the user's vehicle collect driving data, specifically speed, acceleration, braking, handling, and other data in real time, which is then transmitted to a communication device via Bluetooth.

[0777] Input: User's vehicle driving data

[0778] Output: Operation data sent to the communication terminal

[0779] Specific operation: The user drives the vehicle, and the sensor detects when the vehicle accelerates to 60 km / h and records this as acceleration data.

[0780] Step 2: Send data

[0781] The device transmits driving data collected from sensors to the server using a secure protocol (HTTPS), encrypted with SSL / TLS to ensure data integrity and confidentiality.

[0782] Input: Driving data sent to the terminal

[0783] Output: Driving data sent to the server

[0784] Specific operation: The terminal collects data and sends it to the server once per second in packets.

[0785] Step 3: Receiving and storing data

[0786] The server receives the data sent from the device, temporarily stores it in memory, then formats the data in JSON format appropriately and saves it to a database.

[0787] Input: Driving data sent to the server

[0788] Output: Operation data stored in the database

[0789] Specific operation: The server parses the received JSON data and inserts speed data, acceleration data, braking data, etc. into the corresponding columns in the database.

[0790] Step 4: Data analysis

[0791] The server analyzes the driving data stored in the database using a generative AI model. Specifically, it uses TensorFlow and PyTorch to analyze driving patterns and identify behaviors that pose safety issues.

[0792] Input: Operation data stored in the database

[0793] Output: Analysis results of driving patterns

[0794] How it works: The server inputs driving data into the AI ​​model and detects patterns of sudden acceleration. For example, it identifies that sudden acceleration is particularly common during rush hour on Mondays.

[0795] Step 5: Feedback generation

[0796] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback includes specific and actionable advice based on templates.

[0797] Input: Analysis results of driving patterns

[0798] Output: Generated feedback

[0799] Specific behavior: Feedback such as "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy" is generated.

[0800] Step 6: Send your feedback

[0801] The generated feedback is sent from the server to the communication device, and is delivered to the user's device via push notification or email.

[0802] Input: Generated feedback

[0803] Output: Feedback sent to the communication device

[0804] Specific operation: The feedback generated by the server is sent to the device as a push notification, which displays the message, "You can expect to improve fuel efficiency by refraining from sudden acceleration when starting from a traffic light."

[0805] Step 7: Feedback display

[0806] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to review the feedback and understand specific ways to improve their driving.

[0807] Input: Feedback sent to communication terminal

[0808] Output: Feedback displayed as visual graphs and text messages

[0809] Specific operation: The device receives the feedback and displays a text message with a graph on the app screen, such as "Number of sudden accelerations: 5 times / day."

[0810] (Application example 1)

[0811] 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."

[0812] Although conventional driving data analysis systems exist that collect and analyze driving data to provide individual driving improvement points, it is difficult to support special vehicles such as self-driving vehicles. Furthermore, these systems were unable to provide real-time feedback or monitor the driving data of self-driving vehicles, which meant that improvements in driving safety and efficiency were not fully achieved.

[0813] 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.

[0814] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, means for displaying the feedback to the user on the terminal, and means for monitoring the driving data of the autonomous vehicle and providing feedback in real time, thereby enabling the user to monitor the driving data of the autonomous vehicle in real time and immediately receive safe driving tips and improvement points.

[0815] "Driving data" refers to various data relating to the driving state of the vehicle, such as the vehicle's speed, acceleration, braking, and handling.

[0816] An "autonomous vehicle" is a vehicle that can be driven automatically using dedicated hardware and software.

[0817] "Monitoring" is the act of continuously observing data in real time and detecting anomalies or specific patterns.

[0818] "Feedback" refers to hints and specific advice for improving driving that are generated based on the analysis results.

[0819] A "terminal" is a device that receives feedback and displays it to the user, such as a smartphone or tablet.

[0820] The "server" is a centralized management system for receiving, storing, analyzing, and transmitting the results of driving data.

[0821] "Analysis" is the process of deriving safe driving tips and areas for improvement based on collected driving data.

[0822] An "AI model" is an artificial intelligence technology algorithm that learns from large amounts of driving data and identifies and analyzes driving patterns.

[0823] "Real time" refers to a situation where processing and feedback are provided the moment an event occurs.

[0824] "Graph or text message" refers to a visual representation or textual information in a format that presents analysis results or feedback to a user.

[0825] This invention relates to a specific system for collecting driving data and providing users with safe driving tips and individual driving improvement points in real time. This system is composed of various sensors installed in vehicles, a terminal held by the user, and a server that analyzes the data.

[0826] Data collection

[0827] First, various sensors installed in the vehicle collect driving data in real time, including speed sensors, acceleration sensors, braking sensors, and handling sensors, which continuously record various data points generated while driving.

[0828] Data transmission

[0829] The driving data collected by the sensors is then transmitted to a server via the user's device (e.g., a smartphone), using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[0830] Data reception and storage

[0831] The server receives the driving data sent from the terminal and stores it in a database, where it is properly formatted before analysis to ensure data consistency.

[0832] Data analysis

[0833] The server uses machine learning and artificial intelligence (AI) models to analyze the stored driving data. These AI models identify driving patterns and identify unsafe driving behaviors and specific areas for improvement. Based on these analysis results, the server uses templates to generate specific, actionable feedback.

[0834] Feedback generation and submission

[0835] The generated feedback is instantly sent to the user's device, and includes specific tips for safe driving and specific areas for individual driving improvement.

[0836] Feedback Display

[0837] The user's device (such as a smartphone or tablet) displays the submitted feedback as visual graphs and text messages, allowing the user to easily understand specific ways to improve their driving.

[0838] Specific examples

[0839] As a concrete example, consider a situation where a user is driving an autonomous vehicle. For example, if the vehicle accelerates more than normal when going around a curve, this data is collected by sensors and sent to a server. The server analyzes this data and generates feedback suggesting that the vehicle handle the curve more gently. This feedback, such as "The next time you come around a curve, steering more gently will improve safety," is sent to the user's device as a push notification.

[0840] Prompt Sentence Examples

[0841] To give an example to the generative AI model, we use the following prompt:

[0842] Generate feedback like, "When the user was driving around a curve, the acceleration was too high, so next time you drive, you can improve safety by handling the curve more gently."

[0843] The present invention aims to promote safe driving and improve driving efficiency by monitoring driving data of an autonomous vehicle in real time and providing immediate feedback to the user.

[0844] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0845] Step 1:

[0846] Sensors installed in the user's vehicle collect driving data (speed, acceleration, braking, handling, etc.) in real time. Specifically, the various sensors detect data and format it for transmission to the user's device. The input is raw data from the sensors, and the output is formatted driving data.

[0847] Step 2:

[0848] The terminal transmits the collected driving data to the server using a secure communication protocol (e.g., HTTPS). In specific operations, the terminal receives the formatted data and transmits it to the server via the Internet. The input is the formatted driving data, and the output is the driving data transmitted to the server.

[0849] Step 3:

[0850] The server receives the driving data sent from the terminal and stores it in an internal database. Specifically, the server converts the received data into a format for proper analysis and writes it to the database. The input is the driving data sent to the server, and the output is the data stored in the database.

[0851] Step 4:

[0852] The server uses AI models to analyze the stored driving data. Specifically, the server applies machine learning algorithms to identify driving patterns and identify specific unsafe behaviors and areas for improvement. The input is the driving data stored in the database, and the output is the analyzed feedback information.

[0853] Step 5:

[0854] The server generates specific feedback based on the analysis results. Specifically, the server uses templates to generate text containing safe driving tips and driving improvement points. In this process, a generative AI model is used to create appropriate feedback according to the driving scenario. The input is the analysis results, and the output is the generated feedback.

[0855] Step 6:

[0856] The server sends the generated feedback to the user's device. Specifically, the server sends the feedback to the device by push notification or email. The input is the generated feedback, and the output is the feedback sent to the user's device.

[0857] Step 7:

[0858] The terminal receives the sent feedback and displays it to the user. Specifically, the terminal displays the feedback as a visual graph or a text message. The input is the feedback from the server, and the output is the feedback displayed to the user.

[0859] Step 8:

[0860] The user checks the displayed feedback and practices improvements to their driving. Specifically, the user understands the feedback and incorporates the safe driving tips and improvements into their next drive. The input is the feedback displayed on the device, and the output is the user's driving improvements.

[0861] In this way, by clarifying the specific operations performed at each processing step and their inputs and outputs, the flow of the entire system becomes easier to understand and implementation becomes easier.

[0862] 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.

[0863] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced feedback. This system is composed of sensors attached to the vehicle, a user's terminal, and an emotion engine. The following describes in detail the embodiments of the present invention.

[0864] Data collection

[0865] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[0866] Emotional Data Collection

[0867] Furthermore, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This data is also sent to the terminal in real time, just like the driving data.

[0868] Data transmission

[0869] The device transmits data collected from sensors and the emotion engine to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[0870] Data reception and storage

[0871] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[0872] Data analysis

[0873] The server uses AI models to analyze the received driving and emotional data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors and emotional states.

[0874] Feedback Generation

[0875] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback is adjusted to provide appropriate wording and advice based on the user's emotional state. For example, if the user is feeling stressed, the server will provide gentle, encouraging feedback.

[0876] Send Feedback

[0877] The generated feedback is sent from the server to the user's device via push notification or email.

[0878] Feedback Display

[0879] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific driving improvement methods and tips based on their emotional state.

[0880] Specific examples

[0881] As a concrete example, let us consider a case where a user uses this system during their commute. Data on the user's sudden acceleration at a traffic light is collected by a sensor and sent from the device to the server. At the same time, the emotion engine analyzes the user's facial expressions and recognizes that the user is feeling stressed. The server analyzes the driving data and emotion data, determines that the sudden acceleration is negatively affecting fuel economy, and generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music while driving." The server then sends this feedback to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills, fuel efficiency, and stress reduction.

[0882] In this way, this invention allows users to improve their driving skills in real time and increase fuel efficiency. Furthermore, the collected data can be used in cooperation with insurance companies to provide discounts on car insurance grades. Providing feedback that takes into account the user's emotional state is expected to lead to more appropriate and effective driving improvements.

[0883] The processing flow will be explained below.

[0884] Step 1:

[0885] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[0886] Step 2:

[0887] The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice, generating emotion data, which is also sent to the device and stored in memory.

[0888] Step 3:

[0889] The device transmits the buffered driving and emotion data to a server at regular intervals. Data transmission is performed in real time, using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[0890] Step 4:

[0891] The server receives the driving and emotion data sent from the device, stores the data in a database, and formats it appropriately before analyzing it.

[0892] Step 5:

[0893] The server uses a generative AI model to analyze the stored driving and emotional data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and the user's emotional state.

[0894] Step 6:

[0895] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The generated feedback is adjusted to the appropriate wording and advice content based on the user's emotional state.

[0896] Step 7:

[0897] The server sends the generated feedback to the user's device via push notification or email.

[0898] Step 8:

[0899] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[0900] Specific examples

[0901] As a specific example, a case where a user uses this system during his / her commute will be described.

[0902] Step 1:

[0903] The device collects speed and acceleration data when it suddenly accelerates at traffic lights.

[0904] Step 2:

[0905] The emotion engine analyzes the user's facial expressions and recognizes when the user is feeling stressed.

[0906] Step 3:

[0907] The device transmits driving data and emotion data to a server at regular intervals.

[0908] Step 4:

[0909] The server receives the transmitted driving data and emotion data and stores them in a database.

[0910] Step 5:

[0911] The server analyzes the driving data and emotional data and identifies that sudden acceleration is having a negative effect on fuel economy and that the user is feeling stressed.

[0912] Step 6:

[0913] The server generates feedback such as, "Next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be better to listen to relaxing music while driving."

[0914] Step 7:

[0915] The server sends the generated feedback to the user's smartphone via push notification.

[0916] Step 8:

[0917] The device displays the feedback on the smartphone and provides the user with specific tips on how to improve their driving and hints based on their emotions.

[0918] Example 2

[0919] 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."

[0920] Conventional driver assistance systems focus on collecting and analyzing driving data, but it is difficult to provide feedback that takes into account the driver's emotional state. As a result, they lack appropriate improvement advice based on the driver's actual attention state and stress, and are unable to maximize the effectiveness of safe driving. Furthermore, there is a lack of technological means to provide specific advice based on the driver's emotional state.

[0921] 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.

[0922] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for collecting emotion data, means for transmitting the collected emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide specific and effective safe driving advice according to the driver's emotional state by comprehensively analyzing both the driving data and the emotion data.

[0923] "Driving data" is a set of data relating to the driving of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[0924] A "server" is a computing system for receiving, storing, and analyzing data, and generating and sending feedback.

[0925] The "terminal" is a device that transmits data collected from sensors and emotion engines installed in the vehicle to a server and displays feedback to the user.

[0926] A "sensor" is a measuring device used to collect driving data such as vehicle speed, acceleration, braking, and handling.

[0927] The "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize emotional data.

[0928] "Emotion data" is data that represents the user's emotional state, and is information collected from facial expressions, tone of voice, and the like.

[0929] A "generative AI model" is an algorithm or model that uses machine learning and artificial intelligence techniques to analyze data and generate feedback based on that data.

[0930] "Feedback" refers to safe driving tips and individual driving improvement points generated as a result of analyzing driving data and emotional data.

[0931] The present invention is a system that collects and analyzes driving data and emotion data, and provides hints for safe driving and driving improvement points. Below, specific embodiments of the invention will be described in detail.

[0932] Hardware and software used

[0933] The system consists of sensors installed in the vehicle, a device used by the user, a server that analyzes and stores data, and an emotion engine that recognizes the vehicle's emotional state. The sensors used include speed sensors, acceleration sensors, brake sensors, and handling sensors. The emotion engine uses a facial recognition camera and a voice analysis microphone. A generative AI model is also used to analyze the data.

[0934] Data collection

[0935] Sensors attached to the device collect driving data, including speed, acceleration, braking, and handling.

[0936] At the same time, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This allows the user's emotional state while driving to be collected in real time.

[0937] Data transmission

[0938] The device transmits the collected driving and emotion data to a server in real time, using secure communication protocols such as TLS / SSL to ensure data confidentiality and integrity.

[0939] Data reception and storage

[0940] The server receives data sent from the terminal and stores it in a database. The received data is first stored in a buffer area and then stored in the database according to a predefined format.

[0941] Data analysis

[0942] The server analyzes the stored driving and emotional data, using machine learning algorithms and generative AI models to extract features from the data and identify specific driving patterns and emotional states, such as frequent sudden braking or stressful facial expressions.

[0943] Feedback Generation

[0944] The server generates feedback based on the analysis results. This feedback includes suggestions for improving the user's driving skills and tips for safe driving. The generative AI model takes into account the user's emotional state and provides appropriate language and advice. For example, it may generate feedback such as, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be a good idea to listen to relaxing music while driving."

[0945] Send Feedback

[0946] The server sends the generated feedback to the user's device via push notification or email. For example, Firebase Cloud Messaging (FCM) is used to send push notifications.

[0947] Feedback Display

[0948] The device displays the received feedback to the user as visual graphs or text messages, and the application provides the feedback to the user in summary or detailed views, such as a section titled "Driving Improvements" on the app's dashboard.

[0949] Specific examples

[0950] When a user uses this system during their commute, sensors collect data on sudden acceleration at traffic lights and send it from the device to the server. At the same time, an emotion engine analyzes the user's facial expressions and recognizes their stress level. The server then analyzes this data using an AI model to determine that sudden acceleration is negatively impacting fuel economy, and generates feedback such as, "Next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music." This feedback is sent to the user's smartphone in real time and displayed as a push notification. The user can review this feedback and put it into practice the next time they drive to improve their driving technique, fuel efficiency, and reduce stress.

[0951] Specific prompt examples:

[0952] "Based on the user's driving data and emotional data, please analyze their driving patterns and generate tips for safe driving and improving fuel efficiency. Please also include advice that takes into account the user's stress level."

[0953] The above is a specific embodiment of the present invention. This system allows users to improve their driving skills in real time and increase fuel efficiency. In addition, by providing appropriate feedback according to the user's emotional state, more effective driving assistance can be achieved.

[0954] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0955] Step 1:

[0956] The device collects driving data from sensors installed in the vehicle. The input data is the vehicle's speed, acceleration, braking, and handling. The data is sent from the sensors to the device in real time, and the device temporarily stores it in a buffer. Specifically, the speed sensor measures data in meters per second, and the acceleration sensor obtains data in m / s².

[0957] Step 2:

[0958] The terminal and emotion engine collect the user's emotional data through cameras and microphones installed inside the vehicle. The input is the user's facial expressions and tone of voice. The data is sent to the terminal in real time, and the terminal temporarily stores it in a buffer. The emotion engine uses facial expression recognition technology to recognize emotional states such as smiling or anger. It also uses voice analysis technology to analyze stress or relaxation from the tone of voice.

[0959] Step 3:

[0960] The terminal transmits the collected driving data and emotion data to the server in real time. The input is the driving data and emotion data stored in the buffer, and the output is the data sent to the server via a secure communication protocol. Specifically, the data is encrypted with TLS / SSL to ensure end-to-end security.

[0961] Step 4:

[0962] The server receives data sent from the device and stores it in a database. The input is driving data and emotion data sent from the device, and the output is data converted into a format that can be stored in the database. The received data is first stored in a buffer area, and then stored in the database according to a predefined format. For example, JSON format data is converted into an SQL database.

[0963] Step 5:

[0964] The server analyzes the stored driving data and emotional data using a generative AI model. The input is the driving data and emotional data stored in the database, and the output is the driving pattern and emotional state as the analysis results. Specifically, a machine learning algorithm is used to extract data features and identify specific driving patterns and emotional states. For example, if a high frequency of sudden braking is determined, this is identified as dangerous driving.

[0965] Step 6:

[0966] The server generates feedback based on the analysis results. The input is the driving pattern and emotional state obtained through the analysis, and the output is a feedback message to be provided to the user. The generative AI model creates advice based on driving improvements and the emotional state. For example, it generates a message such as, "The next time you start at a traffic light, accelerating slowly over three seconds will improve fuel efficiency. It would also be better to listen to relaxing music."

[0967] Step 7:

[0968] The server sends the generated feedback to the user's device. The input is the generated feedback message, and the output is the data to be sent to the user's device. The sending method can be push notification or email. For example, push notifications can be sent using Firebase Cloud Messaging (FCM).

[0969] Step 8:

[0970] The device receives feedback and displays it to the user as a visual graph or text message. The input is the feedback data sent from the server, and the output is the visual feedback information. A dedicated application provides the feedback to the user in a list or detailed view. For example, a section titled "Driving Improvements" may appear on the app's dashboard.

[0971] In this way, specific data processing and actions are performed at each step, providing the user with feedback based on driving improvements and emotional state.

[0972] (Application example 2)

[0973] 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."

[0974] Conventional driving assistance systems only analyze driving data, which means they are unable to provide feedback that takes into account the emotional state of the driver while driving. Furthermore, in store operations, there is a lack of a way to grasp the emotional state of staff in real time and provide appropriate support. As a result, stress on drivers and staff is not reduced, which prevents safe driving and improved work efficiency.

[0975] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data, means for transmitting the collected driving data and emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide feedback that takes into account the emotional state of the driver and store staff, thereby improving driving skills and work efficiency.

[0976] "Driving data" is information about vehicle behavior collected while driving, such as vehicle speed, acceleration, braking, and handling.

[0977] "Emotion data" is information relating to the emotional state of the user that is analyzed from facial expressions, tone of voice, and the like.

[0978] A "server" is a computer system for analyzing collected driving and emotion data and generating feedback.

[0979] An "artificial intelligence model" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and identify specific patterns or conditions.

[0980] A "terminal" is a device through which a user receives feedback, such as a smartphone, tablet, or head-mounted display.

[0981] "Feedback" refers to hints and advice for improving driving provided based on the analysis results.

[0982] A "visual graph" is a graphic that displays the analysis results of driving data and emotion data in a format that is visually easy to understand.

[0983] "Text messages" are a means of conveying advice and hints based on analysis results to users in the form of text information.

[0984] The present invention is a system that collects and analyzes driving data and emotional data in real time, and provides drivers with hints for safe driving and feedback for driving improvement. This system is composed of sensors, cameras, and microphones attached to the vehicle, the user's smartphone or head-mounted display (HMD), and a cloud server. Specific embodiments of the present invention are described below.

[0985] Data collection

[0986] First, driving data is collected by various sensors installed in the vehicle (speed sensor, acceleration sensor, brake sensor, handling sensor, etc.), while emotion data is collected by analyzing the user's facial expressions and tone of voice using a camera and microphone.

[0987] Data transmission

[0988] The collected driving and emotion data is transmitted in real time via smartphone or HMD to a cloud server using the MQTT protocol to ensure data confidentiality and integrity.

[0989] Data reception and storage

[0990] The cloud server automatically stores the received driving and emotion data in a cloud database (e.g., Amazon RDS), where the data is formatted appropriately before analysis.

[0991] Data analysis

[0992] The cloud server uses artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis) to analyze the received data, which enables it to identify driving patterns and the user's emotional state and generate appropriate feedback based on driving technique and emotional state.

[0993] Feedback Generation

[0994] Based on the analysis results, the cloud server generates specific driving improvement tips and advice. This feedback is tailored to the user's emotional state. For example, if the user is feeling stressed, a gentle, encouraging message will be provided.

[0995] Send and view feedback

[0996] The generated feedback is sent from the cloud server to the user's smartphone or HMD in the form of push notifications or text messages. Users can easily understand the analysis results through visual graphs and text information and put them into practice the next time they drive.

[0997] Specific examples

[0998] If the emotion engine detects that a store staff member is feeling stressed while arranging shelves, the cloud server generates feedback such as "Take a slow, deep breath. Take a five-minute break," and displays it on the HMD.

[0999] Sensors collect data on the driver's sudden acceleration at traffic lights and send it from the device to a server. At the same time, if the emotion engine analyzes the driver's facial expressions and recognizes that the driver is feeling stressed, the server generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be better to listen to relaxing music while driving," and sends it to the smartphone.

[1000] Prompt Sentence Examples

[1001] "Analyze behavioral and emotional data to generate business improvement tips and advice based on emotional state."

[1002] "Staff are stressed, so please display a message encouraging them to take deep breaths to relax."

[1003] "A particular section of the sales floor is in disarray, please tell us to straighten up the displays."

[1004] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1005] Step 1:

[1006] When a user drives a vehicle or starts work, sensors, cameras, and microphones installed in the vehicle are activated to collect driving and emotional data. Inputs include the vehicle's speed, acceleration, braking, handling, and the user's facial expressions and tone of voice. The output is information collected from these data in real time. The specific operation of this step is that various sensor devices collect data while driving and send it to the user terminal in real time.

[1007] Step 2:

[1008] The collected driving data and emotion data are sent to a cloud server via the smartphone or HMD. At this time, the MQTT protocol is used to ensure data confidentiality and integrity. The input is the data collected in real time in step 1. The output is the transmitted driving data and emotion data. The specific operation of this step is for the smartphone or HMD to securely transmit the data to the cloud server using the MQTT protocol.

[1009] Step 3:

[1010] The cloud server automatically stores the received driving data and emotion data in a cloud database (e.g., Amazon RDS). The input is the data sent in step 2. The output is the driving data and emotion data stored in the cloud database. The specific operation of this step is that the server receives the data and stores it in the database.

[1011] Step 4:

[1012] The cloud server processes the stored data and performs analysis using artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis). The input is the driving data and emotion data stored in the cloud database. The output is the analysis results. The specific operations of this step are that the server converts the data into an appropriate format and uses the AI ​​model to identify driving patterns and emotional states.

[1013] Step 5:

[1014] The cloud server generates safe driving tips and individual driving improvement points based on the analysis results. The input is the analysis results obtained in step 4. The output is the generated feedback message. The specific operation of this step is to use the analysis results to generate feedback on driving and work improvements suitable for the user.

[1015] Step 6:

[1016] The generated feedback message is sent from the cloud server to the user's smartphone or HMD in the form of a push notification or text message. The input is the feedback message generated in step 5. The output is the feedback message sent to the user's device. The specific operation of this step is to send the feedback message from the server to the device.

[1017] Step 7:

[1018] The user checks the feedback displayed on the smartphone or HMD and puts it into practice the next time they drive or work. The input is the feedback message sent in step 6. The output is the feedback information received by the user. The specific operation of this step is for the user to check the feedback displayed on the device and reflect it in their driving or work.

[1019] 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.

[1020] 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.

[1021] 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.

[1022] [Fourth embodiment]

[1023] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1024] 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.

[1025] 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).

[1026] 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.

[1027] 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.

[1028] 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).

[1029] 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.

[1030] 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.

[1031] 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.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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."

[1036] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. This system is composed of sensors attached to the vehicle and a user terminal. The following describes in detail the embodiments of the present invention.

[1037] Data collection

[1038] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[1039] Data transmission

[1040] The devices transmit data collected from the sensors to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[1041] Data reception and storage

[1042] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[1043] Data analysis

[1044] The server uses AI models to analyze the received driving data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors.

[1045] Feedback Generation

[1046] Based on the analysis results, the server generates feedback such as safe driving tips and individual driving improvement points. The generated feedback is based on templates and includes specific, actionable advice.

[1047] Send Feedback

[1048] The generated feedback is sent from the server to the user's device via push notification or email.

[1049] Feedback Display

[1050] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific ways to improve their driving.

[1051] Specific examples

[1052] Here, we will explain a specific example in which a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by sensors and sent from the device to the server. The server analyzes this data and determines that sudden acceleration is having a negative impact on fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[1053] In this way, the present invention allows users to improve their driving skills in real time and increase fuel efficiency. In addition, the collected data can be used in cooperation with insurance companies to receive discounts on car insurance premiums.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[1057] Step 2:

[1058] The device transmits the buffered data to the server at regular intervals. Data transmission is performed in real time, and data confidentiality and integrity are ensured using a secure communication protocol (e.g., HTTPS).

[1059] Step 3:

[1060] The server receives the data sent from the terminal, stores the received data in a database, and adjusts the data format as necessary.

[1061] Step 4:

[1062] The server uses a generative AI model to analyze the stored data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and identify specific unsafe behaviors (e.g., sudden acceleration, hard braking, sharp turns).

[1063] Step 5:

[1064] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The feedback content is based on templates and includes specific, actionable advice.

[1065] Step 6:

[1066] The server sends the generated feedback to the user's device, and the feedback is delivered reliably via push notification or email.

[1067] Step 7:

[1068] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[1069] Example 1

[1070] 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."

[1071] Current vehicle driving data collection and analysis systems lack the ability to collect driving data in real time and provide effective feedback. Furthermore, they do not provide specific advice for driving improvement, preventing users from appropriately improving their driving skills. Furthermore, there is a need for systems that can efficiently analyze collected data and provide feedback to users while ensuring the confidentiality and integrity of the data.

[1072] 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.

[1073] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to a communication terminal, means for transmitting the driving data from the communication terminal to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the communication terminal, and means for displaying the feedback to the user on the communication terminal, thereby making it possible to collect driving data in real time, efficiently analyze it, and provide specific feedback for driving improvement.

[1074] "Driving data" is a general term for various data related to the driving state of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[1075] A "communication terminal" is a device used to transmit data collected from sensors installed in a vehicle to a server, and generally includes smartphones, tablets, in-vehicle computers, etc.

[1076] The "server" is a computer system that receives driving data transmitted from the communication terminal, analyzes the data, and generates feedback.

[1077] A "generative AI model" is an algorithm or program that uses machine learning and artificial intelligence techniques to analyze driving data, identify driving patterns, and pinpoint specific unsafe behaviors.

[1078] "Feedback" refers to specific advice on improving driving and hints for safe driving that are provided to the user based on the results of analyzing driving data.

[1079] "Visual graphs" are data display formats such as bar graphs, line graphs, and histograms that visually show operational data and analytical results.

[1080] A "text message" is a written message that provides analysis results and feedback to the user in text format.

[1081] A "driving pattern" indicates a tendency or characteristic of driving data under a specific time or situation, and includes, for example, actions such as sudden acceleration or sudden braking.

[1082] This system collects and analyzes vehicle driving data in real time, providing users with tips for safe driving and individual driving improvements. The system consists of sensors installed in the vehicle, a communication terminal for sending and receiving data, and a server for analyzing the data.

[1083] Data collection

[1084] First, driving data is collected by sensors attached to the user's vehicle. These sensors are connected to the vehicle's OBD-II port and capture real-time data such as speed, acceleration, braking, and handling. This data is then transmitted to a communication device via Bluetooth or Wi-Fi.

[1085] Data transmission

[1086] The communication terminal transmits the driving data collected from the sensors to the server using the HTTPS protocol, with SSL / TLS encryption applied to ensure data confidentiality and integrity.

[1087] Data reception and storage

[1088] The server receives the data sent from the communication terminal and temporarily stores it in its memory. The data is received in JSON format, and is then converted into an appropriate format and saved in a database (MySQL, PostgreSQL, etc.).

[1089] Data analysis

[1090] The server then analyzes the received driving data using a generative AI model to identify driving patterns and pinpoint specific unsafe behaviors (e.g., sudden acceleration, hard braking). This analysis uses machine learning frameworks such as TensorFlow and PyTorch.

[1091] Feedback Generation

[1092] Based on the analysis results, the server generates feedback, including tips for safe driving and individual driving improvements. The generated feedback is based on templates and includes specific, actionable advice. For example, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy."

[1093] Send Feedback

[1094] The generated feedback is sent from the server to the communication device via push notification, email, or other methods. The communication protocol used is Firebase Cloud Messaging (FCM) or the SMTP protocol.

[1095] Feedback Display

[1096] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to intuitively understand specific ways to improve their driving. For example, a graph showing driving improvement points or a text message such as "Number of sudden accelerations: 5 times / day" is displayed.

[1097] Specific examples

[1098] For example, consider a case where a user uses this system while commuting. Data on the user's sudden acceleration at traffic lights is collected by a sensor and sent from the communication device to the server. The server analyzes this data and determines that sudden acceleration is negatively impacting fuel economy. The server then generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy," and sends it to the communication device via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills and fuel efficiency.

[1099] Prompt Sentence Examples

[1100] Below are some example prompts for a generative AI model to analyze driving data and generate safe driving tips.

[1101] "We will provide you with your driving data in the following format to generate feedback for safe driving.

[1102] Speed: 50km / h

[1103] Acceleration: 3 m / s^2

[1104] Braking: 4 m / s^2

[1105] Handling: Left turn

[1106] Example of feedback to generate: "Next time you start at a traffic light, accelerate slowly over a 3-second period to improve fuel economy."

[1107] In this way, the present invention allows users to collect driving data in real time and receive specific feedback for improving their driving by analyzing it efficiently.

[1108] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1109] Step 1: Data collection

[1110] Sensors installed in the user's vehicle collect driving data, specifically speed, acceleration, braking, handling, and other data in real time, which is then transmitted to a communication device via Bluetooth.

[1111] Input: User's vehicle driving data

[1112] Output: Operation data sent to the communication terminal

[1113] Specific operation: The user drives the vehicle, and the sensor detects when the vehicle accelerates to 60 km / h and records this as acceleration data.

[1114] Step 2: Send data

[1115] The device transmits driving data collected from sensors to the server using a secure protocol (HTTPS), encrypted with SSL / TLS to ensure data integrity and confidentiality.

[1116] Input: Driving data sent to the terminal

[1117] Output: Driving data sent to the server

[1118] Specific operation: The terminal collects data and sends it to the server once per second in packets.

[1119] Step 3: Receiving and storing data

[1120] The server receives the data sent from the device, temporarily stores it in memory, then formats the data in JSON format appropriately and saves it to a database.

[1121] Input: Driving data sent to the server

[1122] Output: Operation data stored in the database

[1123] Specific operation: The server parses the received JSON data and inserts speed data, acceleration data, braking data, etc. into the corresponding columns in the database.

[1124] Step 4: Data analysis

[1125] The server analyzes the driving data stored in the database using a generative AI model. Specifically, it uses TensorFlow and PyTorch to analyze driving patterns and identify behaviors that pose safety issues.

[1126] Input: Operation data stored in the database

[1127] Output: Analysis results of driving patterns

[1128] How it works: The server inputs driving data into the AI ​​model and detects patterns of sudden acceleration. For example, it identifies that sudden acceleration is particularly common during rush hour on Mondays.

[1129] Step 5: Feedback generation

[1130] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback includes specific and actionable advice based on templates.

[1131] Input: Analysis results of driving patterns

[1132] Output: Generated feedback

[1133] Specific behavior: Feedback such as "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy" is generated.

[1134] Step 6: Send your feedback

[1135] The generated feedback is sent from the server to the communication device, and is delivered to the user's device via push notification or email.

[1136] Input: Generated feedback

[1137] Output: Feedback sent to the communication device

[1138] Specific operation: The feedback generated by the server is sent to the device as a push notification, which displays the message, "You can expect to improve fuel efficiency by refraining from sudden acceleration when starting from a traffic light."

[1139] Step 7: Feedback display

[1140] The communication device displays the received feedback to the user as visual graphs and text messages, allowing the user to review the feedback and understand specific ways to improve their driving.

[1141] Input: Feedback sent to communication terminal

[1142] Output: Feedback displayed as visual graphs and text messages

[1143] Specific operation: The device receives the feedback and displays a text message with a graph on the app screen, such as "Number of sudden accelerations: 5 times / day."

[1144] (Application example 1)

[1145] 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."

[1146] Although conventional driving data analysis systems exist that collect and analyze driving data to provide individual driving improvement points, it is difficult to support special vehicles such as self-driving vehicles. Furthermore, these systems were unable to provide real-time feedback or monitor the driving data of self-driving vehicles, which meant that improvements in driving safety and efficiency were not fully achieved.

[1147] 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.

[1148] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for analyzing the driving data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, means for displaying the feedback to the user on the terminal, and means for monitoring the driving data of the autonomous vehicle and providing feedback in real time, thereby enabling the user to monitor the driving data of the autonomous vehicle in real time and immediately receive safe driving tips and improvement points.

[1149] "Driving data" refers to various data relating to the driving state of the vehicle, such as the vehicle's speed, acceleration, braking, and handling.

[1150] An "autonomous vehicle" is a vehicle that can be driven automatically using dedicated hardware and software.

[1151] "Monitoring" is the act of continuously observing data in real time and detecting anomalies or specific patterns.

[1152] "Feedback" refers to hints and specific advice for improving driving that are generated based on the analysis results.

[1153] A "terminal" is a device that receives feedback and displays it to the user, such as a smartphone or tablet.

[1154] The "server" is a centralized management system for receiving, storing, analyzing, and transmitting the results of driving data.

[1155] "Analysis" is the process of deriving safe driving tips and areas for improvement based on collected driving data.

[1156] An "AI model" is an artificial intelligence technology algorithm that learns from large amounts of driving data and identifies and analyzes driving patterns.

[1157] "Real time" refers to a situation where processing and feedback are provided the moment an event occurs.

[1158] "Graph or text message" refers to a visual representation or textual information in a format that presents analysis results or feedback to a user.

[1159] This invention relates to a specific system for collecting driving data and providing users with safe driving tips and individual driving improvement points in real time. This system is composed of various sensors installed in vehicles, a terminal held by the user, and a server that analyzes the data.

[1160] Data collection

[1161] First, various sensors installed in the vehicle collect driving data in real time, including speed sensors, acceleration sensors, braking sensors, and handling sensors, which continuously record various data points generated while driving.

[1162] Data transmission

[1163] The driving data collected by the sensors is then transmitted to a server via the user's device (e.g., a smartphone), using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[1164] Data reception and storage

[1165] The server receives the driving data sent from the terminal and stores it in a database, where it is properly formatted before analysis to ensure data consistency.

[1166] Data analysis

[1167] The server uses machine learning and artificial intelligence (AI) models to analyze the stored driving data. These AI models identify driving patterns and identify unsafe driving behaviors and specific areas for improvement. Based on these analysis results, the server uses templates to generate specific, actionable feedback.

[1168] Feedback generation and submission

[1169] The generated feedback is instantly sent to the user's device, and includes specific tips for safe driving and specific areas for individual driving improvement.

[1170] Feedback Display

[1171] The user's device (such as a smartphone or tablet) displays the submitted feedback as visual graphs and text messages, allowing the user to easily understand specific ways to improve their driving.

[1172] Specific examples

[1173] As a concrete example, consider a situation where a user is driving an autonomous vehicle. For example, if the vehicle accelerates more than normal when going around a curve, this data is collected by sensors and sent to a server. The server analyzes this data and generates feedback suggesting that the vehicle handle the curve more gently. This feedback, such as "The next time you come around a curve, steering more gently will improve safety," is sent to the user's device as a push notification.

[1174] Prompt Sentence Examples

[1175] To give an example to the generative AI model, we use the following prompt:

[1176] Generate feedback like, "When the user was driving around a curve, the acceleration was too high, so next time you drive, you can improve safety by handling the curve more gently."

[1177] The present invention aims to promote safe driving and improve driving efficiency by monitoring driving data of an autonomous vehicle in real time and providing immediate feedback to the user.

[1178] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1179] Step 1:

[1180] Sensors installed in the user's vehicle collect driving data (speed, acceleration, braking, handling, etc.) in real time. Specifically, the various sensors detect data and format it for transmission to the user's device. The input is raw data from the sensors, and the output is formatted driving data.

[1181] Step 2:

[1182] The terminal transmits the collected driving data to the server using a secure communication protocol (e.g., HTTPS). In specific operations, the terminal receives the formatted data and transmits it to the server via the Internet. The input is the formatted driving data, and the output is the driving data transmitted to the server.

[1183] Step 3:

[1184] The server receives the driving data sent from the terminal and stores it in an internal database. Specifically, the server converts the received data into a format for proper analysis and writes it to the database. The input is the driving data sent to the server, and the output is the data stored in the database.

[1185] Step 4:

[1186] The server uses AI models to analyze the stored driving data. Specifically, the server applies machine learning algorithms to identify driving patterns and identify specific unsafe behaviors and areas for improvement. The input is the driving data stored in the database, and the output is the analyzed feedback information.

[1187] Step 5:

[1188] The server generates specific feedback based on the analysis results. Specifically, the server uses templates to generate text containing safe driving tips and driving improvement points. In this process, a generative AI model is used to create appropriate feedback according to the driving scenario. The input is the analysis results, and the output is the generated feedback.

[1189] Step 6:

[1190] The server sends the generated feedback to the user's device. Specifically, the server sends the feedback to the device by push notification or email. The input is the generated feedback, and the output is the feedback sent to the user's device.

[1191] Step 7:

[1192] The terminal receives the sent feedback and displays it to the user. Specifically, the terminal displays the feedback as a visual graph or a text message. The input is the feedback from the server, and the output is the feedback displayed to the user.

[1193] Step 8:

[1194] The user checks the displayed feedback and practices improvements to their driving. Specifically, the user understands the feedback and incorporates the safe driving tips and improvements into their next drive. The input is the feedback displayed on the device, and the output is the user's driving improvements.

[1195] In this way, by clarifying the specific operations performed at each processing step and their inputs and outputs, the flow of the entire system becomes easier to understand and implementation becomes easier.

[1196] 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.

[1197] The present invention is a system that collects and analyzes vehicle driving data in real time to provide drivers with safe driving tips and individual driving improvement points. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide more advanced feedback. This system is composed of sensors attached to the vehicle, a user's terminal, and an emotion engine. The following describes in detail the embodiments of the present invention.

[1198] Data collection

[1199] First, sensors installed in the user's vehicle collect driving data, including the vehicle's speed, acceleration, braking, handling, etc. As the user drives the vehicle, this data is transmitted to the device in real time.

[1200] Emotional Data Collection

[1201] Furthermore, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This data is also sent to the terminal in real time, just like the driving data.

[1202] Data transmission

[1203] The device transmits data collected from sensors and the emotion engine to a server in real time using a secure communication protocol to ensure data confidentiality and integrity.

[1204] Data reception and storage

[1205] The server receives the data sent from the device and stores it in a database, where it is properly formatted before being analyzed.

[1206] Data analysis

[1207] The server uses AI models to analyze the received driving and emotional data, using machine learning and artificial intelligence techniques, to identify driving patterns and pinpoint specific unsafe behaviors and emotional states.

[1208] Feedback Generation

[1209] Based on the analysis results, the server generates feedback such as tips for safe driving and individual driving improvement points. The generated feedback is adjusted to provide appropriate wording and advice based on the user's emotional state. For example, if the user is feeling stressed, the server will provide gentle, encouraging feedback.

[1210] Send Feedback

[1211] The generated feedback is sent from the server to the user's device via push notification or email.

[1212] Feedback Display

[1213] The device displays the received feedback to the user as visual graphs and text messages, helping the user understand specific driving improvement methods and tips based on their emotional state.

[1214] Specific examples

[1215] As a concrete example, let us consider a case where a user uses this system during their commute. Data on the user's sudden acceleration at a traffic light is collected by a sensor and sent from the device to the server. At the same time, the emotion engine analyzes the user's facial expressions and recognizes that the user is feeling stressed. The server analyzes the driving data and emotion data, determines that the sudden acceleration is negatively affecting fuel economy, and generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music while driving." The server then sends this feedback to the user's smartphone via a push notification. The user can check this feedback on their smartphone and put it into practice the next time they drive, thereby improving their driving skills, fuel efficiency, and stress reduction.

[1216] In this way, this invention allows users to improve their driving skills in real time and increase fuel efficiency. Furthermore, the collected data can be used in cooperation with insurance companies to provide discounts on car insurance grades. Providing feedback that takes into account the user's emotional state is expected to lead to more appropriate and effective driving improvements.

[1217] The processing flow will be explained below.

[1218] Step 1:

[1219] The device works in conjunction with sensors installed in the vehicle to continuously collect driving data such as speed, acceleration, braking, and handling. The device stores and buffers the collected data in temporary memory.

[1220] Step 2:

[1221] The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice, generating emotion data, which is also sent to the device and stored in memory.

[1222] Step 3:

[1223] The device transmits the buffered driving and emotion data to a server at regular intervals. Data transmission is performed in real time, using a secure communication protocol (e.g., HTTPS) to ensure data confidentiality and integrity.

[1224] Step 4:

[1225] The server receives the driving and emotion data sent from the device, stores the data in a database, and formats it appropriately before analyzing it.

[1226] Step 5:

[1227] The server uses a generative AI model to analyze the stored driving and emotional data. The AI ​​model uses machine learning and artificial intelligence techniques to identify driving patterns and the user's emotional state.

[1228] Step 6:

[1229] Based on the analysis results, the server generates safe driving tips and individual driving improvement points in text and graphic format. The generated feedback is adjusted to the appropriate wording and advice content based on the user's emotional state.

[1230] Step 7:

[1231] The server sends the generated feedback to the user's device via push notification or email.

[1232] Step 8:

[1233] The device displays the received feedback in a user interface, providing the user with visual graphs and text messages to improve their driving and provide tips.

[1234] Specific examples

[1235] As a specific example, a case where a user uses this system during his / her commute will be described.

[1236] Step 1:

[1237] The device collects speed and acceleration data when it suddenly accelerates at traffic lights.

[1238] Step 2:

[1239] The emotion engine analyzes the user's facial expressions and recognizes when the user is feeling stressed.

[1240] Step 3:

[1241] The device transmits driving data and emotion data to a server at regular intervals.

[1242] Step 4:

[1243] The server receives the transmitted driving data and emotion data and stores them in a database.

[1244] Step 5:

[1245] The server analyzes the driving data and emotional data and identifies that sudden acceleration is having a negative effect on fuel economy and that the user is feeling stressed.

[1246] Step 6:

[1247] The server generates feedback such as, "Next time you start at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be better to listen to relaxing music while driving."

[1248] Step 7:

[1249] The server sends the generated feedback to the user's smartphone via push notification.

[1250] Step 8:

[1251] The device displays the feedback on the smartphone and provides the user with specific tips on how to improve their driving and hints based on their emotions.

[1252] Example 2

[1253] 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."

[1254] Conventional driver assistance systems focus on collecting and analyzing driving data, but it is difficult to provide feedback that takes into account the driver's emotional state. As a result, they lack appropriate improvement advice based on the driver's actual attention state and stress, and are unable to maximize the effectiveness of safe driving. Furthermore, there is a lack of technological means to provide specific advice based on the driver's emotional state.

[1255] 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.

[1256] In this invention, the server includes means for collecting driving data, means for transmitting the collected driving data to the server, means for collecting emotion data, means for transmitting the collected emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide specific and effective safe driving advice according to the driver's emotional state by comprehensively analyzing both the driving data and the emotion data.

[1257] "Driving data" is a set of data relating to the driving of a vehicle, such as vehicle speed, acceleration, braking, and handling.

[1258] A "server" is a computing system for receiving, storing, and analyzing data, and generating and sending feedback.

[1259] The "terminal" is a device that transmits data collected from sensors and emotion engines installed in the vehicle to a server and displays feedback to the user.

[1260] A "sensor" is a measuring device used to collect driving data such as vehicle speed, acceleration, braking, and handling.

[1261] The "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize emotional data.

[1262] "Emotion data" is data that represents the user's emotional state, and is information collected from facial expressions, tone of voice, and the like.

[1263] A "generative AI model" is an algorithm or model that uses machine learning and artificial intelligence techniques to analyze data and generate feedback based on that data.

[1264] "Feedback" refers to safe driving tips and individual driving improvement points generated as a result of analyzing driving data and emotional data.

[1265] The present invention is a system that collects and analyzes driving data and emotion data, and provides hints for safe driving and driving improvement points. Below, specific embodiments of the invention will be described in detail.

[1266] Hardware and software used

[1267] The system consists of sensors installed in the vehicle, a device used by the user, a server that analyzes and stores data, and an emotion engine that recognizes the vehicle's emotional state. The sensors used include speed sensors, acceleration sensors, brake sensors, and handling sensors. The emotion engine uses a facial recognition camera and a voice analysis microphone. A generative AI model is also used to analyze the data.

[1268] Data collection

[1269] Sensors attached to the device collect driving data, including speed, acceleration, braking, and handling.

[1270] At the same time, the emotion engine collects the user's emotional data. The emotion engine uses cameras and microphones installed in the vehicle to analyze the user's facial expressions and tone of voice to recognize emotions. This allows the user's emotional state while driving to be collected in real time.

[1271] Data transmission

[1272] The device transmits the collected driving and emotion data to a server in real time, using secure communication protocols such as TLS / SSL to ensure data confidentiality and integrity.

[1273] Data reception and storage

[1274] The server receives data sent from the terminal and stores it in a database. The received data is first stored in a buffer area and then stored in the database according to a predefined format.

[1275] Data analysis

[1276] The server analyzes the stored driving and emotional data, using machine learning algorithms and generative AI models to extract features from the data and identify specific driving patterns and emotional states, such as frequent sudden braking or stressful facial expressions.

[1277] Feedback Generation

[1278] The server generates feedback based on the analysis results. This feedback includes suggestions for improving the user's driving skills and tips for safe driving. The generative AI model takes into account the user's emotional state and provides appropriate language and advice. For example, it may generate feedback such as, "The next time you start at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be a good idea to listen to relaxing music while driving."

[1279] Send Feedback

[1280] The server sends the generated feedback to the user's device via push notification or email. For example, Firebase Cloud Messaging (FCM) is used to send push notifications.

[1281] Feedback Display

[1282] The device displays the received feedback to the user as visual graphs or text messages, and the application provides the feedback to the user in summary or detailed views, such as a section titled "Driving Improvements" on the app's dashboard.

[1283] Specific examples

[1284] When a user uses this system during their commute, sensors collect data on sudden acceleration at traffic lights and send it from the device to the server. At the same time, an emotion engine analyzes the user's facial expressions and recognizes their stress level. The server then analyzes this data using an AI model to determine that sudden acceleration is negatively impacting fuel economy, and generates feedback such as, "Next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel economy. It would also be even better if you listen to relaxing music." This feedback is sent to the user's smartphone in real time and displayed as a push notification. The user can review this feedback and put it into practice the next time they drive to improve their driving technique, fuel efficiency, and reduce stress.

[1285] Specific prompt examples:

[1286] "Based on the user's driving data and emotional data, please analyze their driving patterns and generate tips for safe driving and improving fuel efficiency. Please also include advice that takes into account the user's stress level."

[1287] The above is a specific embodiment of the present invention. This system allows users to improve their driving skills in real time and increase fuel efficiency. In addition, by providing appropriate feedback according to the user's emotional state, more effective driving assistance can be achieved.

[1288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1289] Step 1:

[1290] The device collects driving data from sensors installed in the vehicle. The input data is the vehicle's speed, acceleration, braking, and handling. The data is sent from the sensors to the device in real time, and the device temporarily stores it in a buffer. Specifically, the speed sensor measures data in meters per second, and the acceleration sensor obtains data in m / s².

[1291] Step 2:

[1292] The terminal and emotion engine collect the user's emotional data through cameras and microphones installed inside the vehicle. The input is the user's facial expressions and tone of voice. The data is sent to the terminal in real time, and the terminal temporarily stores it in a buffer. The emotion engine uses facial expression recognition technology to recognize emotional states such as smiling or anger. It also uses voice analysis technology to analyze stress or relaxation from the tone of voice.

[1293] Step 3:

[1294] The terminal transmits the collected driving data and emotion data to the server in real time. The input is the driving data and emotion data stored in the buffer, and the output is the data sent to the server via a secure communication protocol. Specifically, the data is encrypted with TLS / SSL to ensure end-to-end security.

[1295] Step 4:

[1296] The server receives data sent from the device and stores it in a database. The input is driving data and emotion data sent from the device, and the output is data converted into a format that can be stored in the database. The received data is first stored in a buffer area, and then stored in the database according to a predefined format. For example, JSON format data is converted into an SQL database.

[1297] Step 5:

[1298] The server analyzes the stored driving data and emotional data using a generative AI model. The input is the driving data and emotional data stored in the database, and the output is the driving pattern and emotional state as the analysis results. Specifically, a machine learning algorithm is used to extract data features and identify specific driving patterns and emotional states. For example, if a high frequency of sudden braking is determined, this is identified as dangerous driving.

[1299] Step 6:

[1300] The server generates feedback based on the analysis results. The input is the driving pattern and emotional state obtained through the analysis, and the output is a feedback message to be provided to the user. The generative AI model creates advice based on driving improvements and the emotional state. For example, it generates a message such as, "The next time you start at a traffic light, accelerating slowly over three seconds will improve fuel efficiency. It would also be better to listen to relaxing music."

[1301] Step 7:

[1302] The server sends the generated feedback to the user's device. The input is the generated feedback message, and the output is the data to be sent to the user's device. The sending method can be push notification or email. For example, push notifications can be sent using Firebase Cloud Messaging (FCM).

[1303] Step 8:

[1304] The device receives feedback and displays it to the user as a visual graph or text message. The input is the feedback data sent from the server, and the output is the visual feedback information. A dedicated application provides the feedback to the user in a list or detailed view. For example, a section titled "Driving Improvements" may appear on the app's dashboard.

[1305] In this way, specific data processing and actions are performed at each step, providing the user with feedback based on driving improvements and emotional state.

[1306] (Application example 2)

[1307] 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."

[1308] Conventional driving assistance systems only analyze driving data, which means they are unable to provide feedback that takes into account the emotional state of the driver while driving. Furthermore, in store operations, there is a lack of a way to grasp the emotional state of staff in real time and provide appropriate support. As a result, stress on drivers and staff is not reduced, which prevents safe driving and improved work efficiency.

[1309] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for collecting driving data and emotion data, means for transmitting the collected driving data and emotion data to the server, means for analyzing the driving data and emotion data received by the server and generating safe driving tips and individual driving improvement points, means for transmitting the generated feedback to the terminal, and means for displaying the feedback to the user on the terminal. This makes it possible to provide feedback that takes into account the emotional state of the driver and store staff, thereby improving driving skills and work efficiency.

[1310] "Driving data" is information about vehicle behavior collected while driving, such as vehicle speed, acceleration, braking, and handling.

[1311] "Emotion data" is information relating to the emotional state of the user that is analyzed from facial expressions, tone of voice, and the like.

[1312] A "server" is a computer system for analyzing collected driving and emotion data and generating feedback.

[1313] An "artificial intelligence model" is an algorithm that uses techniques such as machine learning and deep learning to analyze data and identify specific patterns or conditions.

[1314] A "terminal" is a device through which a user receives feedback, such as a smartphone, tablet, or head-mounted display.

[1315] "Feedback" refers to hints and advice for improving driving provided based on the analysis results.

[1316] A "visual graph" is a graphic that displays the analysis results of driving data and emotion data in a format that is visually easy to understand.

[1317] "Text messages" are a means of conveying advice and hints based on analysis results to users in the form of text information.

[1318] The present invention is a system that collects and analyzes driving data and emotional data in real time, and provides drivers with hints for safe driving and feedback for driving improvement. This system is composed of sensors, cameras, and microphones attached to the vehicle, the user's smartphone or head-mounted display (HMD), and a cloud server. Specific embodiments of the present invention are described below.

[1319] Data collection

[1320] First, driving data is collected by various sensors installed in the vehicle (speed sensor, acceleration sensor, brake sensor, handling sensor, etc.), while emotion data is collected by analyzing the user's facial expressions and tone of voice using a camera and microphone.

[1321] Data transmission

[1322] The collected driving and emotion data is transmitted in real time via smartphone or HMD to a cloud server using the MQTT protocol to ensure data confidentiality and integrity.

[1323] Data reception and storage

[1324] The cloud server automatically stores the received driving and emotion data in a cloud database (e.g., Amazon RDS), where the data is formatted appropriately before analysis.

[1325] Data analysis

[1326] The cloud server uses artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis) to analyze the received data, which enables it to identify driving patterns and the user's emotional state and generate appropriate feedback based on driving technique and emotional state.

[1327] Feedback Generation

[1328] Based on the analysis results, the cloud server generates specific driving improvement tips and advice. This feedback is tailored to the user's emotional state. For example, if the user is feeling stressed, a gentle, encouraging message will be provided.

[1329] Send and view feedback

[1330] The generated feedback is sent from the cloud server to the user's smartphone or HMD in the form of push notifications or text messages. Users can easily understand the analysis results through visual graphs and text information and put them into practice the next time they drive.

[1331] Specific examples

[1332] If the emotion engine detects that a store staff member is feeling stressed while arranging shelves, the cloud server generates feedback such as "Take a slow, deep breath. Take a five-minute break," and displays it on the HMD.

[1333] Sensors collect data on the driver's sudden acceleration at traffic lights and send it from the device to a server. At the same time, if the emotion engine analyzes the driver's facial expressions and recognizes that the driver is feeling stressed, the server generates feedback such as, "The next time you accelerate at a traffic light, accelerate slowly over three seconds to improve fuel efficiency. It would also be better to listen to relaxing music while driving," and sends it to the smartphone.

[1334] Prompt Sentence Examples

[1335] "Analyze behavioral and emotional data to generate business improvement tips and advice based on emotional state."

[1336] "Staff are stressed, so please display a message encouraging them to take deep breaths to relax."

[1337] "A particular section of the sales floor is in disarray, please tell us to straighten up the displays."

[1338] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1339] Step 1:

[1340] When a user drives a vehicle or starts work, sensors, cameras, and microphones installed in the vehicle are activated to collect driving and emotional data. Inputs include the vehicle's speed, acceleration, braking, handling, and the user's facial expressions and tone of voice. The output is information collected from these data in real time. The specific operation of this step is that various sensor devices collect data while driving and send it to the user terminal in real time.

[1341] Step 2:

[1342] The collected driving data and emotion data are sent to a cloud server via the smartphone or HMD. At this time, the MQTT protocol is used to ensure data confidentiality and integrity. The input is the data collected in real time in step 1. The output is the transmitted driving data and emotion data. The specific operation of this step is for the smartphone or HMD to securely transmit the data to the cloud server using the MQTT protocol.

[1343] Step 3:

[1344] The cloud server automatically stores the received driving data and emotion data in a cloud database (e.g., Amazon RDS). The input is the data sent in step 2. The output is the driving data and emotion data stored in the cloud database. The specific operation of this step is that the server receives the data and stores it in the database.

[1345] Step 4:

[1346] The cloud server processes the stored data and performs analysis using artificial intelligence models (TensorFlow for driving data analysis, Amazon Rekognition for emotion analysis). The input is the driving data and emotion data stored in the cloud database. The output is the analysis results. The specific operations of this step are that the server converts the data into an appropriate format and uses the AI ​​model to identify driving patterns and emotional states.

[1347] Step 5:

[1348] The cloud server generates safe driving tips and individual driving improvement points based on the analysis results. The input is the analysis results obtained in step 4. The output is the generated feedback message. The specific operation of this step is to use the analysis results to generate feedback on driving and work improvements suitable for the user.

[1349] Step 6:

[1350] The generated feedback message is sent from the cloud server to the user's smartphone or HMD in the form of a push notification or text message. The input is the feedback message generated in step 5. The output is the feedback message sent to the user's device. The specific operation of this step is to send the feedback message from the server to the device.

[1351] Step 7:

[1352] The user checks the feedback displayed on the smartphone or HMD and puts it into practice the next time they drive or work. The input is the feedback message sent in step 6. The output is the feedback information received by the user. The specific operation of this step is for the user to check the feedback displayed on the device and reflect it in their driving or work.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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.

[1359] 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).

[1360] 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.

[1361] 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."

[1362] 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.

[1363] 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).

[1364] 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.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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.

[1371] 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.

[1372] 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.

[1373] 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.

[1374] The following is further disclosed regarding the above embodiment.

[1375] (Claim 1)

[1376] a means for collecting driving data;

[1377] means for transmitting the collected driving data to a server;

[1378] means for analyzing the driving data received at the server and generating safe driving tips and personalized driving improvements;

[1379] means for transmitting the generated feedback to the terminal;

[1380] means for displaying feedback to the user at the terminal;

[1381] A system including:

[1382] (Claim 2)

[1383] 10. The system of claim 1, wherein the analysis means uses an AI model to identify driving patterns.

[1384] (Claim 3)

[1385] 10. The system of claim 1, wherein the terminal displays safe driving tips and driving improvement points as visual graphs or text messages.

[1386] "Example 1"

[1387] (Claim 1)

[1388] a means for collecting driving data;

[1389] means for transmitting the collected driving data to a communication terminal;

[1390] means for transmitting driving data from the communication terminal to a server;

[1391] means for analyzing the driving data received at the server and generating safe driving tips and personalized driving improvements;

[1392] means for transmitting the generated feedback to the communication terminal;

[1393] means for displaying feedback to the user at the communication terminal;

[1394] A system including:

[1395] (Claim 2)

[1396] 10. The system of claim 1, wherein the analysis means uses a generative AI model to identify driving patterns and identify specific unsafe behaviors.

[1397] (Claim 3)

[1398] 10. The system of claim 1, wherein the communication terminal displays safe driving tips and driving improvement points as visual graphs or text messages.

[1399] "Application Example 1"

[1400] (Claim 1)

[1401] a means for collecting driving data;

[1402] means for transmitting the collected driving data to a server;

[1403] means for analyzing the driving data received at the server and generating safe driving tips and personalized driving improvements;

[1404] means for transmitting the generated feedback to the terminal;

[1405] means for displaying feedback to the user at the terminal;

[1406] A means for monitoring the driving data of the autonomous vehicle and providing feedback in real time; and

[1407] A system including:

[1408] (Claim 2)

[1409] 10. The system of claim 1, wherein the analysis means uses an AI model to identify driving patterns.

[1410] (Claim 3)

[1411] 10. The system of claim 1, wherein the terminal displays safe driving tips and driving improvement points as visual graphs or text messages.

[1412] "Example 2: Combining Emotion Engines"

[1413] (Claim 1)

[1414] a means for collecting driving data;

[1415] means for transmitting the collected driving data to a server;

[1416] a means for collecting emotion data;

[1417] means for transmitting the collected emotion data to a server;

[1418] a means for analyzing the driving data and emotion data received by the server and generating safe driving tips and personalized driving improvements;

[1419] means for transmitting the generated feedback to the terminal;

[1420] means for displaying feedback to the user at the terminal;

[1421] A system including:

[1422] (Claim 2)

[1423] 10. The system of claim 1, wherein the analysis means uses a generative model to identify driving patterns and emotional states.

[1424] (Claim 3)

[1425] 10. The system of claim 1, wherein the terminal displays driving improvement suggestions and emotional state-based advice as visual graphs or text messages.

[1426] "Application example 2 when combining emotion engines"

[1427] (Claim 1)

[1428] a means for collecting driving data and emotion data;

[1429] means for transmitting the collected driving data and emotion data to a server;

[1430] a means for analyzing the driving data and emotion data received by the server and generating safe driving tips and personalized driving improvements;

[1431] means for transmitting the generated feedback to the terminal;

[1432] means for displaying feedback to the user at the terminal;

[1433] A system including:

[1434] (Claim 2)

[1435] 10. The system of claim 1, wherein the analysis means uses an artificial intelligence model to identify driving patterns and emotional states.

[1436] (Claim 3)

[1437] 10. The system of claim 1, wherein the terminal displays safe driving tips and driving improvement areas as visual graphs or text messages, and provides messages that correspond to the user's emotional state. [Explanation of symbols]

[1438] 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 collecting driving data; means for transmitting the collected driving data to a server; means for analyzing the driving data received at the server and generating safe driving tips and personalized driving improvements; means for transmitting the generated feedback to the terminal; means for displaying feedback to the user at the terminal; A system including:

2. 10. The system of claim 1, wherein the analysis means uses an AI model to identify driving patterns.

3. 10. The system of claim 1, wherein the terminal displays safe driving tips and driving improvement points as visual graphs or text messages.

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