System

Abstract

A system is provided.SOLUTION: A system comprising: a sensor unit provided in a terminal and configured to acquire acceleration, speed, and position information of a vehicle; a data transmission unit configured to transmit the acquired sensor data to a server at regular intervals; a generation-type artificial intelligence unit provided in the server and configured to analyze the received sensor data in real time; a recommendation generation unit configured to transmit eco-driving advice generated by the generation-type artificial intelligence unit to the terminal; and a user interface unit configured to notify a user of the eco-driving advice transmitted by the recommendation generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In recent years, interest in global warming and environmental issues has grown, making the reduction of automobile CO2 emissions an important issue. However, there is often a lack of concrete guidelines or real-time feedback on how individual drivers should practice eco-driving. As a result, many drivers find it difficult to practice eco-driving, and as a result, the effectiveness of reducing CO2 emissions is limited. [Means for solving the problem]

[0005] The present invention solves the problem by providing optimal eco-driving guidelines and real-time feedback to individual drivers through a system that includes a sensor means provided in a terminal to acquire vehicle acceleration, speed, and position information and transmits this data to a server at regular intervals, a generative artificial intelligence means provided in the server to analyze the received sensor data in real time, a recommendation generation means that transmits eco-driving advice generated by the generative artificial intelligence means to the terminal, and a user interface means on the terminal that notifies the user of the advice.

[0006] Specifically, the user interface means has a dashboard that displays the analysis results of driving behavior, and is equipped with a notification means to promote gasoline cost reduction and safe driving awareness, thereby promoting understanding and practice of eco-driving among drivers. Furthermore, the data transmission means is equipped with a buffering and retry function that checks the success or failure of transmission and retransmits failed data, preventing data loss in the event of a communication error. This allows drivers to drive eco-friendly and contribute to reducing CO2 emissions.

[0007] "Terminal" refers to a mobile communication device carried by a user or a device installed in a vehicle that collects, transmits, and receives data.

[0008] "Sensor means" refers to a function including sensors for acquiring vehicle acceleration, speed, position information, etc., thereby collecting driving data in real time.

[0009] The "data transmission means" refers to a function for transmitting data collected by the sensor means to a server at regular intervals, and transmits and receives data using communication.

[0010] "Server" refers to a computer system that receives data from multiple terminals via a network and performs analysis using generative artificial intelligence.

[0011] "Generative artificial intelligence means" refers to a function that analyzes driving patterns and CO2 emissions in real time based on received data and generates optimal eco-driving advice.

[0012] "Recommendation generation means" refers to a function for generating specific eco-driving guidelines and advice for the user based on the analysis results obtained by the generative artificial intelligence means and sending them to the terminal.

[0013] "User interface means" refers to a function for providing the generated eco-driving advice and driving analysis results to the user through the screen or notification displayed on the terminal. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The system of the present invention combines a terminal carried by a user with a server to collect data while driving, analyze it in real time, and provide feedback. Hereinafter, embodiments of the present invention will be specifically described.

[0036] Sensor data collection

[0037] Device:

[0038] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[0039] Sending data

[0040] Device:

[0041] The collected sensor data is sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and a retransmission attempt is made.

[0042] Receiving and analyzing data

[0043] server:

[0044] The server receives sensor data sent from the device. The received data is stored in a database and analyzed in real time by generative artificial intelligence (AI). This analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[0045] Recommendation generation

[0046] server:

[0047] Based on the results of the generative AI analysis, optimal eco-driving recommendations are made to the driver, including avoiding sudden acceleration, maintaining an appropriate speed, and suggesting optimal routes, thereby helping the driver to drive in an environmentally friendly manner.

[0048] User Feedback

[0049] Device:

[0050] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[0051] Specific examples of use

[0052] User:

[0053] The user launches the application and drives the car as usual. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is completed, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[0054] In this way, the system of the present invention helps users practice eco-driving on a daily basis, contributing to reducing environmental impact. In particular, real-time coaching and post-driving analysis feedback are expected to raise driver awareness and lead to sustained improvement.

[0055] The processing flow will be explained below.

[0056] Step 1: Start collecting data

[0057] Terminal: When the application is launched, the sensor means in the terminal starts operating. Specifically, the acceleration sensor, speed sensor, and GPS module acquire the vehicle's acceleration, speed, and position information in real time. This data is temporarily stored in a buffer.

[0058] Step 2: Send data

[0059] Terminal: At regular intervals (for example, every 5 seconds), the collected sensor data is sent to the server. If the transmission is successful, the buffer is cleared. If the transmission fails, the data is kept in the buffer and will be retried the next time it is sent.

[0060] Step 3: Receiving data

[0061] Server: Receives sensor data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and stores it in a database if there are no problems.

[0062] Step 4: Data analysis

[0063] Server: A generative artificial intelligence solution analyzes the stored sensor data in real time, running algorithms to calculate driving patterns, CO2 emissions, and driving efficiency, and assessing each data point.

[0064] Step 5: Recommendation generation

[0065] Server: Based on the analysis results, the server generates optimal eco-driving recommendations for the driver. For example, if there is a lot of sudden acceleration, the server generates advice such as "Avoiding sudden acceleration will improve fuel efficiency." If the speed exceeds the appropriate range, the server generates a notification saying, "Reducing speed will reduce CO2 emissions."

[0066] Step 6: Sending recommendations

[0067] Server: The generated recommendations are sent to the device, including comparisons with past driving data and trend analysis results, to provide information in a format that is easy for the user to understand.

[0068] Step 7: Receive and display recommendations

[0069] Device: Receives recommendations sent from the server. Real-time feedback is displayed to the user via pop-up notifications and in-app interfaces. Notifications include specific courses of action and information on what can be improved.

[0070] Step 8: View the dashboard

[0071] Device: After completing a drive, the analysis results are stored on the app's dashboard. Users can check this dashboard to see their overall driving performance, comparisons with past driving data, and trends in CO2 emissions and fuel efficiency. This feedback helps raise awareness of eco-driving.

[0072] Step 9: User Actions

[0073] User: Check the displayed recommendations and analysis results and try to practice eco-driving the next time you drive. For example, you are asked to adjust your driving style by avoiding sudden acceleration or reviewing your route.

[0074] These steps allow users to receive real-time feedback to improve their driving behavior, enabling them to continuously practice eco-driving.

[0075] Example 1

[0076] 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."

[0077] In conventional eco-driving support systems, the functions of data collection, transmission, analysis, and feedback are separated, making it difficult to provide appropriate coaching and feedback on analysis results in real time. Furthermore, there is a possibility of transmission data being lost, making reliable data collection and accurate analysis difficult. These issues need to be resolved.

[0078] 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.

[0079] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, a recommendation generation means for sending the generated eco-driving advice to the terminal, and a means for calculating driving patterns, CO2 emissions, and driving efficiency, thereby enabling appropriate coaching and feedback of analysis results in real time.

[0080] A "terminal" is a device held by a user, including a smartphone or an in-vehicle device, that acquires acceleration, speed, and position information while driving.

[0081] "Sensor means" refers to a device that collects vehicle driving data using an acceleration sensor, speed sensor, GPS module, etc. built into the terminal.

[0082] "Data transmission means" refers to the function and protocol for transmitting sensor data collected by the terminal to the server at regular intervals.

[0083] "Server" refers to a remote computing system that receives and analyzes sensor data.

[0084] The "generative artificial intelligence means" is an artificial intelligence system installed on a server that analyzes received sensor data in real time and calculates driving patterns, CO2 emissions, and driving efficiency.

[0085] The "recommendation generation means" is a function that generates eco-driving advice based on the analysis results of the generative artificial intelligence means and sends it to the terminal.

[0086] The "user interface means" is a display and operation means for notifying the user of eco-driving advice and analysis results on the terminal.

[0087] The "buffering means" is a function that temporarily stores sensor data when transmission fails and attempts to retransmit it at the next transmission timing.

[0088] The "retry function" is a function for retransmitting data that has previously failed to be transmitted.

[0089] "Driving patterns" refer to the driving tendencies and behavioral patterns of a vehicle derived from collected sensor data.

[0090] "CO2 emissions" is an indicator that shows the amount of carbon dioxide emitted during operation and is used to measure environmental impact.

[0091] "Driving efficiency" is an index that indicates fuel efficiency and energy efficiency calculated based on driving data.

[0092] The present invention is a system that utilizes a user's terminal and a server to collect data while driving, analyze it in real time, and provide feedback. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the preferred embodiments.

[0093] Hardware and Software Configuration

[0094] Terminal

[0095] The device is a smartphone or an in-car device carried by the user, and is equipped with the following hardware:

[0096] Acceleration sensor: Measures acceleration and deceleration.

[0097] Speed ​​sensor: Measures the vehicle's speed.

[0098] GPS module: Obtains current location information.

[0099] Applications installed on the device collect data from these sensors and temporarily store it in a buffer. For example, when a user slams on the brakes, the accelerometer detects this and stores the data in a buffer.

[0100] server

[0101] The server is installed on the cloud and has the following functions:

[0102] Generative artificial intelligence means: Analyzes received sensor data in real time to calculate driving patterns, CO2 emissions, and driving efficiency.

[0103] Recommendation generation means: Based on the analysis results of the generative artificial intelligence means, eco-driving advice is generated and sent to the terminal.

[0104] For example, an instruction to "avoid sudden acceleration" is sent from the server to the user's terminal.

[0105] Data processing and calculation

[0106] Data transmission method

[0107] The device sends the sensor data stored in the buffer to the server at regular intervals. The device checks the Internet connection and sends the data if a connection is established. If the transmission fails, the device stores the data in the buffer and retries at the next transmission opportunity. For example, even if the connection is lost while driving, the data will be sent again after exiting a tunnel.

[0108] Data reception and analysis

[0109] The server receives sensor data sent from the device and stores it in a database. The received data is analyzed in real time by generative artificial intelligence (AI), which calculates driving patterns, CO2 emissions, and driving efficiency. For example, if the server detects a pattern of "frequent sudden acceleration while driving," it will prepare an alert based on that.

[0110] User Interface Means

[0111] The recommendations and analysis results sent from the server are notified to the user via the device application. This information is displayed visually in the user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard. For example, a notification saying "Avoiding sudden acceleration will improve fuel efficiency" pops up on the screen while driving.

[0112] Examples and prompts

[0113] Examples:

[0114] Suppose a user launches the application and drives a car. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is over, the app's dashboard displays the CO2 emissions and fuel efficiency of the driving session, allowing the user to review their driving.

[0115] Example prompt sentence:

[0116] "Generate optimal eco-driving recommendations for users based on driving data"

[0117] "It analyzes the latest sensor data in real time and provides advice to avoid sudden acceleration."

[0118] The above is a specific embodiment of the present invention. The system of the present invention is expected to help users practice eco-driving on a daily basis and contribute to reducing the burden on the environment.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] Sensor data collection

[0122] input:

[0123] The user starts an application installed on a smartphone or an in-car terminal.

[0124] Specific operation:

[0125] The application initializes the device's accelerometer, speed sensor, and GPS module and begins collecting data.

[0126] The sensors collect acceleration, speed, and position information in real time while driving.

[0127] output:

[0128] The acquired sensor data is temporarily stored in a buffer. For example, when a user suddenly brakes, the acceleration sensor detects it and stores the data in the buffer.

[0129] Step 2:

[0130] Sending data

[0131] input:

[0132] Buffered sensor data

[0133] Specific operation:

[0134] At regular intervals, the device checks for an internet connection.

[0135] If a connection is established, it sends the buffered sensor data to the server.

[0136] If the transmission fails, the data is stored in a buffer and a retransmission attempt is made at the next transmission opportunity.

[0137] output:

[0138] Sensor data is sent to the server. For example, driving data for the past minute is sent to the server.

[0139] Step 3:

[0140] Receiving data

[0141] input:

[0142] Sensor data sent from the device

[0143] Specific operation:

[0144] The server provides an API for receiving data.

[0145] The received data is first stored in temporary storage and then stored in a database.

[0146] output:

[0147] Sensor data stored in a database, for example data about the vehicle's last driving session.

[0148] Step 4:

[0149] Analyzing the data

[0150] input:

[0151] Sensor data stored in a database

[0152] Specific operation:

[0153] The server uses generative artificial intelligence to analyze the stored sensor data in real time.

[0154] The analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[0155] output:

[0156] Analysis results from generative artificial intelligence, such as frequency of sudden acceleration and fuel efficiency.

[0157] Step 5:

[0158] Recommendation generation

[0159] input:

[0160] Analysis results using generative artificial intelligence

[0161] Specific operation:

[0162] Based on the analysis results of generative artificial intelligence, optimal eco-driving recommendations are generated.

[0163] Recommendations include avoiding sudden acceleration, maintaining an appropriate speed, and suggesting the best route.

[0164] Send recommendations to the device.

[0165] output:

[0166] Eco-driving recommendations, such as "Avoiding sudden acceleration will improve fuel efficiency."

[0167] Step 6:

[0168] Providing feedback

[0169] input:

[0170] Eco-driving recommendations and analysis results sent from the server

[0171] Specific operation:

[0172] The terminal receives the recommendations and analysis results from the server.

[0173] The application visually displays this information in a user interface.

[0174] Eco-driving advice is displayed as pop-up notifications while you're driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[0175] output:

[0176] Feedback to the user, such as a notification that pops up on the screen while driving saying, "Avoiding sudden acceleration will improve fuel efficiency."

[0177] (Application example 1)

[0178] 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."

[0179] Conventional systems make it difficult for drivers to receive appropriate eco-driving advice in real time, and lack specific guidance on how to improve fuel efficiency and reduce CO2 emissions. Furthermore, they do not provide sufficient analysis results or feedback after driving, which prevents drivers from raising their awareness to continuously drive in an eco-friendly manner.

[0180] 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.

[0181] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, an analysis means for calculating driving patterns, CO2 emissions, and driving efficiency in real time using a generative artificial intelligence model, and a recommendation generation means for sending eco-driving advice generated by the generative artificial intelligence means to the terminal. This makes it possible to provide appropriate eco-driving advice to the driver in real time and display detailed analysis results and feedback in an easy-to-understand manner even after driving has ended.

[0182] The "sensor means" refers to a sensor built into the terminal, which has the function of acquiring information on the acceleration, speed, and position of the vehicle.

[0183] The "data transmission means" is a device or processing process that has the function of transmitting acquired sensor data to a server at regular intervals.

[0184] A "generative artificial intelligence means" is a system installed on a server that analyzes received sensor data in real time and uses a generative AI model to calculate driving patterns, driving efficiency, etc.

[0185] The "recommendation generating means" is a device or processing process having a function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[0186] The "user interface means" is a device or software that provides an interface for notifying the user of the eco-driving advice sent by the recommendation generation means.

[0187] The "pop-up notification means" is a device or process that has the function of displaying eco-driving advice as a pop-up notification on the screen of the terminal at regular intervals.

[0188] "Analysis means" refers to a device or process that has the function of calculating driving patterns, CO2 emissions, and driving efficiency in real time from received driving data using a generative artificial intelligence model.

[0189] The system of the present invention functions by combining a terminal held by the user with a server. The terminal is equipped with sensor means necessary to acquire vehicle acceleration, speed, and position information, and has data transmission means for transmitting this sensor data to the server at regular intervals. The server is equipped with generative artificial intelligence means that uses a generative artificial intelligence (AI) model to analyze the received sensor data in real time. Eco-driving advice generated by the generative artificial intelligence means is transmitted from the server to the terminal.

[0190] The user's terminal notifies the received eco-driving advice through a user interface means, and provides the content of the advice to the user. This user interface means includes a pop-up notification means for visually displaying notifications while driving, and also includes a dashboard for checking analysis results and reports after driving.

[0191] As a concrete example, when a user starts driving, the device's sensor means collects data such as acceleration, speed, and GPS information. This collected data is temporarily stored in the device's buffer and periodically sent to a server via the Internet. The server then analyzes this data in real time using a generative AI model to calculate driving patterns, CO2 emissions, and driving efficiency.

[0192] Based on the analysis results, the server's generative artificial intelligence means generates eco-driving advice and sends it to the device. The user's device's pop-up notification means displays advice such as "Avoiding sudden acceleration will improve fuel efficiency by 15% and reduce CO2 emissions by 50g" as a pop-up notification while driving. After driving, the dashboard displays detailed analysis results and reports such as "CO2 emissions for this session were 500g."

[0193] The hardware used includes smartphones and in-vehicle terminals, which have built-in acceleration sensors, speed sensors, and GPS modules, while the software used includes data transmission means, generative artificial intelligence means, pop-up notification means, and user interface means including a dashboard.

[0194] An example of a prompt is:

[0195] prompt:

[0196] Analyze the following driving data, calculate driving patterns, CO2 emissions, and driving efficiency, and output recommendations for optimal eco-driving.

[0197] Driving data:

[0198] [

[0199] {"timestamp": 1660000000, "acceleration": -1.2, "speed": 60, "gps": {"latitude": 35.6895, "longitude": 139.6917}},

[0200] {"timestamp": 1660000600, "acceleration": 0.8, "speed": 70, "gps": {"latitude": 35.6900, "longitude": 139.6920}},

[0201] / / Additional data follows

[0202] ]

[0203] request:

[0204] Driving Pattern

[0205] CO2 emissions

[0206] Operating efficiency

[0207] Eco-driving recommendations (avoiding sudden acceleration, appropriate speed, suggesting optimal routes)

[0208] Let's say.

[0209] This system allows users to practice eco-driving on a daily basis and contribute to reducing environmental impact. It also provides detailed feedback during and after driving, which helps to continuously improve driver behavior and raise awareness.

[0210] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0211] Step 1:

[0212] The user launches an application on their smartphone or in-vehicle device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, and uses these sensors to collect driving data. The device temporarily stores the acquired acceleration, speed, and location information in a buffer.

[0213] Input: Application launch by user operation, sensor data (acceleration, speed, position information).

[0214] Data processing: Collecting data from sensor means and temporarily storing it in a buffer.

[0215] Output: Sensor data in buffer.

[0216] Step 2:

[0217] The terminal extracts the sensor data from the buffer at regular intervals and transmits it to the server via the Internet. If the connection is not established, the data transmission means attempts to retransmit.

[0218] Input: Buffered sensor data, internet connection status.

[0219] Data manipulation: Extracting data from buffers, sending it over the internet, and attempting to resend it.

[0220] Output: Sensor data sent to the server.

[0221] Step 3:

[0222] The server receives the sensor data sent from the device and stores it in a database, where it is analyzed in real time by generative artificial intelligence means.

[0223] Input: Sensor data sent from the device.

[0224] Data processing: Storage in database, real-time analysis using generative AI models.

[0225] Output: driving patterns, CO2 emissions, driving efficiency.

[0226] Step 4:

[0227] The generative artificial intelligence means calculates driving patterns, CO2 emissions, and driving efficiency using a generative AI model. Based on this analysis, optimal eco-driving advice is generated and sent to the terminal by the recommendation generation means.

[0228] Input: Driving data from a generative AI model.

[0229] Data processing: Calculation of driving patterns, CO2 emissions, driving efficiency, and generation of eco-driving advice.

[0230] Output: Advice data.

[0231] Step 5:

[0232] The user terminal receives the eco-driving advice sent from the server and notifies the user in real time using a pop-up notification means, particularly by displaying advice on how to avoid sudden acceleration while driving using a pop-up notification.

[0233] Input: Advice data from the server.

[0234] Data processing: Visual presentation of advice data.

[0235] Output: Advice displayed as a popup notification.

[0236] Step 6:

[0237] After the user has finished driving, the device displays an analysis of the entire driving session on a dashboard of the user interface means, where CO2 emissions and fuel efficiency are displayed as graphs and detailed analysis results.

[0238] Input: Driving session data, analysis results from the server.

[0239] Data processing: Data aggregation and graphing, and visualization of analysis results.

[0240] Output: Analysis results and reports on dashboards.

[0241] In this way, the system of the present invention performs specific processing at each step, supporting effective eco-driving in real time.

[0242] 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.

[0243] The system of the present invention is composed of a user-held device, a server, and an emotion engine that recognizes the user's emotions. This system collects data while driving, analyzes it in real time, and provides feedback based on the user's emotions, supporting more effective eco-driving.

[0244] Sensor data collection

[0245] Device:

[0246] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[0247] Collecting Emotional Data

[0248] Device:

[0249] The emotion engine means acquires emotion data by analyzing the user's facial expressions, voice, body temperature, heart rate, etc. For example, it uses a front camera to recognize the user's facial expressions, a microphone to analyze the tone and tension of the voice, and collects body temperature and heart rate from a smartwatch or other biometric sensors.

[0250] Sending data

[0251] Device:

[0252] The collected sensor data and emotion data are sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and will be retried the next time it is sent.

[0253] Receiving and analyzing data

[0254] server:

[0255] The server receives sensor data and emotion data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and, if there are no problems, stores it in a database. Generative artificial intelligence (AI) uses this data to analyze driving patterns, CO2 emissions, and driving efficiency in real time. At the same time, the user's emotion data is also incorporated into the analysis.

[0256] Recommendation generation

[0257] server:

[0258] Based on the analysis results of the generative artificial intelligence, the system generates recommendations for optimal eco-driving for the driver. What is noteworthy here is that the system also takes into account the user's emotional data obtained by the emotion engine means. For example, if the user is feeling stressed, the system will provide advice on driving methods that will reduce stress and suggest relaxation techniques.

[0259] Sending recommendations

[0260] server:

[0261] The generated recommendations are sent to the device, and information is provided in an easy-to-understand format, including comparisons with past driving data, trend analysis results, and user emotional data.

[0262] User Feedback

[0263] Device:

[0264] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the in-app dashboard. Real-time advice tailored to specific emotional states is also provided.

[0265] Specific examples of use

[0266] User:

[0267] The user launches the application and drives the car as usual. While driving, the device collects sensor data and emotional data, which it periodically sends to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. If the user feels stressed, the server also provides relaxation music and advice on driving techniques. After the drive is over, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[0268] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[0269] The processing flow will be explained below.

[0270] Step 1: Launching the Application

[0271] User: Launches the application installed on a smartphone or in-car device. When the application is launched, the sensor and emotion engine automatically enter standby mode.

[0272] Step 2: Start collecting sensor data

[0273] Terminal: The sensor means in the application starts to operate and acquires the vehicle's acceleration, speed and position information in real time from the acceleration sensor, speed sensor and GPS module. This data is temporarily stored in a buffer.

[0274] Step 3: Collecting emotion data

[0275] Device: The emotion engine uses sensor information from the front camera, microphone, smartwatch, etc. to recognize the user's emotions. It analyzes the user's facial expressions, voice, body temperature, and heart rate to generate emotion data, which is also stored in a buffer.

[0276] Step 4: Sending data

[0277] Terminal: Collected sensor data and emotion data are sent to the server at regular intervals. After an internet connection is confirmed, the data is sent to the server, and if the transmission is successful, the buffer is cleared. If the transmission fails, the data remains in the buffer and is retried the next time it is sent.

[0278] Step 5: Receiving and storing data

[0279] Server: Receives sensor data and emotion data sent from the device. The received data includes metadata such as timestamps and device IDs, and performs data integrity checks. If there are no problems, the data is stored in a database.

[0280] Step 6: Analyze data in real time

[0281] Server: Generative artificial intelligence (AI) analyzes the received sensor data and emotional data in real time. Specifically, it analyzes driving patterns, CO2 emissions, and driving efficiency, and also takes into account the user's emotional state to provide a comprehensive driving evaluation.

[0282] Step 7: Generate eco-driving recommendations

[0283] Server: Based on the analysis results of generative AI, the server generates optimal eco-driving recommendations for the user. For example, if there is a lot of unnecessary acceleration, the server will notify the user by saying, "Avoiding sudden acceleration will improve fuel efficiency," and if the user is feeling stressed, the server will suggest, "Play music for relaxation."

[0284] Step 8: Sending recommendations

[0285] Server: Sends the generated recommendations and analysis results to the device, including comparisons with past driving data, trend analysis, and information corresponding to the user's emotional state.

[0286] Step 9: Receive and display recommendations

[0287] Device: Receives recommendations and analysis results sent from the server. While driving, real-time feedback is displayed as pop-up notifications, and detailed analysis results can be viewed on the in-app dashboard.

[0288] Step 10: Driving results feedback

[0289] User: After completing a drive, the app's dashboard displays graphs of CO2 emissions and fuel efficiency for each session, allowing users to review their driving and identify areas for improvement for their next drive.

[0290] Through these steps, the system provides users with eco-driving advice that adapts in real time and promotes continuous improvement. By utilizing the emotion engine, it is also possible to provide personalized feedback based on the user's emotions. This makes it easier for drivers to practice eco-friendly driving and contributes to reducing CO2 emissions.

[0291] Example 2

[0292] 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."

[0293] Conventional eco-driving support systems aim to improve driving efficiency based on sensor data such as vehicle acceleration, speed, and location information, but they are unable to provide recommendation functions that take the user's emotional state into account. As a result, they ignore the impact of user stress and fatigue on driving efficiency, making it difficult to improve overall driving quality. Furthermore, conventional systems lacked reliability due to insufficient data integrity and retransmission processing in the event of a transmission failure.

[0294] 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.

[0295] In this invention, the server includes a generative artificial intelligence means for analyzing the received sensor data and emotional data in real time, a recommendation generation means for transmitting eco-driving advice generated by the generative artificial intelligence means to the terminal, and a means for storing the received sensor data and emotional data in a database. This enables comprehensive driving assistance that takes the user's emotional data into consideration, thereby simultaneously improving driving efficiency and reducing stress.

[0296] A "terminal" is a device used by a user, such as a sensor mounted on a vehicle or a smartphone, that has the function of acquiring and transmitting sensor data and emotion data.

[0297] "Sensor means" refers to a device or module for acquiring vehicle acceleration, speed, and position information, and collects this data in real time.

[0298] The "emotion engine means" is a function for acquiring emotion data by analyzing the user's facial expression, voice, body temperature, heart rate, etc., and for measuring and evaluating the user's psychological state.

[0299] The "data transmission means" is a function for transmitting collected sensor data and emotion data to a server at regular intervals, and transmits data using an internet connection.

[0300] "Generative artificial intelligence means" refers to an AI model that analyzes received sensor data and emotional data in real time to evaluate driving patterns and driving efficiency.

[0301] The "recommendation generating means" is a function for generating eco-driving advice for the driver based on the data analyzed by the generative artificial intelligence means and transmitting it to the terminal.

[0302] The "user interface means" is a function for visualizing and providing eco-driving advice and analysis results to the user on the terminal, and is displayed as notifications or a dashboard.

[0303] This invention is a system that supports eco-driving by analyzing the user's driving behavior and emotional state in real time using a user's terminal and a server. Specifically, it comprises sensor means for acquiring vehicle acceleration, speed, and position information, emotion engine means for acquiring the user's facial expression, voice, body temperature, heart rate, etc., and data transmission means for transmitting this data to the server at regular intervals.

[0304] Hardware and Software Details

[0305] A device is a device that can be easily accessed by a user, such as a smartphone, tablet, or in-car device. The device is equipped with an accelerometer, speed sensor, GPS module, camera, microphone, smartwatch, etc., and uses these to collect data.

[0306] Examples:

[0307] Car driving data and user facial expression data are collected using a smartphone.

[0308] The smartwatch is used to measure the user's heart rate and body temperature.

[0309] Acquiring and Sending Data

[0310] The device temporarily stores the acquired sensor data and emotion data in a buffer, and after checking the Internet connection, transmits it to the server at regular intervals. If the transmission fails, the data is kept in a local buffer and is retried the next time it is transmitted.

[0311] Data reception and analysis

[0312] The server receives sensor data and emotion data sent from the device and analyzes it in real time using generative artificial intelligence, including analysis of driving patterns, CO2 emissions, driving efficiency, etc. It also incorporates the user's emotion data to provide a comprehensive evaluation.

[0313] Generating and sending recommendations

[0314] The server generates eco-driving recommendations based on the analysis results. These recommendations include advice on how to avoid sudden acceleration, how to improve driving efficiency, and how to reduce user stress. The generated recommendations are sent from the server to the device.

[0315] User Feedback

[0316] The device notifies the user of the recommendations and analysis results sent from the server. This information is presented visually through a user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[0317] Examples:

[0318] While driving, a pop-up notification will appear saying, "Avoiding sudden acceleration will improve fuel efficiency."

[0319] After completing a drive, check the app's dashboard to see the CO2 emissions and fuel efficiency of that driving session.

[0320] Prompt Sentence Examples

[0321] "What's the trick to avoiding sudden acceleration?"

[0322] "Tell me how to relax when you're feeling stressed"

[0323] "Show me the latest CO2 emissions data"

[0324] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[0325] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0326] Step 1:

[0327] The user launches the application.

[0328] Specific operation: Tap to launch an application on your smartphone or in-car device.

[0329] Input: User actions.

[0330] Output: The startup status of the application.

[0331] Step 2:

[0332] The terminal starts collecting data using the sensor means.

[0333] Specific operation: The acceleration sensor, speed sensor, and GPS module in the device are activated to collect data in real time.

[0334] Input: Data obtained from the device's sensors (acceleration, speed, location information).

[0335] Output: Temporarily buffered sensor data.

[0336] Step 3:

[0337] The terminal begins collecting emotion data using the emotion engine means.

[0338] Specific operation: The front camera captures the user's facial expressions, the microphone analyzes the tone and tension of the voice, and the smartwatch collects body temperature and heart rate data.

[0339] Input: Camera footage, audio data, body temperature, heart rate.

[0340] Output: Emotion data temporarily stored in a buffer.

[0341] Step 4:

[0342] The terminal stores the collected sensor data and emotion data in a buffer and transmits them to the server using the data transmission means.

[0343] What it does: Checks for internet connectivity and sends data if connection is established. If the transmission fails, it buffers the data and tries to send it again.

[0344] Input: Buffered sensor data and emotion data.

[0345] Output: Data sent to the server or held in the buffer.

[0346] Step 5:

[0347] The server receives the sensor data and emotion data transmitted from the terminal.

[0348] Specific operation: The server receives the data and checks the metadata such as the timestamp and device ID.

[0349] Input: Sensor data and emotion data sent from the device.

[0350] Output: Data received, data checked for integrity.

[0351] Step 6:

[0352] The server uses generative artificial intelligence tools to analyze the data in real time.

[0353] Specific operation: Using an AI model, the system analyzes driving patterns, CO2 emissions, driving efficiency, etc., and makes a comprehensive assessment incorporating emotional data.

[0354] Input: Received sensor data and emotion data.

[0355] Output: Analysis results (driving patterns, CO2 emissions, driving efficiency, emotional state).

[0356] Step 7:

[0357] Based on the analysis results, the server generates recommendations for eco-driving.

[0358] Specific actions: Based on the analysis results, advice to improve driving efficiency and suggestions to reduce stress are generated.

[0359] Input: Analysis results.

[0360] Output: The generated recommendations.

[0361] Step 8:

[0362] The server transmits the generated recommendations to the terminal.

[0363] Specific operation: Recommendations are sent to the device and information is provided in a visually easy-to-understand format.

[0364] Input: Generated recommendations.

[0365] Output: Recommendations sent to the device.

[0366] Step 9:

[0367] The terminal notifies the user of the recommendation using a user interface means.

[0368] Specific operation: Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[0369] Input: Recommendations sent from the server.

[0370] Output: Notifications and dashboard display to the user.

[0371] (Application example 2)

[0372] 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."

[0373] Conventional eco-driving support systems only provide advice based on the user's driving behavior and are unable to provide personalized feedback that takes into account the user's emotional state. As a result, there has been a lack of support for reducing the stress and anxiety that users feel while driving and maintaining a comfortable driving environment. The present invention aims to combine driving data and emotional data to provide users with optimal eco-driving advice and personalized feedback based on their emotions, thereby improving driving efficiency and maintaining a comfortable driving environment.

[0374] The identification process by the identification 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 generative artificial intelligence means that analyzes received sensor data and emotion data in real time, recommendation generation means that transmits generated eco-driving advice to the terminal, and user interface means that notifies the user of the transmitted eco-driving advice. This makes it possible to simultaneously provide the user with feedback to improve their driving behavior and relax their emotions.

[0375] "Sensor means" refers to a device for acquiring information on the acceleration, speed, and position of a vehicle.

[0376] "Data transmission means" refers to a device or function for transmitting acquired sensor data to a server at regular intervals.

[0377] "Emotion engine means" refers to a device or software for analyzing a user's facial expressions, voice, body temperature, and heart rate to obtain emotion data.

[0378] "Emotion data transmission means" refers to a device or function for transmitting acquired emotion data to a server.

[0379] "Generative artificial intelligence means" refers to software or a system installed on a server for analyzing received sensor data and emotion data in real time.

[0380] "Recommendation generation means" refers to a device or function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[0381] "User interface means" refers to a device or software for notifying the user of the eco-driving advice sent by the recommendation generation means.

[0382] "Dashboard" refers to a screen or interface for displaying the analysis results of a user's driving behavior and emotional data.

[0383] "Notification means" refers to a device or function that provides a user with messages or alerts to promote reduction in gasoline costs, improvement in awareness of safe driving, and emotional relaxation.

[0384] "Buffering and retry function" refers to a function for checking the success and failure of transmission and retransmitting failed data.

[0385] The present invention relates to an eco-driving support system that combines a sensor means and an emotion engine means. Hereinafter, an embodiment of the present invention will be described in detail.

[0386] 1. System Configuration

[0387] The eco-driving support system of the present invention comprises a terminal and a server. The terminal is equipped with sensor means for acquiring vehicle acceleration, speed, and position information, and emotion engine means for analyzing the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. The terminal also has data transmission means for transmitting the collected sensor data and emotion data to the server at regular intervals.

[0388] The server is equipped with a generative artificial intelligence means for analyzing the received sensor data and emotion data in real time. The generated eco-driving advice is sent to the terminal by the recommendation generation means and notified to the user via the user interface means.

[0389] 2. Hardware and Software

[0390] Hardware: Smartphones (camera, microphone, accelerometer, GPS module) and biometric sensors such as smartwatches.

[0391] Software: smartphone applications, sentiment analysis software, generative artificial intelligence (AI) modules.

[0392] 3. Data processing and calculation

[0393] The device uses sensor means to acquire vehicle acceleration, speed, and position information in real time. This data is temporarily stored in a buffer. At the same time, the device uses emotion engine means to analyze the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. This data is also stored in the buffer.

[0394] When an internet connection is established, the device transmits this data to a server, which checks the integrity of the received data and stores it in a database. A generative artificial intelligence means analyzes this data in real time to calculate driving patterns, CO2 emissions, driving efficiency, and the user's emotional state.

[0395] The recommendation generation means generates optimal eco-driving advice for the user based on the results of the generative artificial intelligence means and transmits it to the terminal. The user interface means provides this advice to the user via pop-up notifications or a dashboard.

[0396] 4. Examples and prompts

[0397] Specific examples

[0398] App usage: The user launches a smartphone app while driving autonomously, and driving and emotional data are collected in real time.

[0399] Examples of feedback: Avoiding sudden acceleration will improve fuel efficiency, Your heart rate is high. Please relax, etc.

[0400] Prompt statement

[0401] Collect driving and emotional data in real time and generate feedback for eco-driving. Sensor data includes acceleration, speed, and GPS information. Emotional data includes facial expressions, tone of voice, and heart rate. Feedback to the user should provide advice on driving style and emotional relaxation.

[0402] This system allows users to improve driving efficiency while maintaining a comfortable driving environment.

[0403] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0404] Step 1:

[0405] The terminal acquires the acceleration, speed, and position information of the vehicle using the sensor means. The input at this time is raw sensor data related to the movement of the vehicle, and the output is acceleration, speed, and position information. Specifically, the terminal acquires data directly from the sensor in the terminal and temporarily stores it in a buffer.

[0406] Step 2:

[0407] The device uses an emotion engine to analyze the user's facial expressions, voice, body temperature, and heart rate to obtain emotional data. The input is raw data such as the user's facial image, voice, body temperature, and heart rate, and the output is analyzed emotional data. Specifically, data is obtained in real time from the camera, microphone, and biometric sensors, and processed using analysis software.

[0408] Step 3:

[0409] The device transmits the acquired sensor data and emotion data to the server at regular intervals. The input is the sensor data and emotion data stored in the buffer, and the output is the status of whether the transmission to the server was successful or not. Specifically, the device checks the Internet connection, transmits the data if the connection is established, and then checks the transmission status.

[0410] Step 4:

[0411] The server checks the integrity of the received sensor data and emotion data, and if there are no problems, stores it in a database. The input is the raw data sent from the device, and the output is the organized data storage status. Specifically, the server converts the received data into the correct format and inserts it into the database.

[0412] Step 5:

[0413] The server uses generative artificial intelligence (AI) means to analyze the driving patterns, CO2 emissions, driving efficiency, and the user's emotional state in real time based on the received data. The input is sensor data and emotional data, and the output is the analysis results. Specifically, the data is input into the generative AI platform, and the algorithm is executed to obtain the required analysis results.

[0414] Step 6:

[0415] Based on the analysis results, the server uses the recommendation generation means to generate optimal eco-driving advice for the user and sends it to the terminal. The input is the analysis results obtained from the generative artificial intelligence means, and the output is eco-driving advice. Specifically, the server creates feedback content based on the analysis results and sends it to the terminal.

[0416] Step 7:

[0417] The device notifies the user of the received eco-driving advice through a user interface means. The input is the eco-driving advice from the server, and the output is a notification to the user. Specifically, this is done by displaying a pop-up notification on the device screen or displaying detailed advice on a dashboard within the app.

[0418] Step 8:

[0419] The user will review their driving behavior based on the provided eco-driving advice and adjust their behavior according to their emotional state. The input is notifications from the device and dashboard information, and the output is improvements to the user's driving behavior and emotional state. Specifically, the user will take actions such as avoiding sudden acceleration and playing music for relaxation.

[0420] 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.

[0421] 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.

[0422] 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.

[0423] [Second embodiment]

[0424] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0425] 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.

[0426] 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).

[0427] 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.

[0428] 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.

[0429] 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).

[0430] 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.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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."

[0436] The system of the present invention combines a terminal carried by a user with a server to collect data while driving, analyze it in real time, and provide feedback. Hereinafter, embodiments of the present invention will be specifically described.

[0437] Sensor data collection

[0438] Device:

[0439] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[0440] Sending data

[0441] Device:

[0442] The collected sensor data is sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and a retransmission attempt is made.

[0443] Receiving and analyzing data

[0444] server:

[0445] The server receives sensor data sent from the device. The received data is stored in a database and analyzed in real time by generative artificial intelligence (AI). This analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[0446] Recommendation generation

[0447] server:

[0448] Based on the results of the generative AI analysis, optimal eco-driving recommendations are made to the driver, including avoiding sudden acceleration, maintaining an appropriate speed, and suggesting optimal routes, thereby helping the driver to drive in an environmentally friendly manner.

[0449] User Feedback

[0450] Device:

[0451] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[0452] Specific examples of use

[0453] User:

[0454] The user launches the application and drives the car as usual. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is completed, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[0455] In this way, the system of the present invention helps users practice eco-driving on a daily basis, contributing to reducing environmental impact. In particular, real-time coaching and post-driving analysis feedback are expected to raise driver awareness and lead to sustained improvement.

[0456] The processing flow will be explained below.

[0457] Step 1: Start collecting data

[0458] Terminal: When the application is launched, the sensor means in the terminal starts operating. Specifically, the acceleration sensor, speed sensor, and GPS module acquire the vehicle's acceleration, speed, and position information in real time. This data is temporarily stored in a buffer.

[0459] Step 2: Send data

[0460] Terminal: At regular intervals (for example, every 5 seconds), the collected sensor data is sent to the server. If the transmission is successful, the buffer is cleared. If the transmission fails, the data is kept in the buffer and will be retried the next time it is sent.

[0461] Step 3: Receiving data

[0462] Server: Receives sensor data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and stores it in a database if there are no problems.

[0463] Step 4: Data analysis

[0464] Server: A generative artificial intelligence solution analyzes the stored sensor data in real time, running algorithms to calculate driving patterns, CO2 emissions, and driving efficiency, and assessing each data point.

[0465] Step 5: Recommendation generation

[0466] Server: Based on the analysis results, the server generates optimal eco-driving recommendations for the driver. For example, if there is a lot of sudden acceleration, the server generates advice such as "Avoiding sudden acceleration will improve fuel efficiency." If the speed exceeds the appropriate range, the server generates a notification saying, "Reducing speed will reduce CO2 emissions."

[0467] Step 6: Sending recommendations

[0468] Server: The generated recommendations are sent to the device, including comparisons with past driving data and trend analysis results, to provide information in a format that is easy for the user to understand.

[0469] Step 7: Receive and display recommendations

[0470] Device: Receives recommendations sent from the server. Real-time feedback is displayed to the user via pop-up notifications and in-app interfaces. Notifications include specific courses of action and information on what can be improved.

[0471] Step 8: View the dashboard

[0472] Device: After completing a drive, the analysis results are stored on the app's dashboard. Users can check this dashboard to see their overall driving performance, comparisons with past driving data, and trends in CO2 emissions and fuel efficiency. This feedback helps raise awareness of eco-driving.

[0473] Step 9: User Actions

[0474] User: Check the displayed recommendations and analysis results and try to practice eco-driving the next time you drive. For example, you are asked to adjust your driving style by avoiding sudden acceleration or reviewing your route.

[0475] These steps allow users to receive real-time feedback to improve their driving behavior, enabling them to continuously practice eco-driving.

[0476] Example 1

[0477] 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."

[0478] In conventional eco-driving support systems, the functions of data collection, transmission, analysis, and feedback are separated, making it difficult to provide appropriate coaching and feedback on analysis results in real time. Furthermore, there is a possibility of transmission data being lost, making reliable data collection and accurate analysis difficult. These issues need to be resolved.

[0479] 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.

[0480] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, a recommendation generation means for sending the generated eco-driving advice to the terminal, and a means for calculating driving patterns, CO2 emissions, and driving efficiency, thereby enabling appropriate coaching and feedback of analysis results in real time.

[0481] A "terminal" is a device held by a user, including a smartphone or an in-vehicle device, that acquires acceleration, speed, and position information while driving.

[0482] "Sensor means" refers to a device that collects vehicle driving data using an acceleration sensor, speed sensor, GPS module, etc. built into the terminal.

[0483] "Data transmission means" refers to the function and protocol for transmitting sensor data collected by the terminal to the server at regular intervals.

[0484] "Server" refers to a remote computing system that receives and analyzes sensor data.

[0485] The "generative artificial intelligence means" is an artificial intelligence system installed on a server that analyzes received sensor data in real time and calculates driving patterns, CO2 emissions, and driving efficiency.

[0486] The "recommendation generation means" is a function that generates eco-driving advice based on the analysis results of the generative artificial intelligence means and sends it to the terminal.

[0487] The "user interface means" is a display and operation means for notifying the user of eco-driving advice and analysis results on the terminal.

[0488] The "buffering means" is a function that temporarily stores sensor data when transmission fails and attempts to retransmit it at the next transmission timing.

[0489] The "retry function" is a function for retransmitting data that has previously failed to be transmitted.

[0490] "Driving patterns" refer to the driving tendencies and behavioral patterns of a vehicle derived from collected sensor data.

[0491] "CO2 emissions" is an indicator that shows the amount of carbon dioxide emitted during operation and is used to measure environmental impact.

[0492] "Driving efficiency" is an index that indicates fuel efficiency and energy efficiency calculated based on driving data.

[0493] The present invention is a system that utilizes a user's terminal and a server to collect data while driving, analyze it in real time, and provide feedback. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the preferred embodiments.

[0494] Hardware and Software Configuration

[0495] Terminal

[0496] The device is a smartphone or an in-car device carried by the user, and is equipped with the following hardware:

[0497] Acceleration sensor: Measures acceleration and deceleration.

[0498] Speed ​​sensor: Measures the vehicle's speed.

[0499] GPS module: Obtains current location information.

[0500] Applications installed on the device collect data from these sensors and temporarily store it in a buffer. For example, when a user slams on the brakes, the accelerometer detects this and stores the data in a buffer.

[0501] server

[0502] The server is installed on the cloud and has the following functions:

[0503] Generative artificial intelligence means: Analyzes received sensor data in real time to calculate driving patterns, CO2 emissions, and driving efficiency.

[0504] Recommendation generation means: Based on the analysis results of the generative artificial intelligence means, eco-driving advice is generated and sent to the terminal.

[0505] For example, an instruction to "avoid sudden acceleration" is sent from the server to the user's terminal.

[0506] Data processing and calculation

[0507] Data transmission method

[0508] The device sends the sensor data stored in the buffer to the server at regular intervals. The device checks the Internet connection and sends the data if a connection is established. If the transmission fails, the device stores the data in the buffer and retries at the next transmission opportunity. For example, even if the connection is lost while driving, the data will be sent again after exiting a tunnel.

[0509] Data reception and analysis

[0510] The server receives sensor data sent from the device and stores it in a database. The received data is analyzed in real time by generative artificial intelligence (AI), which calculates driving patterns, CO2 emissions, and driving efficiency. For example, if the server detects a pattern of "frequent sudden acceleration while driving," it will prepare an alert based on that.

[0511] User Interface Means

[0512] The recommendations and analysis results sent from the server are notified to the user via the device application. This information is displayed visually in the user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard. For example, a notification saying "Avoiding sudden acceleration will improve fuel efficiency" pops up on the screen while driving.

[0513] Examples and prompts

[0514] Examples:

[0515] Suppose a user launches the application and drives a car. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is over, the app's dashboard displays the CO2 emissions and fuel efficiency of the driving session, allowing the user to review their driving.

[0516] Example prompt sentence:

[0517] "Generate optimal eco-driving recommendations for users based on driving data"

[0518] "It analyzes the latest sensor data in real time and provides advice to avoid sudden acceleration."

[0519] The above is a specific embodiment of the present invention. The system of the present invention is expected to help users practice eco-driving on a daily basis and contribute to reducing the burden on the environment.

[0520] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0521] Step 1:

[0522] Sensor data collection

[0523] input:

[0524] The user starts an application installed on a smartphone or an in-car terminal.

[0525] Specific operation:

[0526] The application initializes the device's accelerometer, speed sensor, and GPS module and begins collecting data.

[0527] The sensors collect acceleration, speed, and position information in real time while driving.

[0528] output:

[0529] The acquired sensor data is temporarily stored in a buffer. For example, when a user suddenly brakes, the acceleration sensor detects it and stores the data in the buffer.

[0530] Step 2:

[0531] Sending data

[0532] input:

[0533] Buffered sensor data

[0534] Specific operation:

[0535] At regular intervals, the device checks for an internet connection.

[0536] If a connection is established, it sends the buffered sensor data to the server.

[0537] If the transmission fails, the data is stored in a buffer and a retransmission attempt is made at the next transmission opportunity.

[0538] output:

[0539] Sensor data is sent to the server. For example, driving data for the past minute is sent to the server.

[0540] Step 3:

[0541] Receiving data

[0542] input:

[0543] Sensor data sent from the device

[0544] Specific operation:

[0545] The server provides an API for receiving data.

[0546] The received data is first stored in temporary storage and then stored in a database.

[0547] output:

[0548] Sensor data stored in a database, for example data about the vehicle's last driving session.

[0549] Step 4:

[0550] Analyzing the data

[0551] input:

[0552] Sensor data stored in a database

[0553] Specific operation:

[0554] The server uses generative artificial intelligence to analyze the stored sensor data in real time.

[0555] The analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[0556] output:

[0557] Analysis results from generative artificial intelligence, such as frequency of sudden acceleration and fuel efficiency.

[0558] Step 5:

[0559] Recommendation generation

[0560] input:

[0561] Analysis results using generative artificial intelligence

[0562] Specific operation:

[0563] Based on the analysis results of generative artificial intelligence, optimal eco-driving recommendations are generated.

[0564] Recommendations include avoiding sudden acceleration, maintaining an appropriate speed, and suggesting the best route.

[0565] Send recommendations to the device.

[0566] output:

[0567] Eco-driving recommendations, such as "Avoiding sudden acceleration will improve fuel efficiency."

[0568] Step 6:

[0569] Providing feedback

[0570] input:

[0571] Eco-driving recommendations and analysis results sent from the server

[0572] Specific operation:

[0573] The terminal receives the recommendations and analysis results from the server.

[0574] The application visually displays this information in a user interface.

[0575] Eco-driving advice is displayed as pop-up notifications while you're driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[0576] output:

[0577] Feedback to the user, such as a notification that pops up on the screen while driving saying, "Avoiding sudden acceleration will improve fuel efficiency."

[0578] (Application example 1)

[0579] 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."

[0580] Conventional systems make it difficult for drivers to receive appropriate eco-driving advice in real time, and lack specific guidance on how to improve fuel efficiency and reduce CO2 emissions. Furthermore, they do not provide sufficient analysis results or feedback after driving, which prevents drivers from raising their awareness to continuously drive in an eco-friendly manner.

[0581] 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.

[0582] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, an analysis means for calculating driving patterns, CO2 emissions, and driving efficiency in real time using a generative artificial intelligence model, and a recommendation generation means for sending eco-driving advice generated by the generative artificial intelligence means to the terminal. This makes it possible to provide appropriate eco-driving advice to the driver in real time and display detailed analysis results and feedback in an easy-to-understand manner even after driving has ended.

[0583] The "sensor means" refers to a sensor built into the terminal, which has the function of acquiring information on the acceleration, speed, and position of the vehicle.

[0584] The "data transmission means" is a device or processing process that has the function of transmitting acquired sensor data to a server at regular intervals.

[0585] A "generative artificial intelligence means" is a system installed on a server that analyzes received sensor data in real time and uses a generative AI model to calculate driving patterns, driving efficiency, etc.

[0586] The "recommendation generating means" is a device or processing process having a function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[0587] The "user interface means" is a device or software that provides an interface for notifying the user of the eco-driving advice sent by the recommendation generation means.

[0588] The "pop-up notification means" is a device or process that has the function of displaying eco-driving advice as a pop-up notification on the screen of the terminal at regular intervals.

[0589] "Analysis means" refers to a device or process that has the function of calculating driving patterns, CO2 emissions, and driving efficiency in real time from received driving data using a generative artificial intelligence model.

[0590] The system of the present invention functions by combining a terminal held by the user with a server. The terminal is equipped with sensor means necessary to acquire vehicle acceleration, speed, and position information, and has data transmission means for transmitting this sensor data to the server at regular intervals. The server is equipped with generative artificial intelligence means that uses a generative artificial intelligence (AI) model to analyze the received sensor data in real time. Eco-driving advice generated by the generative artificial intelligence means is transmitted from the server to the terminal.

[0591] The user's terminal notifies the received eco-driving advice through a user interface means, and provides the content of the advice to the user. This user interface means includes a pop-up notification means for visually displaying notifications while driving, and also includes a dashboard for checking analysis results and reports after driving.

[0592] As a concrete example, when a user starts driving, the device's sensor means collects data such as acceleration, speed, and GPS information. This collected data is temporarily stored in the device's buffer and periodically sent to a server via the Internet. The server then analyzes this data in real time using a generative AI model to calculate driving patterns, CO2 emissions, and driving efficiency.

[0593] Based on the analysis results, the server's generative artificial intelligence means generates eco-driving advice and sends it to the device. The user's device's pop-up notification means displays advice such as "Avoiding sudden acceleration will improve fuel efficiency by 15% and reduce CO2 emissions by 50g" as a pop-up notification while driving. After driving, the dashboard displays detailed analysis results and reports such as "CO2 emissions for this session were 500g."

[0594] The hardware used includes smartphones and in-vehicle terminals, which have built-in acceleration sensors, speed sensors, and GPS modules, while the software used includes data transmission means, generative artificial intelligence means, pop-up notification means, and user interface means including a dashboard.

[0595] An example of a prompt is:

[0596] prompt:

[0597] Analyze the following driving data, calculate driving patterns, CO2 emissions, and driving efficiency, and output recommendations for optimal eco-driving.

[0598] Driving data:

[0599] [

[0600] {"timestamp": 1660000000, "acceleration": -1.2, "speed": 60, "gps": {"latitude": 35.6895, "longitude": 139.6917}},

[0601] {"timestamp": 1660000600, "acceleration": 0.8, "speed": 70, "gps": {"latitude": 35.6900, "longitude": 139.6920}},

[0602] / / Additional data follows

[0603] ]

[0604] request:

[0605] Driving Pattern

[0606] CO2 emissions

[0607] Operating efficiency

[0608] Eco-driving recommendations (avoiding sudden acceleration, appropriate speed, suggesting optimal routes)

[0609] Let's say.

[0610] This system allows users to practice eco-driving on a daily basis and contribute to reducing environmental impact. It also provides detailed feedback during and after driving, which helps to continuously improve driver behavior and raise awareness.

[0611] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0612] Step 1:

[0613] The user launches an application on their smartphone or in-vehicle device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, and uses these sensors to collect driving data. The device temporarily stores the acquired acceleration, speed, and location information in a buffer.

[0614] Input: Application launch by user operation, sensor data (acceleration, speed, position information).

[0615] Data processing: Collecting data from sensor means and temporarily storing it in a buffer.

[0616] Output: Sensor data in buffer.

[0617] Step 2:

[0618] The terminal extracts the sensor data from the buffer at regular intervals and transmits it to the server via the Internet. If the connection is not established, the data transmission means attempts to retransmit.

[0619] Input: Buffered sensor data, internet connection status.

[0620] Data manipulation: Extracting data from buffers, sending it over the internet, and attempting to resend it.

[0621] Output: Sensor data sent to the server.

[0622] Step 3:

[0623] The server receives the sensor data sent from the device and stores it in a database, where it is analyzed in real time by generative artificial intelligence means.

[0624] Input: Sensor data sent from the device.

[0625] Data processing: Storage in database, real-time analysis using generative AI models.

[0626] Output: driving patterns, CO2 emissions, driving efficiency.

[0627] Step 4:

[0628] The generative artificial intelligence means calculates driving patterns, CO2 emissions, and driving efficiency using a generative AI model. Based on this analysis, optimal eco-driving advice is generated and sent to the terminal by the recommendation generation means.

[0629] Input: Driving data from a generative AI model.

[0630] Data processing: Calculation of driving patterns, CO2 emissions, driving efficiency, and generation of eco-driving advice.

[0631] Output: Advice data.

[0632] Step 5:

[0633] The user terminal receives the eco-driving advice sent from the server and notifies the user in real time using a pop-up notification means, particularly by displaying advice on how to avoid sudden acceleration while driving using a pop-up notification.

[0634] Input: Advice data from the server.

[0635] Data processing: Visual presentation of advice data.

[0636] Output: Advice displayed as a popup notification.

[0637] Step 6:

[0638] After the user has finished driving, the device displays an analysis of the entire driving session on a dashboard of the user interface means, where CO2 emissions and fuel efficiency are displayed as graphs and detailed analysis results.

[0639] Input: Driving session data, analysis results from the server.

[0640] Data processing: Data aggregation and graphing, and visualization of analysis results.

[0641] Output: Analysis results and reports on dashboards.

[0642] In this way, the system of the present invention performs specific processing at each step, supporting effective eco-driving in real time.

[0643] 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.

[0644] The system of the present invention is composed of a user-held device, a server, and an emotion engine that recognizes the user's emotions. This system collects data while driving, analyzes it in real time, and provides feedback based on the user's emotions, supporting more effective eco-driving.

[0645] Sensor data collection

[0646] Device:

[0647] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[0648] Collecting Emotional Data

[0649] Device:

[0650] The emotion engine means acquires emotion data by analyzing the user's facial expressions, voice, body temperature, heart rate, etc. For example, it uses a front camera to recognize the user's facial expressions, a microphone to analyze the tone and tension of the voice, and collects body temperature and heart rate from a smartwatch or other biometric sensors.

[0651] Sending data

[0652] Device:

[0653] The collected sensor data and emotion data are sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and will be retried the next time it is sent.

[0654] Receiving and analyzing data

[0655] server:

[0656] The server receives sensor data and emotion data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and, if there are no problems, stores it in a database. Generative artificial intelligence (AI) uses this data to analyze driving patterns, CO2 emissions, and driving efficiency in real time. At the same time, the user's emotion data is also incorporated into the analysis.

[0657] Recommendation generation

[0658] server:

[0659] Based on the analysis results of the generative artificial intelligence, the system generates recommendations for optimal eco-driving for the driver. What is noteworthy here is that the system also takes into account the user's emotional data obtained by the emotion engine means. For example, if the user is feeling stressed, the system will provide advice on driving methods that will reduce stress and suggest relaxation techniques.

[0660] Sending recommendations

[0661] server:

[0662] The generated recommendations are sent to the device, and information is provided in an easy-to-understand format, including comparisons with past driving data, trend analysis results, and user emotional data.

[0663] User Feedback

[0664] Device:

[0665] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the in-app dashboard. Real-time advice tailored to specific emotional states is also provided.

[0666] Specific examples of use

[0667] User:

[0668] The user launches the application and drives the car as usual. While driving, the device collects sensor data and emotional data, which it periodically sends to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. If the user feels stressed, the server also provides relaxation music and advice on driving techniques. After the drive is over, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[0669] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[0670] The processing flow will be explained below.

[0671] Step 1: Launching the Application

[0672] User: Launches the application installed on a smartphone or in-car device. When the application is launched, the sensor and emotion engine automatically enter standby mode.

[0673] Step 2: Start collecting sensor data

[0674] Terminal: The sensor means in the application starts to operate and acquires the vehicle's acceleration, speed and position information in real time from the acceleration sensor, speed sensor and GPS module. This data is temporarily stored in a buffer.

[0675] Step 3: Collecting emotion data

[0676] Device: The emotion engine uses sensor information from the front camera, microphone, smartwatch, etc. to recognize the user's emotions. It analyzes the user's facial expressions, voice, body temperature, and heart rate to generate emotion data, which is also stored in a buffer.

[0677] Step 4: Sending data

[0678] Terminal: Collected sensor data and emotion data are sent to the server at regular intervals. After an internet connection is confirmed, the data is sent to the server, and if the transmission is successful, the buffer is cleared. If the transmission fails, the data remains in the buffer and is retried the next time it is sent.

[0679] Step 5: Receiving and storing data

[0680] Server: Receives sensor data and emotion data sent from the device. The received data includes metadata such as timestamps and device IDs, and performs data integrity checks. If there are no problems, the data is stored in a database.

[0681] Step 6: Analyze data in real time

[0682] Server: Generative artificial intelligence (AI) analyzes the received sensor data and emotional data in real time. Specifically, it analyzes driving patterns, CO2 emissions, and driving efficiency, and also takes into account the user's emotional state to provide a comprehensive driving evaluation.

[0683] Step 7: Generate eco-driving recommendations

[0684] Server: Based on the analysis results of generative AI, the server generates optimal eco-driving recommendations for the user. For example, if there is a lot of unnecessary acceleration, the server will notify the user by saying, "Avoiding sudden acceleration will improve fuel efficiency," and if the user is feeling stressed, the server will suggest, "Play music for relaxation."

[0685] Step 8: Sending recommendations

[0686] Server: Sends the generated recommendations and analysis results to the device, including comparisons with past driving data, trend analysis, and information corresponding to the user's emotional state.

[0687] Step 9: Receive and display recommendations

[0688] Device: Receives recommendations and analysis results sent from the server. While driving, real-time feedback is displayed as pop-up notifications, and detailed analysis results can be viewed on the in-app dashboard.

[0689] Step 10: Driving results feedback

[0690] User: After completing a drive, the app's dashboard displays graphs of CO2 emissions and fuel efficiency for each session, allowing users to review their driving and identify areas for improvement for their next drive.

[0691] Through these steps, the system provides users with eco-driving advice that adapts in real time and promotes continuous improvement. By utilizing the emotion engine, it is also possible to provide personalized feedback based on the user's emotions. This makes it easier for drivers to practice eco-friendly driving and contributes to reducing CO2 emissions.

[0692] Example 2

[0693] 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."

[0694] Conventional eco-driving support systems aim to improve driving efficiency based on sensor data such as vehicle acceleration, speed, and location information, but they are unable to provide recommendation functions that take the user's emotional state into account. As a result, they ignore the impact of user stress and fatigue on driving efficiency, making it difficult to improve overall driving quality. Furthermore, conventional systems lacked reliability due to insufficient data integrity and retransmission processing in the event of a transmission failure.

[0695] 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.

[0696] In this invention, the server includes a generative artificial intelligence means for analyzing the received sensor data and emotional data in real time, a recommendation generation means for transmitting eco-driving advice generated by the generative artificial intelligence means to the terminal, and a means for storing the received sensor data and emotional data in a database. This enables comprehensive driving assistance that takes the user's emotional data into consideration, thereby simultaneously improving driving efficiency and reducing stress.

[0697] A "terminal" is a device used by a user, such as a sensor mounted on a vehicle or a smartphone, that has the function of acquiring and transmitting sensor data and emotion data.

[0698] "Sensor means" refers to a device or module for acquiring vehicle acceleration, speed, and position information, and collects this data in real time.

[0699] The "emotion engine means" is a function for acquiring emotion data by analyzing the user's facial expression, voice, body temperature, heart rate, etc., and for measuring and evaluating the user's psychological state.

[0700] The "data transmission means" is a function for transmitting collected sensor data and emotion data to a server at regular intervals, and transmits data using an internet connection.

[0701] "Generative artificial intelligence means" refers to an AI model that analyzes received sensor data and emotional data in real time to evaluate driving patterns and driving efficiency.

[0702] The "recommendation generating means" is a function for generating eco-driving advice for the driver based on the data analyzed by the generative artificial intelligence means and transmitting it to the terminal.

[0703] The "user interface means" is a function for visualizing and providing eco-driving advice and analysis results to the user on the terminal, and is displayed as notifications or a dashboard.

[0704] This invention is a system that supports eco-driving by analyzing the user's driving behavior and emotional state in real time using a user's terminal and a server. Specifically, it comprises sensor means for acquiring vehicle acceleration, speed, and position information, emotion engine means for acquiring the user's facial expression, voice, body temperature, heart rate, etc., and data transmission means for transmitting this data to the server at regular intervals.

[0705] Hardware and Software Details

[0706] A device is a device that can be easily accessed by a user, such as a smartphone, tablet, or in-car device. The device is equipped with an accelerometer, speed sensor, GPS module, camera, microphone, smartwatch, etc., and uses these to collect data.

[0707] Examples:

[0708] Car driving data and user facial expression data are collected using a smartphone.

[0709] The smartwatch is used to measure the user's heart rate and body temperature.

[0710] Acquiring and Sending Data

[0711] The device temporarily stores the acquired sensor data and emotion data in a buffer, and after checking the Internet connection, transmits it to the server at regular intervals. If the transmission fails, the data is kept in a local buffer and is retried the next time it is transmitted.

[0712] Data reception and analysis

[0713] The server receives sensor data and emotion data sent from the device and analyzes it in real time using generative artificial intelligence, including analysis of driving patterns, CO2 emissions, driving efficiency, etc. It also incorporates the user's emotion data to provide a comprehensive evaluation.

[0714] Generating and sending recommendations

[0715] The server generates eco-driving recommendations based on the analysis results. These recommendations include advice on how to avoid sudden acceleration, how to improve driving efficiency, and how to reduce user stress. The generated recommendations are sent from the server to the device.

[0716] User Feedback

[0717] The device notifies the user of the recommendations and analysis results sent from the server. This information is presented visually through a user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[0718] Examples:

[0719] While driving, a pop-up notification will appear saying, "Avoiding sudden acceleration will improve fuel efficiency."

[0720] After completing a drive, check the app's dashboard to see the CO2 emissions and fuel efficiency of that driving session.

[0721] Prompt Sentence Examples

[0722] "What's the trick to avoiding sudden acceleration?"

[0723] "Tell me how to relax when you're feeling stressed"

[0724] "Show me the latest CO2 emissions data"

[0725] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[0726] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0727] Step 1:

[0728] The user launches the application.

[0729] Specific operation: Tap to launch an application on your smartphone or in-car device.

[0730] Input: User actions.

[0731] Output: The startup status of the application.

[0732] Step 2:

[0733] The terminal starts collecting data using the sensor means.

[0734] Specific operation: The acceleration sensor, speed sensor, and GPS module in the device are activated to collect data in real time.

[0735] Input: Data obtained from the device's sensors (acceleration, speed, location information).

[0736] Output: Temporarily buffered sensor data.

[0737] Step 3:

[0738] The terminal begins collecting emotion data using the emotion engine means.

[0739] Specific operation: The front camera captures the user's facial expressions, the microphone analyzes the tone and tension of the voice, and the smartwatch collects body temperature and heart rate data.

[0740] Input: Camera footage, audio data, body temperature, heart rate.

[0741] Output: Emotion data temporarily stored in a buffer.

[0742] Step 4:

[0743] The terminal stores the collected sensor data and emotion data in a buffer and transmits them to the server using the data transmission means.

[0744] What it does: Checks for internet connectivity and sends data if connection is established. If the transmission fails, it buffers the data and tries to send it again.

[0745] Input: Buffered sensor data and emotion data.

[0746] Output: Data sent to the server or held in the buffer.

[0747] Step 5:

[0748] The server receives the sensor data and emotion data transmitted from the terminal.

[0749] Specific operation: The server receives the data and checks the metadata such as the timestamp and device ID.

[0750] Input: Sensor data and emotion data sent from the device.

[0751] Output: Data received, data checked for integrity.

[0752] Step 6:

[0753] The server uses generative artificial intelligence tools to analyze the data in real time.

[0754] Specific operation: Using an AI model, the system analyzes driving patterns, CO2 emissions, driving efficiency, etc., and makes a comprehensive assessment incorporating emotional data.

[0755] Input: Received sensor data and emotion data.

[0756] Output: Analysis results (driving patterns, CO2 emissions, driving efficiency, emotional state).

[0757] Step 7:

[0758] Based on the analysis results, the server generates recommendations for eco-driving.

[0759] Specific actions: Based on the analysis results, advice to improve driving efficiency and suggestions to reduce stress are generated.

[0760] Input: Analysis results.

[0761] Output: The generated recommendations.

[0762] Step 8:

[0763] The server transmits the generated recommendations to the terminal.

[0764] Specific operation: Recommendations are sent to the device and information is provided in a visually easy-to-understand format.

[0765] Input: Generated recommendations.

[0766] Output: Recommendations sent to the device.

[0767] Step 9:

[0768] The terminal notifies the user of the recommendation using a user interface means.

[0769] Specific operation: Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[0770] Input: Recommendations sent from the server.

[0771] Output: Notifications and dashboard display to the user.

[0772] (Application example 2)

[0773] 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."

[0774] Conventional eco-driving support systems only provide advice based on the user's driving behavior and are unable to provide personalized feedback that takes into account the user's emotional state. As a result, there has been a lack of support for reducing the stress and anxiety that users feel while driving and maintaining a comfortable driving environment. The present invention aims to combine driving data and emotional data to provide users with optimal eco-driving advice and personalized feedback based on their emotions, thereby improving driving efficiency and maintaining a comfortable driving environment.

[0775] The identification process by the identification 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 generative artificial intelligence means that analyzes received sensor data and emotion data in real time, recommendation generation means that transmits generated eco-driving advice to the terminal, and user interface means that notifies the user of the transmitted eco-driving advice. This makes it possible to simultaneously provide the user with feedback to improve their driving behavior and relax their emotions.

[0776] "Sensor means" refers to a device for acquiring information on the acceleration, speed, and position of a vehicle.

[0777] "Data transmission means" refers to a device or function for transmitting acquired sensor data to a server at regular intervals.

[0778] "Emotion engine means" refers to a device or software for analyzing a user's facial expressions, voice, body temperature, and heart rate to obtain emotion data.

[0779] "Emotion data transmission means" refers to a device or function for transmitting acquired emotion data to a server.

[0780] "Generative artificial intelligence means" refers to software or a system installed on a server for analyzing received sensor data and emotion data in real time.

[0781] "Recommendation generation means" refers to a device or function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[0782] "User interface means" refers to a device or software for notifying the user of the eco-driving advice sent by the recommendation generation means.

[0783] "Dashboard" refers to a screen or interface for displaying the analysis results of a user's driving behavior and emotional data.

[0784] "Notification means" refers to a device or function that provides a user with messages or alerts to promote reduction in gasoline costs, improvement in awareness of safe driving, and emotional relaxation.

[0785] "Buffering and retry function" refers to a function for checking the success and failure of transmission and retransmitting failed data.

[0786] The present invention relates to an eco-driving support system that combines a sensor means and an emotion engine means. Hereinafter, an embodiment of the present invention will be described in detail.

[0787] 1. System Configuration

[0788] The eco-driving support system of the present invention comprises a terminal and a server. The terminal is equipped with sensor means for acquiring vehicle acceleration, speed, and position information, and emotion engine means for analyzing the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. The terminal also has data transmission means for transmitting the collected sensor data and emotion data to the server at regular intervals.

[0789] The server is equipped with a generative artificial intelligence means for analyzing the received sensor data and emotion data in real time. The generated eco-driving advice is sent to the terminal by the recommendation generation means and notified to the user via the user interface means.

[0790] 2. Hardware and Software

[0791] Hardware: Smartphones (camera, microphone, accelerometer, GPS module) and biometric sensors such as smartwatches.

[0792] Software: smartphone applications, sentiment analysis software, generative artificial intelligence (AI) modules.

[0793] 3. Data processing and calculation

[0794] The device uses sensor means to acquire vehicle acceleration, speed, and position information in real time. This data is temporarily stored in a buffer. At the same time, the device uses emotion engine means to analyze the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. This data is also stored in the buffer.

[0795] When an internet connection is established, the device transmits this data to a server, which checks the integrity of the received data and stores it in a database. A generative artificial intelligence means analyzes this data in real time to calculate driving patterns, CO2 emissions, driving efficiency, and the user's emotional state.

[0796] The recommendation generation means generates optimal eco-driving advice for the user based on the results of the generative artificial intelligence means and transmits it to the terminal. The user interface means provides this advice to the user via pop-up notifications or a dashboard.

[0797] 4. Examples and prompts

[0798] Specific examples

[0799] App usage: The user launches a smartphone app while driving autonomously, and driving and emotional data are collected in real time.

[0800] Examples of feedback: Avoiding sudden acceleration will improve fuel efficiency, Your heart rate is high. Please relax, etc.

[0801] Prompt statement

[0802] Collect driving and emotional data in real time and generate feedback for eco-driving. Sensor data includes acceleration, speed, and GPS information. Emotional data includes facial expressions, tone of voice, and heart rate. Feedback to the user should provide advice on driving style and emotional relaxation.

[0803] This system allows users to improve driving efficiency while maintaining a comfortable driving environment.

[0804] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0805] Step 1:

[0806] The terminal acquires the acceleration, speed, and position information of the vehicle using the sensor means. The input at this time is raw sensor data related to the movement of the vehicle, and the output is acceleration, speed, and position information. Specifically, the terminal acquires data directly from the sensor in the terminal and temporarily stores it in a buffer.

[0807] Step 2:

[0808] The device uses an emotion engine to analyze the user's facial expressions, voice, body temperature, and heart rate to obtain emotional data. The input is raw data such as the user's facial image, voice, body temperature, and heart rate, and the output is analyzed emotional data. Specifically, data is obtained in real time from the camera, microphone, and biometric sensors, and processed using analysis software.

[0809] Step 3:

[0810] The device transmits the acquired sensor data and emotion data to the server at regular intervals. The input is the sensor data and emotion data stored in the buffer, and the output is the status of whether the transmission to the server was successful or not. Specifically, the device checks the Internet connection, transmits the data if the connection is established, and then checks the transmission status.

[0811] Step 4:

[0812] The server checks the integrity of the received sensor data and emotion data, and if there are no problems, stores it in a database. The input is the raw data sent from the device, and the output is the organized data storage status. Specifically, the server converts the received data into the correct format and inserts it into the database.

[0813] Step 5:

[0814] The server uses generative artificial intelligence (AI) means to analyze the driving patterns, CO2 emissions, driving efficiency, and the user's emotional state in real time based on the received data. The input is sensor data and emotional data, and the output is the analysis results. Specifically, the data is input into the generative AI platform, and the algorithm is executed to obtain the required analysis results.

[0815] Step 6:

[0816] Based on the analysis results, the server uses the recommendation generation means to generate optimal eco-driving advice for the user and sends it to the terminal. The input is the analysis results obtained from the generative artificial intelligence means, and the output is eco-driving advice. Specifically, the server creates feedback content based on the analysis results and sends it to the terminal.

[0817] Step 7:

[0818] The device notifies the user of the received eco-driving advice through a user interface means. The input is the eco-driving advice from the server, and the output is a notification to the user. Specifically, this is done by displaying a pop-up notification on the device screen or displaying detailed advice on a dashboard within the app.

[0819] Step 8:

[0820] The user will review their driving behavior based on the provided eco-driving advice and adjust their behavior according to their emotional state. The input is notifications from the device and dashboard information, and the output is improvements to the user's driving behavior and emotional state. Specifically, the user will take actions such as avoiding sudden acceleration and playing music for relaxation.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] [Third embodiment]

[0825] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0826] 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.

[0827] 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).

[0828] 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.

[0829] 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.

[0830] 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).

[0831] 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.

[0832] 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.

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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."

[0837] The system of the present invention combines a terminal carried by a user with a server to collect data while driving, analyze it in real time, and provide feedback. Hereinafter, embodiments of the present invention will be specifically described.

[0838] Sensor data collection

[0839] Device:

[0840] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[0841] Sending data

[0842] Device:

[0843] The collected sensor data is sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and a retransmission attempt is made.

[0844] Receiving and analyzing data

[0845] server:

[0846] The server receives sensor data sent from the device. The received data is stored in a database and analyzed in real time by generative artificial intelligence (AI). This analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[0847] Recommendation generation

[0848] server:

[0849] Based on the results of the generative AI analysis, optimal eco-driving recommendations are made to the driver, including avoiding sudden acceleration, maintaining an appropriate speed, and suggesting optimal routes, thereby helping the driver to drive in an environmentally friendly manner.

[0850] User Feedback

[0851] Device:

[0852] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[0853] Specific examples of use

[0854] User:

[0855] The user launches the application and drives the car as usual. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is completed, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[0856] In this way, the system of the present invention helps users practice eco-driving on a daily basis, contributing to reducing environmental impact. In particular, real-time coaching and post-driving analysis feedback are expected to raise driver awareness and lead to sustained improvement.

[0857] The processing flow will be explained below.

[0858] Step 1: Start collecting data

[0859] Terminal: When the application is launched, the sensor means in the terminal starts operating. Specifically, the acceleration sensor, speed sensor, and GPS module acquire the vehicle's acceleration, speed, and position information in real time. This data is temporarily stored in a buffer.

[0860] Step 2: Send data

[0861] Terminal: At regular intervals (for example, every 5 seconds), the collected sensor data is sent to the server. If the transmission is successful, the buffer is cleared. If the transmission fails, the data is kept in the buffer and will be retried the next time it is sent.

[0862] Step 3: Receiving data

[0863] Server: Receives sensor data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and stores it in a database if there are no problems.

[0864] Step 4: Data analysis

[0865] Server: A generative artificial intelligence solution analyzes the stored sensor data in real time, running algorithms to calculate driving patterns, CO2 emissions, and driving efficiency, and assessing each data point.

[0866] Step 5: Recommendation generation

[0867] Server: Based on the analysis results, the server generates optimal eco-driving recommendations for the driver. For example, if there is a lot of sudden acceleration, the server generates advice such as "Avoiding sudden acceleration will improve fuel efficiency." If the speed exceeds the appropriate range, the server generates a notification saying, "Reducing speed will reduce CO2 emissions."

[0868] Step 6: Sending recommendations

[0869] Server: The generated recommendations are sent to the device, including comparisons with past driving data and trend analysis results, to provide information in a format that is easy for the user to understand.

[0870] Step 7: Receive and display recommendations

[0871] Device: Receives recommendations sent from the server. Real-time feedback is displayed to the user via pop-up notifications and in-app interfaces. Notifications include specific courses of action and information on what can be improved.

[0872] Step 8: View the dashboard

[0873] Device: After completing a drive, the analysis results are stored on the app's dashboard. Users can check this dashboard to see their overall driving performance, comparisons with past driving data, and trends in CO2 emissions and fuel efficiency. This feedback helps raise awareness of eco-driving.

[0874] Step 9: User Actions

[0875] User: Check the displayed recommendations and analysis results and try to practice eco-driving the next time you drive. For example, you are asked to adjust your driving style by avoiding sudden acceleration or reviewing your route.

[0876] These steps allow users to receive real-time feedback to improve their driving behavior, enabling them to continuously practice eco-driving.

[0877] Example 1

[0878] 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."

[0879] In conventional eco-driving support systems, the functions of data collection, transmission, analysis, and feedback are separated, making it difficult to provide appropriate coaching and feedback on analysis results in real time. Furthermore, there is a possibility of transmission data being lost, making reliable data collection and accurate analysis difficult. These issues need to be resolved.

[0880] 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.

[0881] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, a recommendation generation means for sending the generated eco-driving advice to the terminal, and a means for calculating driving patterns, CO2 emissions, and driving efficiency, thereby enabling appropriate coaching and feedback of analysis results in real time.

[0882] A "terminal" is a device held by a user, including a smartphone or an in-vehicle device, that acquires acceleration, speed, and position information while driving.

[0883] "Sensor means" refers to a device that collects vehicle driving data using an acceleration sensor, speed sensor, GPS module, etc. built into the terminal.

[0884] "Data transmission means" refers to the function and protocol for transmitting sensor data collected by the terminal to the server at regular intervals.

[0885] "Server" refers to a remote computing system that receives and analyzes sensor data.

[0886] The "generative artificial intelligence means" is an artificial intelligence system installed on a server that analyzes received sensor data in real time and calculates driving patterns, CO2 emissions, and driving efficiency.

[0887] The "recommendation generation means" is a function that generates eco-driving advice based on the analysis results of the generative artificial intelligence means and sends it to the terminal.

[0888] The "user interface means" is a display and operation means for notifying the user of eco-driving advice and analysis results on the terminal.

[0889] The "buffering means" is a function that temporarily stores sensor data when transmission fails and attempts to retransmit it at the next transmission timing.

[0890] The "retry function" is a function for retransmitting data that has previously failed to be transmitted.

[0891] "Driving patterns" refer to the driving tendencies and behavioral patterns of a vehicle derived from collected sensor data.

[0892] "CO2 emissions" is an indicator that shows the amount of carbon dioxide emitted during operation and is used to measure environmental impact.

[0893] "Driving efficiency" is an index that indicates fuel efficiency and energy efficiency calculated based on driving data.

[0894] The present invention is a system that utilizes a user's terminal and a server to collect data while driving, analyze it in real time, and provide feedback. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the preferred embodiments.

[0895] Hardware and Software Configuration

[0896] Terminal

[0897] The device is a smartphone or an in-car device carried by the user, and is equipped with the following hardware:

[0898] Acceleration sensor: Measures acceleration and deceleration.

[0899] Speed ​​sensor: Measures the vehicle's speed.

[0900] GPS module: Obtains current location information.

[0901] Applications installed on the device collect data from these sensors and temporarily store it in a buffer. For example, when a user slams on the brakes, the accelerometer detects this and stores the data in a buffer.

[0902] server

[0903] The server is installed on the cloud and has the following functions:

[0904] Generative artificial intelligence means: Analyzes received sensor data in real time to calculate driving patterns, CO2 emissions, and driving efficiency.

[0905] Recommendation generation means: Based on the analysis results of the generative artificial intelligence means, eco-driving advice is generated and sent to the terminal.

[0906] For example, an instruction to "avoid sudden acceleration" is sent from the server to the user's terminal.

[0907] Data processing and calculation

[0908] Data transmission method

[0909] The device sends the sensor data stored in the buffer to the server at regular intervals. The device checks the Internet connection and sends the data if a connection is established. If the transmission fails, the device stores the data in the buffer and retries at the next transmission opportunity. For example, even if the connection is lost while driving, the data will be sent again after exiting a tunnel.

[0910] Data reception and analysis

[0911] The server receives sensor data sent from the device and stores it in a database. The received data is analyzed in real time by generative artificial intelligence (AI), which calculates driving patterns, CO2 emissions, and driving efficiency. For example, if the server detects a pattern of "frequent sudden acceleration while driving," it will prepare an alert based on that.

[0912] User Interface Means

[0913] The recommendations and analysis results sent from the server are notified to the user via the device application. This information is displayed visually in the user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard. For example, a notification saying "Avoiding sudden acceleration will improve fuel efficiency" pops up on the screen while driving.

[0914] Examples and prompts

[0915] Examples:

[0916] Suppose a user launches the application and drives a car. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is over, the app's dashboard displays the CO2 emissions and fuel efficiency of the driving session, allowing the user to review their driving.

[0917] Example prompt sentence:

[0918] "Generate optimal eco-driving recommendations for users based on driving data"

[0919] "It analyzes the latest sensor data in real time and provides advice to avoid sudden acceleration."

[0920] The above is a specific embodiment of the present invention. The system of the present invention is expected to help users practice eco-driving on a daily basis and contribute to reducing the burden on the environment.

[0921] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0922] Step 1:

[0923] Sensor data collection

[0924] input:

[0925] The user starts an application installed on a smartphone or an in-car terminal.

[0926] Specific operation:

[0927] The application initializes the device's accelerometer, speed sensor, and GPS module and begins collecting data.

[0928] The sensors collect acceleration, speed, and position information in real time while driving.

[0929] output:

[0930] The acquired sensor data is temporarily stored in a buffer. For example, when a user suddenly brakes, the acceleration sensor detects it and stores the data in the buffer.

[0931] Step 2:

[0932] Sending data

[0933] input:

[0934] Buffered sensor data

[0935] Specific operation:

[0936] At regular intervals, the device checks for an internet connection.

[0937] If a connection is established, it sends the buffered sensor data to the server.

[0938] If the transmission fails, the data is stored in a buffer and a retransmission attempt is made at the next transmission opportunity.

[0939] output:

[0940] Sensor data is sent to the server. For example, driving data for the past minute is sent to the server.

[0941] Step 3:

[0942] Receiving data

[0943] input:

[0944] Sensor data sent from the device

[0945] Specific operation:

[0946] The server provides an API for receiving data.

[0947] The received data is first stored in temporary storage and then stored in a database.

[0948] output:

[0949] Sensor data stored in a database, for example data about the vehicle's last driving session.

[0950] Step 4:

[0951] Analyzing the data

[0952] input:

[0953] Sensor data stored in a database

[0954] Specific operation:

[0955] The server uses generative artificial intelligence to analyze the stored sensor data in real time.

[0956] The analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[0957] output:

[0958] Analysis results from generative artificial intelligence, such as frequency of sudden acceleration and fuel efficiency.

[0959] Step 5:

[0960] Recommendation generation

[0961] input:

[0962] Analysis results using generative artificial intelligence

[0963] Specific operation:

[0964] Based on the analysis results of generative artificial intelligence, optimal eco-driving recommendations are generated.

[0965] Recommendations include avoiding sudden acceleration, maintaining an appropriate speed, and suggesting the best route.

[0966] Send recommendations to the device.

[0967] output:

[0968] Eco-driving recommendations, such as "Avoiding sudden acceleration will improve fuel efficiency."

[0969] Step 6:

[0970] Providing feedback

[0971] input:

[0972] Eco-driving recommendations and analysis results sent from the server

[0973] Specific operation:

[0974] The terminal receives the recommendations and analysis results from the server.

[0975] The application visually displays this information in a user interface.

[0976] Eco-driving advice is displayed as pop-up notifications while you're driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[0977] output:

[0978] Feedback to the user, such as a notification that pops up on the screen while driving saying, "Avoiding sudden acceleration will improve fuel efficiency."

[0979] (Application example 1)

[0980] 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."

[0981] Conventional systems make it difficult for drivers to receive appropriate eco-driving advice in real time, and lack specific guidance on how to improve fuel efficiency and reduce CO2 emissions. Furthermore, they do not provide sufficient analysis results or feedback after driving, which prevents drivers from raising their awareness to continuously drive in an eco-friendly manner.

[0982] 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.

[0983] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, an analysis means for calculating driving patterns, CO2 emissions, and driving efficiency in real time using a generative artificial intelligence model, and a recommendation generation means for sending eco-driving advice generated by the generative artificial intelligence means to the terminal. This makes it possible to provide appropriate eco-driving advice to the driver in real time and display detailed analysis results and feedback in an easy-to-understand manner even after driving has ended.

[0984] The "sensor means" refers to a sensor built into the terminal, which has the function of acquiring information on the acceleration, speed, and position of the vehicle.

[0985] The "data transmission means" is a device or processing process that has the function of transmitting acquired sensor data to a server at regular intervals.

[0986] A "generative artificial intelligence means" is a system installed on a server that analyzes received sensor data in real time and uses a generative AI model to calculate driving patterns, driving efficiency, etc.

[0987] The "recommendation generating means" is a device or processing process having a function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[0988] The "user interface means" is a device or software that provides an interface for notifying the user of the eco-driving advice sent by the recommendation generation means.

[0989] The "pop-up notification means" is a device or process that has the function of displaying eco-driving advice as a pop-up notification on the screen of the terminal at regular intervals.

[0990] "Analysis means" refers to a device or process that has the function of calculating driving patterns, CO2 emissions, and driving efficiency in real time from received driving data using a generative artificial intelligence model.

[0991] The system of the present invention functions by combining a terminal held by the user with a server. The terminal is equipped with sensor means necessary to acquire vehicle acceleration, speed, and position information, and has data transmission means for transmitting this sensor data to the server at regular intervals. The server is equipped with generative artificial intelligence means that uses a generative artificial intelligence (AI) model to analyze the received sensor data in real time. Eco-driving advice generated by the generative artificial intelligence means is transmitted from the server to the terminal.

[0992] The user's terminal notifies the received eco-driving advice through a user interface means, and provides the content of the advice to the user. This user interface means includes a pop-up notification means for visually displaying notifications while driving, and also includes a dashboard for checking analysis results and reports after driving.

[0993] As a concrete example, when a user starts driving, the device's sensor means collects data such as acceleration, speed, and GPS information. This collected data is temporarily stored in the device's buffer and periodically sent to a server via the Internet. The server then analyzes this data in real time using a generative AI model to calculate driving patterns, CO2 emissions, and driving efficiency.

[0994] Based on the analysis results, the server's generative artificial intelligence means generates eco-driving advice and sends it to the device. The user's device's pop-up notification means displays advice such as "Avoiding sudden acceleration will improve fuel efficiency by 15% and reduce CO2 emissions by 50g" as a pop-up notification while driving. After driving, the dashboard displays detailed analysis results and reports such as "CO2 emissions for this session were 500g."

[0995] The hardware used includes smartphones and in-vehicle terminals, which have built-in acceleration sensors, speed sensors, and GPS modules, while the software used includes data transmission means, generative artificial intelligence means, pop-up notification means, and user interface means including a dashboard.

[0996] An example of a prompt is:

[0997] prompt:

[0998] Analyze the following driving data, calculate driving patterns, CO2 emissions, and driving efficiency, and output recommendations for optimal eco-driving.

[0999] Driving data:

[1000] [

[1001] {"timestamp": 1660000000, "acceleration": -1.2, "speed": 60, "gps": {"latitude": 35.6895, "longitude": 139.6917}},

[1002] {"timestamp": 1660000600, "acceleration": 0.8, "speed": 70, "gps": {"latitude": 35.6900, "longitude": 139.6920}},

[1003] / / Additional data follows

[1004] ]

[1005] request:

[1006] Driving Pattern

[1007] CO2 emissions

[1008] Operating efficiency

[1009] Eco-driving recommendations (avoiding sudden acceleration, appropriate speed, suggesting optimal routes)

[1010] Let's say.

[1011] This system allows users to practice eco-driving on a daily basis and contribute to reducing environmental impact. It also provides detailed feedback during and after driving, which helps to continuously improve driver behavior and raise awareness.

[1012] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1013] Step 1:

[1014] The user launches an application on their smartphone or in-vehicle device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, and uses these sensors to collect driving data. The device temporarily stores the acquired acceleration, speed, and location information in a buffer.

[1015] Input: Application launch by user operation, sensor data (acceleration, speed, position information).

[1016] Data processing: Collecting data from sensor means and temporarily storing it in a buffer.

[1017] Output: Sensor data in buffer.

[1018] Step 2:

[1019] The terminal extracts the sensor data from the buffer at regular intervals and transmits it to the server via the Internet. If the connection is not established, the data transmission means attempts to retransmit.

[1020] Input: Buffered sensor data, internet connection status.

[1021] Data manipulation: Extracting data from buffers, sending it over the internet, and attempting to resend it.

[1022] Output: Sensor data sent to the server.

[1023] Step 3:

[1024] The server receives the sensor data sent from the device and stores it in a database, where it is analyzed in real time by generative artificial intelligence means.

[1025] Input: Sensor data sent from the device.

[1026] Data processing: Storage in database, real-time analysis using generative AI models.

[1027] Output: driving patterns, CO2 emissions, driving efficiency.

[1028] Step 4:

[1029] The generative artificial intelligence means calculates driving patterns, CO2 emissions, and driving efficiency using a generative AI model. Based on this analysis, optimal eco-driving advice is generated and sent to the terminal by the recommendation generation means.

[1030] Input: Driving data from a generative AI model.

[1031] Data processing: Calculation of driving patterns, CO2 emissions, driving efficiency, and generation of eco-driving advice.

[1032] Output: Advice data.

[1033] Step 5:

[1034] The user terminal receives the eco-driving advice sent from the server and notifies the user in real time using a pop-up notification means, particularly by displaying advice on how to avoid sudden acceleration while driving using a pop-up notification.

[1035] Input: Advice data from the server.

[1036] Data processing: Visual presentation of advice data.

[1037] Output: Advice displayed as a popup notification.

[1038] Step 6:

[1039] After the user has finished driving, the device displays an analysis of the entire driving session on a dashboard of the user interface means, where CO2 emissions and fuel efficiency are displayed as graphs and detailed analysis results.

[1040] Input: Driving session data, analysis results from the server.

[1041] Data processing: Data aggregation and graphing, and visualization of analysis results.

[1042] Output: Analysis results and reports on dashboards.

[1043] In this way, the system of the present invention performs specific processing at each step, supporting effective eco-driving in real time.

[1044] 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.

[1045] The system of the present invention is composed of a user-held device, a server, and an emotion engine that recognizes the user's emotions. This system collects data while driving, analyzes it in real time, and provides feedback based on the user's emotions, supporting more effective eco-driving.

[1046] Sensor data collection

[1047] Device:

[1048] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[1049] Collecting Emotional Data

[1050] Device:

[1051] The emotion engine means acquires emotion data by analyzing the user's facial expressions, voice, body temperature, heart rate, etc. For example, it uses a front camera to recognize the user's facial expressions, a microphone to analyze the tone and tension of the voice, and collects body temperature and heart rate from a smartwatch or other biometric sensors.

[1052] Sending data

[1053] Device:

[1054] The collected sensor data and emotion data are sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and will be retried the next time it is sent.

[1055] Receiving and analyzing data

[1056] server:

[1057] The server receives sensor data and emotion data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and, if there are no problems, stores it in a database. Generative artificial intelligence (AI) uses this data to analyze driving patterns, CO2 emissions, and driving efficiency in real time. At the same time, the user's emotion data is also incorporated into the analysis.

[1058] Recommendation generation

[1059] server:

[1060] Based on the analysis results of the generative artificial intelligence, the system generates recommendations for optimal eco-driving for the driver. What is noteworthy here is that the system also takes into account the user's emotional data obtained by the emotion engine means. For example, if the user is feeling stressed, the system will provide advice on driving methods that will reduce stress and suggest relaxation techniques.

[1061] Sending recommendations

[1062] server:

[1063] The generated recommendations are sent to the device, and information is provided in an easy-to-understand format, including comparisons with past driving data, trend analysis results, and user emotional data.

[1064] User Feedback

[1065] Device:

[1066] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the in-app dashboard. Real-time advice tailored to specific emotional states is also provided.

[1067] Specific examples of use

[1068] User:

[1069] The user launches the application and drives the car as usual. While driving, the device collects sensor data and emotional data, which it periodically sends to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. If the user feels stressed, the server also provides relaxation music and advice on driving techniques. After the drive is over, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[1070] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[1071] The processing flow will be explained below.

[1072] Step 1: Launching the Application

[1073] User: Launches the application installed on a smartphone or in-car device. When the application is launched, the sensor and emotion engine automatically enter standby mode.

[1074] Step 2: Start collecting sensor data

[1075] Terminal: The sensor means in the application starts to operate and acquires the vehicle's acceleration, speed and position information in real time from the acceleration sensor, speed sensor and GPS module. This data is temporarily stored in a buffer.

[1076] Step 3: Collecting emotion data

[1077] Device: The emotion engine uses sensor information from the front camera, microphone, smartwatch, etc. to recognize the user's emotions. It analyzes the user's facial expressions, voice, body temperature, and heart rate to generate emotion data, which is also stored in a buffer.

[1078] Step 4: Sending data

[1079] Terminal: Collected sensor data and emotion data are sent to the server at regular intervals. After an internet connection is confirmed, the data is sent to the server, and if the transmission is successful, the buffer is cleared. If the transmission fails, the data remains in the buffer and is retried the next time it is sent.

[1080] Step 5: Receiving and storing data

[1081] Server: Receives sensor data and emotion data sent from the device. The received data includes metadata such as timestamps and device IDs, and performs data integrity checks. If there are no problems, the data is stored in a database.

[1082] Step 6: Analyze data in real time

[1083] Server: Generative artificial intelligence (AI) analyzes the received sensor data and emotional data in real time. Specifically, it analyzes driving patterns, CO2 emissions, and driving efficiency, and also takes into account the user's emotional state to provide a comprehensive driving evaluation.

[1084] Step 7: Generate eco-driving recommendations

[1085] Server: Based on the analysis results of generative AI, the server generates optimal eco-driving recommendations for the user. For example, if there is a lot of unnecessary acceleration, the server will notify the user by saying, "Avoiding sudden acceleration will improve fuel efficiency," and if the user is feeling stressed, the server will suggest, "Play music for relaxation."

[1086] Step 8: Sending recommendations

[1087] Server: Sends the generated recommendations and analysis results to the device, including comparisons with past driving data, trend analysis, and information corresponding to the user's emotional state.

[1088] Step 9: Receive and display recommendations

[1089] Device: Receives recommendations and analysis results sent from the server. While driving, real-time feedback is displayed as pop-up notifications, and detailed analysis results can be viewed on the in-app dashboard.

[1090] Step 10: Driving results feedback

[1091] User: After completing a drive, the app's dashboard displays graphs of CO2 emissions and fuel efficiency for each session, allowing users to review their driving and identify areas for improvement for their next drive.

[1092] Through these steps, the system provides users with eco-driving advice that adapts in real time and promotes continuous improvement. By utilizing the emotion engine, it is also possible to provide personalized feedback based on the user's emotions. This makes it easier for drivers to practice eco-friendly driving and contributes to reducing CO2 emissions.

[1093] Example 2

[1094] 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."

[1095] Conventional eco-driving support systems aim to improve driving efficiency based on sensor data such as vehicle acceleration, speed, and location information, but they are unable to provide recommendation functions that take the user's emotional state into account. As a result, they ignore the impact of user stress and fatigue on driving efficiency, making it difficult to improve overall driving quality. Furthermore, conventional systems lacked reliability due to insufficient data integrity and retransmission processing in the event of a transmission failure.

[1096] 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.

[1097] In this invention, the server includes a generative artificial intelligence means for analyzing the received sensor data and emotional data in real time, a recommendation generation means for transmitting eco-driving advice generated by the generative artificial intelligence means to the terminal, and a means for storing the received sensor data and emotional data in a database. This enables comprehensive driving assistance that takes the user's emotional data into consideration, thereby simultaneously improving driving efficiency and reducing stress.

[1098] A "terminal" is a device used by a user, such as a sensor mounted on a vehicle or a smartphone, that has the function of acquiring and transmitting sensor data and emotion data.

[1099] "Sensor means" refers to a device or module for acquiring vehicle acceleration, speed, and position information, and collects this data in real time.

[1100] The "emotion engine means" is a function for acquiring emotion data by analyzing the user's facial expression, voice, body temperature, heart rate, etc., and for measuring and evaluating the user's psychological state.

[1101] The "data transmission means" is a function for transmitting collected sensor data and emotion data to a server at regular intervals, and transmits data using an internet connection.

[1102] "Generative artificial intelligence means" refers to an AI model that analyzes received sensor data and emotional data in real time to evaluate driving patterns and driving efficiency.

[1103] The "recommendation generating means" is a function for generating eco-driving advice for the driver based on the data analyzed by the generative artificial intelligence means and transmitting it to the terminal.

[1104] The "user interface means" is a function for visualizing and providing eco-driving advice and analysis results to the user on the terminal, and is displayed as notifications or a dashboard.

[1105] This invention is a system that supports eco-driving by analyzing the user's driving behavior and emotional state in real time using a user's terminal and a server. Specifically, it comprises sensor means for acquiring vehicle acceleration, speed, and position information, emotion engine means for acquiring the user's facial expression, voice, body temperature, heart rate, etc., and data transmission means for transmitting this data to the server at regular intervals.

[1106] Hardware and Software Details

[1107] A device is a device that can be easily accessed by a user, such as a smartphone, tablet, or in-car device. The device is equipped with an accelerometer, speed sensor, GPS module, camera, microphone, smartwatch, etc., and uses these to collect data.

[1108] Examples:

[1109] Car driving data and user facial expression data are collected using a smartphone.

[1110] The smartwatch is used to measure the user's heart rate and body temperature.

[1111] Acquiring and Sending Data

[1112] The device temporarily stores the acquired sensor data and emotion data in a buffer, and after checking the Internet connection, transmits it to the server at regular intervals. If the transmission fails, the data is kept in a local buffer and is retried the next time it is transmitted.

[1113] Data reception and analysis

[1114] The server receives sensor data and emotion data sent from the device and analyzes it in real time using generative artificial intelligence, including analysis of driving patterns, CO2 emissions, driving efficiency, etc. It also incorporates the user's emotion data to provide a comprehensive evaluation.

[1115] Generating and sending recommendations

[1116] The server generates eco-driving recommendations based on the analysis results. These recommendations include advice on how to avoid sudden acceleration, how to improve driving efficiency, and how to reduce user stress. The generated recommendations are sent from the server to the device.

[1117] User Feedback

[1118] The device notifies the user of the recommendations and analysis results sent from the server. This information is presented visually through a user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[1119] Examples:

[1120] While driving, a pop-up notification will appear saying, "Avoiding sudden acceleration will improve fuel efficiency."

[1121] After completing a drive, check the app's dashboard to see the CO2 emissions and fuel efficiency of that driving session.

[1122] Prompt Sentence Examples

[1123] "What's the trick to avoiding sudden acceleration?"

[1124] "Tell me how to relax when you're feeling stressed"

[1125] "Show me the latest CO2 emissions data"

[1126] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[1127] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1128] Step 1:

[1129] The user launches the application.

[1130] Specific operation: Tap to launch an application on your smartphone or in-car device.

[1131] Input: User actions.

[1132] Output: The startup status of the application.

[1133] Step 2:

[1134] The terminal starts collecting data using the sensor means.

[1135] Specific operation: The acceleration sensor, speed sensor, and GPS module in the device are activated to collect data in real time.

[1136] Input: Data obtained from the device's sensors (acceleration, speed, location information).

[1137] Output: Temporarily buffered sensor data.

[1138] Step 3:

[1139] The terminal begins collecting emotion data using the emotion engine means.

[1140] Specific operation: The front camera captures the user's facial expressions, the microphone analyzes the tone and tension of the voice, and the smartwatch collects body temperature and heart rate data.

[1141] Input: Camera footage, audio data, body temperature, heart rate.

[1142] Output: Emotion data temporarily stored in a buffer.

[1143] Step 4:

[1144] The terminal stores the collected sensor data and emotion data in a buffer and transmits them to the server using the data transmission means.

[1145] What it does: Checks for internet connectivity and sends data if connection is established. If the transmission fails, it buffers the data and tries to send it again.

[1146] Input: Buffered sensor data and emotion data.

[1147] Output: Data sent to the server or held in the buffer.

[1148] Step 5:

[1149] The server receives the sensor data and emotion data transmitted from the terminal.

[1150] Specific operation: The server receives the data and checks the metadata such as the timestamp and device ID.

[1151] Input: Sensor data and emotion data sent from the device.

[1152] Output: Data received, data checked for integrity.

[1153] Step 6:

[1154] The server uses generative artificial intelligence tools to analyze the data in real time.

[1155] Specific operation: Using an AI model, the system analyzes driving patterns, CO2 emissions, driving efficiency, etc., and makes a comprehensive assessment incorporating emotional data.

[1156] Input: Received sensor data and emotion data.

[1157] Output: Analysis results (driving patterns, CO2 emissions, driving efficiency, emotional state).

[1158] Step 7:

[1159] Based on the analysis results, the server generates recommendations for eco-driving.

[1160] Specific actions: Based on the analysis results, advice to improve driving efficiency and suggestions to reduce stress are generated.

[1161] Input: Analysis results.

[1162] Output: The generated recommendations.

[1163] Step 8:

[1164] The server transmits the generated recommendations to the terminal.

[1165] Specific operation: Recommendations are sent to the device and information is provided in a visually easy-to-understand format.

[1166] Input: Generated recommendations.

[1167] Output: Recommendations sent to the device.

[1168] Step 9:

[1169] The terminal notifies the user of the recommendation using a user interface means.

[1170] Specific operation: Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[1171] Input: Recommendations sent from the server.

[1172] Output: Notifications and dashboard display to the user.

[1173] (Application example 2)

[1174] 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."

[1175] Conventional eco-driving support systems only provide advice based on the user's driving behavior and are unable to provide personalized feedback that takes into account the user's emotional state. As a result, there has been a lack of support for reducing the stress and anxiety that users feel while driving and maintaining a comfortable driving environment. The present invention aims to combine driving data and emotional data to provide users with optimal eco-driving advice and personalized feedback based on their emotions, thereby improving driving efficiency and maintaining a comfortable driving environment.

[1176] The identification process by the identification 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 generative artificial intelligence means that analyzes received sensor data and emotion data in real time, recommendation generation means that transmits generated eco-driving advice to the terminal, and user interface means that notifies the user of the transmitted eco-driving advice. This makes it possible to simultaneously provide the user with feedback to improve their driving behavior and relax their emotions.

[1177] "Sensor means" refers to a device for acquiring information on the acceleration, speed, and position of a vehicle.

[1178] "Data transmission means" refers to a device or function for transmitting acquired sensor data to a server at regular intervals.

[1179] "Emotion engine means" refers to a device or software for analyzing a user's facial expressions, voice, body temperature, and heart rate to obtain emotion data.

[1180] "Emotion data transmission means" refers to a device or function for transmitting acquired emotion data to a server.

[1181] "Generative artificial intelligence means" refers to software or a system installed on a server for analyzing received sensor data and emotion data in real time.

[1182] "Recommendation generation means" refers to a device or function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[1183] "User interface means" refers to a device or software for notifying the user of the eco-driving advice sent by the recommendation generation means.

[1184] "Dashboard" refers to a screen or interface for displaying the analysis results of a user's driving behavior and emotional data.

[1185] "Notification means" refers to a device or function that provides a user with messages or alerts to promote reduction in gasoline costs, improvement in awareness of safe driving, and emotional relaxation.

[1186] "Buffering and retry function" refers to a function for checking the success and failure of transmission and retransmitting failed data.

[1187] The present invention relates to an eco-driving support system that combines a sensor means and an emotion engine means. Hereinafter, an embodiment of the present invention will be described in detail.

[1188] 1. System Configuration

[1189] The eco-driving support system of the present invention comprises a terminal and a server. The terminal is equipped with sensor means for acquiring vehicle acceleration, speed, and position information, and emotion engine means for analyzing the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. The terminal also has data transmission means for transmitting the collected sensor data and emotion data to the server at regular intervals.

[1190] The server is equipped with a generative artificial intelligence means for analyzing the received sensor data and emotion data in real time. The generated eco-driving advice is sent to the terminal by the recommendation generation means and notified to the user via the user interface means.

[1191] 2. Hardware and Software

[1192] Hardware: Smartphones (camera, microphone, accelerometer, GPS module) and biometric sensors such as smartwatches.

[1193] Software: smartphone applications, sentiment analysis software, generative artificial intelligence (AI) modules.

[1194] 3. Data processing and calculation

[1195] The device uses sensor means to acquire vehicle acceleration, speed, and position information in real time. This data is temporarily stored in a buffer. At the same time, the device uses emotion engine means to analyze the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. This data is also stored in the buffer.

[1196] When an internet connection is established, the device transmits this data to a server, which checks the integrity of the received data and stores it in a database. A generative artificial intelligence means analyzes this data in real time to calculate driving patterns, CO2 emissions, driving efficiency, and the user's emotional state.

[1197] The recommendation generation means generates optimal eco-driving advice for the user based on the results of the generative artificial intelligence means and transmits it to the terminal. The user interface means provides this advice to the user via pop-up notifications or a dashboard.

[1198] 4. Examples and prompts

[1199] Specific examples

[1200] App usage: The user launches a smartphone app while driving autonomously, and driving and emotional data are collected in real time.

[1201] Examples of feedback: Avoiding sudden acceleration will improve fuel efficiency, Your heart rate is high. Please relax, etc.

[1202] Prompt statement

[1203] Collect driving and emotional data in real time and generate feedback for eco-driving. Sensor data includes acceleration, speed, and GPS information. Emotional data includes facial expressions, tone of voice, and heart rate. Feedback to the user should provide advice on driving style and emotional relaxation.

[1204] This system allows users to improve driving efficiency while maintaining a comfortable driving environment.

[1205] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1206] Step 1:

[1207] The terminal acquires the acceleration, speed, and position information of the vehicle using the sensor means. The input at this time is raw sensor data related to the movement of the vehicle, and the output is acceleration, speed, and position information. Specifically, the terminal acquires data directly from the sensor in the terminal and temporarily stores it in a buffer.

[1208] Step 2:

[1209] The device uses an emotion engine to analyze the user's facial expressions, voice, body temperature, and heart rate to obtain emotional data. The input is raw data such as the user's facial image, voice, body temperature, and heart rate, and the output is analyzed emotional data. Specifically, data is obtained in real time from the camera, microphone, and biometric sensors, and processed using analysis software.

[1210] Step 3:

[1211] The device transmits the acquired sensor data and emotion data to the server at regular intervals. The input is the sensor data and emotion data stored in the buffer, and the output is the status of whether the transmission to the server was successful or not. Specifically, the device checks the Internet connection, transmits the data if the connection is established, and then checks the transmission status.

[1212] Step 4:

[1213] The server checks the integrity of the received sensor data and emotion data, and if there are no problems, stores it in a database. The input is the raw data sent from the device, and the output is the organized data storage status. Specifically, the server converts the received data into the correct format and inserts it into the database.

[1214] Step 5:

[1215] The server uses generative artificial intelligence (AI) means to analyze the driving patterns, CO2 emissions, driving efficiency, and the user's emotional state in real time based on the received data. The input is sensor data and emotional data, and the output is the analysis results. Specifically, the data is input into the generative AI platform, and the algorithm is executed to obtain the required analysis results.

[1216] Step 6:

[1217] Based on the analysis results, the server uses the recommendation generation means to generate optimal eco-driving advice for the user and sends it to the terminal. The input is the analysis results obtained from the generative artificial intelligence means, and the output is eco-driving advice. Specifically, the server creates feedback content based on the analysis results and sends it to the terminal.

[1218] Step 7:

[1219] The device notifies the user of the received eco-driving advice through a user interface means. The input is the eco-driving advice from the server, and the output is a notification to the user. Specifically, this is done by displaying a pop-up notification on the device screen or displaying detailed advice on a dashboard within the app.

[1220] Step 8:

[1221] The user will review their driving behavior based on the provided eco-driving advice and adjust their behavior according to their emotional state. The input is notifications from the device and dashboard information, and the output is improvements to the user's driving behavior and emotional state. Specifically, the user will take actions such as avoiding sudden acceleration and playing music for relaxation.

[1222] 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.

[1223] 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.

[1224] 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.

[1225] [Fourth embodiment]

[1226] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1227] 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.

[1228] 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).

[1229] 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.

[1230] 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.

[1231] 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).

[1232] 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.

[1233] 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.

[1234] 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.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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."

[1239] The system of the present invention combines a terminal carried by a user with a server to collect data while driving, analyze it in real time, and provide feedback. Hereinafter, embodiments of the present invention will be specifically described.

[1240] Sensor data collection

[1241] Device:

[1242] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[1243] Sending data

[1244] Device:

[1245] The collected sensor data is sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and a retransmission attempt is made.

[1246] Receiving and analyzing data

[1247] server:

[1248] The server receives sensor data sent from the device. The received data is stored in a database and analyzed in real time by generative artificial intelligence (AI). This analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[1249] Recommendation generation

[1250] server:

[1251] Based on the results of the generative AI analysis, optimal eco-driving recommendations are made to the driver, including avoiding sudden acceleration, maintaining an appropriate speed, and suggesting optimal routes, thereby helping the driver to drive in an environmentally friendly manner.

[1252] User Feedback

[1253] Device:

[1254] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[1255] Specific examples of use

[1256] User:

[1257] The user launches the application and drives the car as usual. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is completed, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[1258] In this way, the system of the present invention helps users practice eco-driving on a daily basis, contributing to reducing environmental impact. In particular, real-time coaching and post-driving analysis feedback are expected to raise driver awareness and lead to sustained improvement.

[1259] The processing flow will be explained below.

[1260] Step 1: Start collecting data

[1261] Terminal: When the application is launched, the sensor means in the terminal starts operating. Specifically, the acceleration sensor, speed sensor, and GPS module acquire the vehicle's acceleration, speed, and position information in real time. This data is temporarily stored in a buffer.

[1262] Step 2: Send data

[1263] Terminal: At regular intervals (for example, every 5 seconds), the collected sensor data is sent to the server. If the transmission is successful, the buffer is cleared. If the transmission fails, the data is kept in the buffer and will be retried the next time it is sent.

[1264] Step 3: Receiving data

[1265] Server: Receives sensor data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and stores it in a database if there are no problems.

[1266] Step 4: Data analysis

[1267] Server: A generative artificial intelligence solution analyzes the stored sensor data in real time, running algorithms to calculate driving patterns, CO2 emissions, and driving efficiency, and assessing each data point.

[1268] Step 5: Recommendation generation

[1269] Server: Based on the analysis results, the server generates optimal eco-driving recommendations for the driver. For example, if there is a lot of sudden acceleration, the server generates advice such as "Avoiding sudden acceleration will improve fuel efficiency." If the speed exceeds the appropriate range, the server generates a notification saying, "Reducing speed will reduce CO2 emissions."

[1270] Step 6: Sending recommendations

[1271] Server: The generated recommendations are sent to the device, including comparisons with past driving data and trend analysis results, to provide information in a format that is easy for the user to understand.

[1272] Step 7: Receive and display recommendations

[1273] Device: Receives recommendations sent from the server. Real-time feedback is displayed to the user via pop-up notifications and in-app interfaces. Notifications include specific courses of action and information on what can be improved.

[1274] Step 8: View the dashboard

[1275] Device: After completing a drive, the analysis results are stored on the app's dashboard. Users can check this dashboard to see their overall driving performance, comparisons with past driving data, and trends in CO2 emissions and fuel efficiency. This feedback helps raise awareness of eco-driving.

[1276] Step 9: User Actions

[1277] User: Check the displayed recommendations and analysis results and try to practice eco-driving the next time you drive. For example, you are asked to adjust your driving style by avoiding sudden acceleration or reviewing your route.

[1278] These steps allow users to receive real-time feedback to improve their driving behavior, enabling them to continuously practice eco-driving.

[1279] Example 1

[1280] 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."

[1281] In conventional eco-driving support systems, the functions of data collection, transmission, analysis, and feedback are separated, making it difficult to provide appropriate coaching and feedback on analysis results in real time. Furthermore, there is a possibility of transmission data being lost, making reliable data collection and accurate analysis difficult. These issues need to be resolved.

[1282] 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.

[1283] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, a recommendation generation means for sending the generated eco-driving advice to the terminal, and a means for calculating driving patterns, CO2 emissions, and driving efficiency, thereby enabling appropriate coaching and feedback of analysis results in real time.

[1284] A "terminal" is a device held by a user, including a smartphone or an in-vehicle device, that acquires acceleration, speed, and position information while driving.

[1285] "Sensor means" refers to a device that collects vehicle driving data using an acceleration sensor, speed sensor, GPS module, etc. built into the terminal.

[1286] "Data transmission means" refers to the function and protocol for transmitting sensor data collected by the terminal to the server at regular intervals.

[1287] "Server" refers to a remote computing system that receives and analyzes sensor data.

[1288] The "generative artificial intelligence means" is an artificial intelligence system installed on a server that analyzes received sensor data in real time and calculates driving patterns, CO2 emissions, and driving efficiency.

[1289] The "recommendation generation means" is a function that generates eco-driving advice based on the analysis results of the generative artificial intelligence means and sends it to the terminal.

[1290] The "user interface means" is a display and operation means for notifying the user of eco-driving advice and analysis results on the terminal.

[1291] The "buffering means" is a function that temporarily stores sensor data when transmission fails and attempts to retransmit it at the next transmission timing.

[1292] The "retry function" is a function for retransmitting data that has previously failed to be transmitted.

[1293] "Driving patterns" refer to the driving tendencies and behavioral patterns of a vehicle derived from collected sensor data.

[1294] "CO2 emissions" is an indicator that shows the amount of carbon dioxide emitted during operation and is used to measure environmental impact.

[1295] "Driving efficiency" is an index that indicates fuel efficiency and energy efficiency calculated based on driving data.

[1296] The present invention is a system that utilizes a user's terminal and a server to collect data while driving, analyze it in real time, and provide feedback. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described in detail below with reference to the preferred embodiments.

[1297] Hardware and Software Configuration

[1298] Terminal

[1299] The device is a smartphone or an in-car device carried by the user, and is equipped with the following hardware:

[1300] Acceleration sensor: Measures acceleration and deceleration.

[1301] Speed ​​sensor: Measures the vehicle's speed.

[1302] GPS module: Obtains current location information.

[1303] Applications installed on the device collect data from these sensors and temporarily store it in a buffer. For example, when a user slams on the brakes, the accelerometer detects this and stores the data in a buffer.

[1304] server

[1305] The server is installed on the cloud and has the following functions:

[1306] Generative artificial intelligence means: Analyzes received sensor data in real time to calculate driving patterns, CO2 emissions, and driving efficiency.

[1307] Recommendation generation means: Based on the analysis results of the generative artificial intelligence means, eco-driving advice is generated and sent to the terminal.

[1308] For example, an instruction to "avoid sudden acceleration" is sent from the server to the user's terminal.

[1309] Data processing and calculation

[1310] Data transmission method

[1311] The device sends the sensor data stored in the buffer to the server at regular intervals. The device checks the Internet connection and sends the data if a connection is established. If the transmission fails, the device stores the data in the buffer and retries at the next transmission opportunity. For example, even if the connection is lost while driving, the data will be sent again after exiting a tunnel.

[1312] Data reception and analysis

[1313] The server receives sensor data sent from the device and stores it in a database. The received data is analyzed in real time by generative artificial intelligence (AI), which calculates driving patterns, CO2 emissions, and driving efficiency. For example, if the server detects a pattern of "frequent sudden acceleration while driving," it will prepare an alert based on that.

[1314] User Interface Means

[1315] The recommendations and analysis results sent from the server are notified to the user via the device application. This information is displayed visually in the user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the app's dashboard. For example, a notification saying "Avoiding sudden acceleration will improve fuel efficiency" pops up on the screen while driving.

[1316] Examples and prompts

[1317] Examples:

[1318] Suppose a user launches the application and drives a car. While driving, the device collects sensor data and periodically sends it to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. After the drive is over, the app's dashboard displays the CO2 emissions and fuel efficiency of the driving session, allowing the user to review their driving.

[1319] Example prompt sentence:

[1320] "Generate optimal eco-driving recommendations for users based on driving data"

[1321] "It analyzes the latest sensor data in real time and provides advice to avoid sudden acceleration."

[1322] The above is a specific embodiment of the present invention. The system of the present invention is expected to help users practice eco-driving on a daily basis and contribute to reducing the burden on the environment.

[1323] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1324] Step 1:

[1325] Sensor data collection

[1326] input:

[1327] The user starts an application installed on a smartphone or an in-car terminal.

[1328] Specific operation:

[1329] The application initializes the device's accelerometer, speed sensor, and GPS module and begins collecting data.

[1330] The sensors collect acceleration, speed, and position information in real time while driving.

[1331] output:

[1332] The acquired sensor data is temporarily stored in a buffer. For example, when a user suddenly brakes, the acceleration sensor detects it and stores the data in the buffer.

[1333] Step 2:

[1334] Sending data

[1335] input:

[1336] Buffered sensor data

[1337] Specific operation:

[1338] At regular intervals, the device checks for an internet connection.

[1339] If a connection is established, it sends the buffered sensor data to the server.

[1340] If the transmission fails, the data is stored in a buffer and a retransmission attempt is made at the next transmission opportunity.

[1341] output:

[1342] Sensor data is sent to the server. For example, driving data for the past minute is sent to the server.

[1343] Step 3:

[1344] Receiving data

[1345] input:

[1346] Sensor data sent from the device

[1347] Specific operation:

[1348] The server provides an API for receiving data.

[1349] The received data is first stored in temporary storage and then stored in a database.

[1350] output:

[1351] Sensor data stored in a database, for example data about the vehicle's last driving session.

[1352] Step 4:

[1353] Analyzing the data

[1354] input:

[1355] Sensor data stored in a database

[1356] Specific operation:

[1357] The server uses generative artificial intelligence to analyze the stored sensor data in real time.

[1358] The analysis calculates driving patterns, CO2 emissions, and driving efficiency.

[1359] output:

[1360] Analysis results from generative artificial intelligence, such as frequency of sudden acceleration and fuel efficiency.

[1361] Step 5:

[1362] Recommendation generation

[1363] input:

[1364] Analysis results using generative artificial intelligence

[1365] Specific operation:

[1366] Based on the analysis results of generative artificial intelligence, optimal eco-driving recommendations are generated.

[1367] Recommendations include avoiding sudden acceleration, maintaining an appropriate speed, and suggesting the best route.

[1368] Send recommendations to the device.

[1369] output:

[1370] Eco-driving recommendations, such as "Avoiding sudden acceleration will improve fuel efficiency."

[1371] Step 6:

[1372] Providing feedback

[1373] input:

[1374] Eco-driving recommendations and analysis results sent from the server

[1375] Specific operation:

[1376] The terminal receives the recommendations and analysis results from the server.

[1377] The application visually displays this information in a user interface.

[1378] Eco-driving advice is displayed as pop-up notifications while you're driving, and detailed analysis results and reports can be viewed on the app's dashboard.

[1379] output:

[1380] Feedback to the user, such as a notification that pops up on the screen while driving saying, "Avoiding sudden acceleration will improve fuel efficiency."

[1381] (Application example 1)

[1382] 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."

[1383] Conventional systems make it difficult for drivers to receive appropriate eco-driving advice in real time, and lack specific guidance on how to improve fuel efficiency and reduce CO2 emissions. Furthermore, they do not provide sufficient analysis results or feedback after driving, which prevents drivers from raising their awareness to continuously drive in an eco-friendly manner.

[1384] 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.

[1385] In this invention, the server includes a generative artificial intelligence means for analyzing received sensor data in real time, an analysis means for calculating driving patterns, CO2 emissions, and driving efficiency in real time using a generative artificial intelligence model, and a recommendation generation means for sending eco-driving advice generated by the generative artificial intelligence means to the terminal. This makes it possible to provide appropriate eco-driving advice to the driver in real time and display detailed analysis results and feedback in an easy-to-understand manner even after driving has ended.

[1386] The "sensor means" refers to a sensor built into the terminal, which has the function of acquiring information on the acceleration, speed, and position of the vehicle.

[1387] The "data transmission means" is a device or processing process that has the function of transmitting acquired sensor data to a server at regular intervals.

[1388] A "generative artificial intelligence means" is a system installed on a server that analyzes received sensor data in real time and uses a generative AI model to calculate driving patterns, driving efficiency, etc.

[1389] The "recommendation generating means" is a device or processing process having a function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[1390] The "user interface means" is a device or software that provides an interface for notifying the user of the eco-driving advice sent by the recommendation generation means.

[1391] The "pop-up notification means" is a device or process that has the function of displaying eco-driving advice as a pop-up notification on the screen of the terminal at regular intervals.

[1392] "Analysis means" refers to a device or process that has the function of calculating driving patterns, CO2 emissions, and driving efficiency in real time from received driving data using a generative artificial intelligence model.

[1393] The system of the present invention functions by combining a terminal held by the user with a server. The terminal is equipped with sensor means necessary to acquire vehicle acceleration, speed, and position information, and has data transmission means for transmitting this sensor data to the server at regular intervals. The server is equipped with generative artificial intelligence means that uses a generative artificial intelligence (AI) model to analyze the received sensor data in real time. Eco-driving advice generated by the generative artificial intelligence means is transmitted from the server to the terminal.

[1394] The user's terminal notifies the received eco-driving advice through a user interface means, and provides the content of the advice to the user. This user interface means includes a pop-up notification means for visually displaying notifications while driving, and also includes a dashboard for checking analysis results and reports after driving.

[1395] As a concrete example, when a user starts driving, the device's sensor means collects data such as acceleration, speed, and GPS information. This collected data is temporarily stored in the device's buffer and periodically sent to a server via the Internet. The server then analyzes this data in real time using a generative AI model to calculate driving patterns, CO2 emissions, and driving efficiency.

[1396] Based on the analysis results, the server's generative artificial intelligence means generates eco-driving advice and sends it to the device. The user's device's pop-up notification means displays advice such as "Avoiding sudden acceleration will improve fuel efficiency by 15% and reduce CO2 emissions by 50g" as a pop-up notification while driving. After driving, the dashboard displays detailed analysis results and reports such as "CO2 emissions for this session were 500g."

[1397] The hardware used includes smartphones and in-vehicle terminals, which have built-in acceleration sensors, speed sensors, and GPS modules, while the software used includes data transmission means, generative artificial intelligence means, pop-up notification means, and user interface means including a dashboard.

[1398] An example of a prompt is:

[1399] prompt:

[1400] Analyze the following driving data, calculate driving patterns, CO2 emissions, and driving efficiency, and output recommendations for optimal eco-driving.

[1401] Driving data:

[1402] [

[1403] {"timestamp": 1660000000, "acceleration": -1.2, "speed": 60, "gps": {"latitude": 35.6895, "longitude": 139.6917}},

[1404] {"timestamp": 1660000600, "acceleration": 0.8, "speed": 70, "gps": {"latitude": 35.6900, "longitude": 139.6920}},

[1405] / / Additional data follows

[1406] ]

[1407] request:

[1408] Driving Pattern

[1409] CO2 emissions

[1410] Operating efficiency

[1411] Eco-driving recommendations (avoiding sudden acceleration, appropriate speed, suggesting optimal routes)

[1412] Let's say.

[1413] This system allows users to practice eco-driving on a daily basis and contribute to reducing environmental impact. It also provides detailed feedback during and after driving, which helps to continuously improve driver behavior and raise awareness.

[1414] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1415] Step 1:

[1416] The user launches an application on their smartphone or in-vehicle device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, and uses these sensors to collect driving data. The device temporarily stores the acquired acceleration, speed, and location information in a buffer.

[1417] Input: Application launch by user operation, sensor data (acceleration, speed, position information).

[1418] Data processing: Collecting data from sensor means and temporarily storing it in a buffer.

[1419] Output: Sensor data in buffer.

[1420] Step 2:

[1421] The terminal extracts the sensor data from the buffer at regular intervals and transmits it to the server via the Internet. If the connection is not established, the data transmission means attempts to retransmit.

[1422] Input: Buffered sensor data, internet connection status.

[1423] Data manipulation: Extracting data from buffers, sending it over the internet, and attempting to resend it.

[1424] Output: Sensor data sent to the server.

[1425] Step 3:

[1426] The server receives the sensor data sent from the device and stores it in a database, where it is analyzed in real time by generative artificial intelligence means.

[1427] Input: Sensor data sent from the device.

[1428] Data processing: Storage in database, real-time analysis using generative AI models.

[1429] Output: driving patterns, CO2 emissions, driving efficiency.

[1430] Step 4:

[1431] The generative artificial intelligence means calculates driving patterns, CO2 emissions, and driving efficiency using a generative AI model. Based on this analysis, optimal eco-driving advice is generated and sent to the terminal by the recommendation generation means.

[1432] Input: Driving data from a generative AI model.

[1433] Data processing: Calculation of driving patterns, CO2 emissions, driving efficiency, and generation of eco-driving advice.

[1434] Output: Advice data.

[1435] Step 5:

[1436] The user terminal receives the eco-driving advice sent from the server and notifies the user in real time using a pop-up notification means, particularly by displaying advice on how to avoid sudden acceleration while driving using a pop-up notification.

[1437] Input: Advice data from the server.

[1438] Data processing: Visual presentation of advice data.

[1439] Output: Advice displayed as a popup notification.

[1440] Step 6:

[1441] After the user has finished driving, the device displays an analysis of the entire driving session on a dashboard of the user interface means, where CO2 emissions and fuel efficiency are displayed as graphs and detailed analysis results.

[1442] Input: Driving session data, analysis results from the server.

[1443] Data processing: Data aggregation and graphing, and visualization of analysis results.

[1444] Output: Analysis results and reports on dashboards.

[1445] In this way, the system of the present invention performs specific processing at each step, supporting effective eco-driving in real time.

[1446] 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.

[1447] The system of the present invention is composed of a user-held device, a server, and an emotion engine that recognizes the user's emotions. This system collects data while driving, analyzes it in real time, and provides feedback based on the user's emotions, supporting more effective eco-driving.

[1448] Sensor data collection

[1449] Device:

[1450] The user launches an application installed on a smartphone or in-car device. The device is equipped with an acceleration sensor, a speed sensor, and a GPS module, which collects real-time acceleration, speed, and location information while driving. The collected data is temporarily stored in a buffer.

[1451] Collecting Emotional Data

[1452] Device:

[1453] The emotion engine means acquires emotion data by analyzing the user's facial expressions, voice, body temperature, heart rate, etc. For example, it uses a front camera to recognize the user's facial expressions, a microphone to analyze the tone and tension of the voice, and collects body temperature and heart rate from a smartwatch or other biometric sensors.

[1454] Sending data

[1455] Device:

[1456] The collected sensor data and emotion data are sent to the server at regular intervals. The device checks for an internet connection and sends the data if a connection is established. If the transmission fails, the data is stored in a buffer and will be retried the next time it is sent.

[1457] Receiving and analyzing data

[1458] server:

[1459] The server receives sensor data and emotion data sent from the device. The received data includes metadata such as a timestamp and device ID. After receiving the data, it checks its integrity and, if there are no problems, stores it in a database. Generative artificial intelligence (AI) uses this data to analyze driving patterns, CO2 emissions, and driving efficiency in real time. At the same time, the user's emotion data is also incorporated into the analysis.

[1460] Recommendation generation

[1461] server:

[1462] Based on the analysis results of the generative artificial intelligence, the system generates recommendations for optimal eco-driving for the driver. What is noteworthy here is that the system also takes into account the user's emotional data obtained by the emotion engine means. For example, if the user is feeling stressed, the system will provide advice on driving methods that will reduce stress and suggest relaxation techniques.

[1463] Sending recommendations

[1464] server:

[1465] The generated recommendations are sent to the device, and information is provided in an easy-to-understand format, including comparisons with past driving data, trend analysis results, and user emotional data.

[1466] User Feedback

[1467] Device:

[1468] The recommendations and analysis results sent from the server are displayed on the device. The user interface visually presents this information to the user. For example, eco-driving advice is displayed as a pop-up notification while driving, and detailed analysis results and reports can be viewed on the in-app dashboard. Real-time advice tailored to specific emotional states is also provided.

[1469] Specific examples of use

[1470] User:

[1471] The user launches the application and drives the car as usual. While driving, the device collects sensor data and emotional data, which it periodically sends to the server. The server analyzes this data in real time and displays coaching notifications such as "Avoiding sudden acceleration will improve fuel efficiency" if the driving involves a lot of unnecessary acceleration or deceleration. If the user feels stressed, the server also provides relaxation music and advice on driving techniques. After the drive is over, the app's dashboard displays graphs of the driving session's CO2 emissions and fuel efficiency, allowing the user to review their driving.

[1472] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[1473] The processing flow will be explained below.

[1474] Step 1: Launching the Application

[1475] User: Launches the application installed on a smartphone or in-car device. When the application is launched, the sensor and emotion engine automatically enter standby mode.

[1476] Step 2: Start collecting sensor data

[1477] Terminal: The sensor means in the application starts to operate and acquires the vehicle's acceleration, speed and position information in real time from the acceleration sensor, speed sensor and GPS module. This data is temporarily stored in a buffer.

[1478] Step 3: Collecting emotion data

[1479] Device: The emotion engine uses sensor information from the front camera, microphone, smartwatch, etc. to recognize the user's emotions. It analyzes the user's facial expressions, voice, body temperature, and heart rate to generate emotion data, which is also stored in a buffer.

[1480] Step 4: Sending data

[1481] Terminal: Collected sensor data and emotion data are sent to the server at regular intervals. After an internet connection is confirmed, the data is sent to the server, and if the transmission is successful, the buffer is cleared. If the transmission fails, the data remains in the buffer and is retried the next time it is sent.

[1482] Step 5: Receiving and storing data

[1483] Server: Receives sensor data and emotion data sent from the device. The received data includes metadata such as timestamps and device IDs, and performs data integrity checks. If there are no problems, the data is stored in a database.

[1484] Step 6: Analyze data in real time

[1485] Server: Generative artificial intelligence (AI) analyzes the received sensor data and emotional data in real time. Specifically, it analyzes driving patterns, CO2 emissions, and driving efficiency, and also takes into account the user's emotional state to provide a comprehensive driving evaluation.

[1486] Step 7: Generate eco-driving recommendations

[1487] Server: Based on the analysis results of generative AI, the server generates optimal eco-driving recommendations for the user. For example, if there is a lot of unnecessary acceleration, the server will notify the user by saying, "Avoiding sudden acceleration will improve fuel efficiency," and if the user is feeling stressed, the server will suggest, "Play music for relaxation."

[1488] Step 8: Sending recommendations

[1489] Server: Sends the generated recommendations and analysis results to the device, including comparisons with past driving data, trend analysis, and information corresponding to the user's emotional state.

[1490] Step 9: Receive and display recommendations

[1491] Device: Receives recommendations and analysis results sent from the server. While driving, real-time feedback is displayed as pop-up notifications, and detailed analysis results can be viewed on the in-app dashboard.

[1492] Step 10: Driving results feedback

[1493] User: After completing a drive, the app's dashboard displays graphs of CO2 emissions and fuel efficiency for each session, allowing users to review their driving and identify areas for improvement for their next drive.

[1494] Through these steps, the system provides users with eco-driving advice that adapts in real time and promotes continuous improvement. By utilizing the emotion engine, it is also possible to provide personalized feedback based on the user's emotions. This makes it easier for drivers to practice eco-friendly driving and contributes to reducing CO2 emissions.

[1495] Example 2

[1496] 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."

[1497] Conventional eco-driving support systems aim to improve driving efficiency based on sensor data such as vehicle acceleration, speed, and location information, but they are unable to provide recommendation functions that take the user's emotional state into account. As a result, they ignore the impact of user stress and fatigue on driving efficiency, making it difficult to improve overall driving quality. Furthermore, conventional systems lacked reliability due to insufficient data integrity and retransmission processing in the event of a transmission failure.

[1498] 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.

[1499] In this invention, the server includes a generative artificial intelligence means for analyzing the received sensor data and emotional data in real time, a recommendation generation means for transmitting eco-driving advice generated by the generative artificial intelligence means to the terminal, and a means for storing the received sensor data and emotional data in a database. This enables comprehensive driving assistance that takes the user's emotional data into consideration, thereby simultaneously improving driving efficiency and reducing stress.

[1500] A "terminal" is a device used by a user, such as a sensor mounted on a vehicle or a smartphone, that has the function of acquiring and transmitting sensor data and emotion data.

[1501] "Sensor means" refers to a device or module for acquiring vehicle acceleration, speed, and position information, and collects this data in real time.

[1502] The "emotion engine means" is a function for acquiring emotion data by analyzing the user's facial expression, voice, body temperature, heart rate, etc., and for measuring and evaluating the user's psychological state.

[1503] The "data transmission means" is a function for transmitting collected sensor data and emotion data to a server at regular intervals, and transmits data using an internet connection.

[1504] "Generative artificial intelligence means" refers to an AI model that analyzes received sensor data and emotional data in real time to evaluate driving patterns and driving efficiency.

[1505] The "recommendation generating means" is a function for generating eco-driving advice for the driver based on the data analyzed by the generative artificial intelligence means and transmitting it to the terminal.

[1506] The "user interface means" is a function for visualizing and providing eco-driving advice and analysis results to the user on the terminal, and is displayed as notifications or a dashboard.

[1507] This invention is a system that supports eco-driving by analyzing the user's driving behavior and emotional state in real time using a user's terminal and a server. Specifically, it comprises sensor means for acquiring vehicle acceleration, speed, and position information, emotion engine means for acquiring the user's facial expression, voice, body temperature, heart rate, etc., and data transmission means for transmitting this data to the server at regular intervals.

[1508] Hardware and Software Details

[1509] A device is a device that can be easily accessed by a user, such as a smartphone, tablet, or in-car device. The device is equipped with an accelerometer, speed sensor, GPS module, camera, microphone, smartwatch, etc., and uses these to collect data.

[1510] Examples:

[1511] Car driving data and user facial expression data are collected using a smartphone.

[1512] The smartwatch is used to measure the user's heart rate and body temperature.

[1513] Acquiring and Sending Data

[1514] The device temporarily stores the acquired sensor data and emotion data in a buffer, and after checking the Internet connection, transmits it to the server at regular intervals. If the transmission fails, the data is kept in a local buffer and is retried the next time it is transmitted.

[1515] Data reception and analysis

[1516] The server receives sensor data and emotion data sent from the device and analyzes it in real time using generative artificial intelligence, including analysis of driving patterns, CO2 emissions, driving efficiency, etc. It also incorporates the user's emotion data to provide a comprehensive evaluation.

[1517] Generating and sending recommendations

[1518] The server generates eco-driving recommendations based on the analysis results. These recommendations include advice on how to avoid sudden acceleration, how to improve driving efficiency, and how to reduce user stress. The generated recommendations are sent from the server to the device.

[1519] User Feedback

[1520] The device notifies the user of the recommendations and analysis results sent from the server. This information is presented visually through a user interface. Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[1521] Examples:

[1522] While driving, a pop-up notification will appear saying, "Avoiding sudden acceleration will improve fuel efficiency."

[1523] After completing a drive, check the app's dashboard to see the CO2 emissions and fuel efficiency of that driving session.

[1524] Prompt Sentence Examples

[1525] "What's the trick to avoiding sudden acceleration?"

[1526] "Tell me how to relax when you're feeling stressed"

[1527] "Show me the latest CO2 emissions data"

[1528] This system allows users to receive real-time feedback to improve their driving behavior and personalized advice based on their emotions, helping them to continuously practice eco-driving and contribute to reducing environmental impact.

[1529] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1530] Step 1:

[1531] The user launches the application.

[1532] Specific operation: Tap to launch an application on your smartphone or in-car device.

[1533] Input: User actions.

[1534] Output: The startup status of the application.

[1535] Step 2:

[1536] The terminal starts collecting data using the sensor means.

[1537] Specific operation: The acceleration sensor, speed sensor, and GPS module in the device are activated to collect data in real time.

[1538] Input: Data obtained from the device's sensors (acceleration, speed, location information).

[1539] Output: Temporarily buffered sensor data.

[1540] Step 3:

[1541] The terminal begins collecting emotion data using the emotion engine means.

[1542] Specific operation: The front camera captures the user's facial expressions, the microphone analyzes the tone and tension of the voice, and the smartwatch collects body temperature and heart rate data.

[1543] Input: Camera footage, audio data, body temperature, heart rate.

[1544] Output: Emotion data temporarily stored in a buffer.

[1545] Step 4:

[1546] The terminal stores the collected sensor data and emotion data in a buffer and transmits them to the server using the data transmission means.

[1547] What it does: Checks for internet connectivity and sends data if connection is established. If the transmission fails, it buffers the data and tries to send it again.

[1548] Input: Buffered sensor data and emotion data.

[1549] Output: Data sent to the server or held in the buffer.

[1550] Step 5:

[1551] The server receives the sensor data and emotion data transmitted from the terminal.

[1552] Specific operation: The server receives the data and checks the metadata such as the timestamp and device ID.

[1553] Input: Sensor data and emotion data sent from the device.

[1554] Output: Data received, data checked for integrity.

[1555] Step 6:

[1556] The server uses generative artificial intelligence tools to analyze the data in real time.

[1557] Specific operation: Using an AI model, the system analyzes driving patterns, CO2 emissions, driving efficiency, etc., and makes a comprehensive assessment incorporating emotional data.

[1558] Input: Received sensor data and emotion data.

[1559] Output: Analysis results (driving patterns, CO2 emissions, driving efficiency, emotional state).

[1560] Step 7:

[1561] Based on the analysis results, the server generates recommendations for eco-driving.

[1562] Specific actions: Based on the analysis results, advice to improve driving efficiency and suggestions to reduce stress are generated.

[1563] Input: Analysis results.

[1564] Output: The generated recommendations.

[1565] Step 8:

[1566] The server transmits the generated recommendations to the terminal.

[1567] Specific operation: Recommendations are sent to the device and information is provided in a visually easy-to-understand format.

[1568] Input: Generated recommendations.

[1569] Output: Recommendations sent to the device.

[1570] Step 9:

[1571] The terminal notifies the user of the recommendation using a user interface means.

[1572] Specific operation: Eco-driving advice is displayed as a pop-up notification while driving, and detailed data can be viewed on the dashboard within the app.

[1573] Input: Recommendations sent from the server.

[1574] Output: Notifications and dashboard display to the user.

[1575] (Application example 2)

[1576] 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."

[1577] Conventional eco-driving support systems only provide advice based on the user's driving behavior and are unable to provide personalized feedback that takes into account the user's emotional state. As a result, there has been a lack of support for reducing the stress and anxiety that users feel while driving and maintaining a comfortable driving environment. The present invention aims to combine driving data and emotional data to provide users with optimal eco-driving advice and personalized feedback based on their emotions, thereby improving driving efficiency and maintaining a comfortable driving environment.

[1578] The identification process by the identification 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 generative artificial intelligence means that analyzes received sensor data and emotion data in real time, recommendation generation means that transmits generated eco-driving advice to the terminal, and user interface means that notifies the user of the transmitted eco-driving advice. This makes it possible to simultaneously provide the user with feedback to improve their driving behavior and relax their emotions.

[1579] "Sensor means" refers to a device for acquiring information on the acceleration, speed, and position of a vehicle.

[1580] "Data transmission means" refers to a device or function for transmitting acquired sensor data to a server at regular intervals.

[1581] "Emotion engine means" refers to a device or software for analyzing a user's facial expressions, voice, body temperature, and heart rate to obtain emotion data.

[1582] "Emotion data transmission means" refers to a device or function for transmitting acquired emotion data to a server.

[1583] "Generative artificial intelligence means" refers to software or a system installed on a server for analyzing received sensor data and emotion data in real time.

[1584] "Recommendation generation means" refers to a device or function for transmitting eco-driving advice generated by the generative artificial intelligence means to a terminal.

[1585] "User interface means" refers to a device or software for notifying the user of the eco-driving advice sent by the recommendation generation means.

[1586] "Dashboard" refers to a screen or interface for displaying the analysis results of a user's driving behavior and emotional data.

[1587] "Notification means" refers to a device or function that provides a user with messages or alerts to promote reduction in gasoline costs, improvement in awareness of safe driving, and emotional relaxation.

[1588] "Buffering and retry function" refers to a function for checking the success and failure of transmission and retransmitting failed data.

[1589] The present invention relates to an eco-driving support system that combines a sensor means and an emotion engine means. Hereinafter, an embodiment of the present invention will be described in detail.

[1590] 1. System Configuration

[1591] The eco-driving support system of the present invention comprises a terminal and a server. The terminal is equipped with sensor means for acquiring vehicle acceleration, speed, and position information, and emotion engine means for analyzing the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. The terminal also has data transmission means for transmitting the collected sensor data and emotion data to the server at regular intervals.

[1592] The server is equipped with a generative artificial intelligence means for analyzing the received sensor data and emotion data in real time. The generated eco-driving advice is sent to the terminal by the recommendation generation means and notified to the user via the user interface means.

[1593] 2. Hardware and Software

[1594] Hardware: Smartphones (camera, microphone, accelerometer, GPS module) and biometric sensors such as smartwatches.

[1595] Software: smartphone applications, sentiment analysis software, generative artificial intelligence (AI) modules.

[1596] 3. Data processing and calculation

[1597] The device uses sensor means to acquire vehicle acceleration, speed, and position information in real time. This data is temporarily stored in a buffer. At the same time, the device uses emotion engine means to analyze the user's facial expressions, voice, body temperature, and heart rate to acquire emotion data. This data is also stored in the buffer.

[1598] When an internet connection is established, the device transmits this data to a server, which checks the integrity of the received data and stores it in a database. A generative artificial intelligence means analyzes this data in real time to calculate driving patterns, CO2 emissions, driving efficiency, and the user's emotional state.

[1599] The recommendation generation means generates optimal eco-driving advice for the user based on the results of the generative artificial intelligence means and transmits it to the terminal. The user interface means provides this advice to the user via pop-up notifications or a dashboard.

[1600] 4. Examples and prompts

[1601] Specific examples

[1602] App usage: The user launches a smartphone app while driving autonomously, and driving and emotional data are collected in real time.

[1603] Examples of feedback: Avoiding sudden acceleration will improve fuel efficiency, Your heart rate is high. Please relax, etc.

[1604] Prompt statement

[1605] Collect driving and emotional data in real time and generate feedback for eco-driving. Sensor data includes acceleration, speed, and GPS information. Emotional data includes facial expressions, tone of voice, and heart rate. Feedback to the user should provide advice on driving style and emotional relaxation.

[1606] This system allows users to improve driving efficiency while maintaining a comfortable driving environment.

[1607] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1608] Step 1:

[1609] The terminal acquires the acceleration, speed, and position information of the vehicle using the sensor means. The input at this time is raw sensor data related to the movement of the vehicle, and the output is acceleration, speed, and position information. Specifically, the terminal acquires data directly from the sensor in the terminal and temporarily stores it in a buffer.

[1610] Step 2:

[1611] The device uses an emotion engine to analyze the user's facial expressions, voice, body temperature, and heart rate to obtain emotional data. The input is raw data such as the user's facial image, voice, body temperature, and heart rate, and the output is analyzed emotional data. Specifically, data is obtained in real time from the camera, microphone, and biometric sensors, and processed using analysis software.

[1612] Step 3:

[1613] The device transmits the acquired sensor data and emotion data to the server at regular intervals. The input is the sensor data and emotion data stored in the buffer, and the output is the status of whether the transmission to the server was successful or not. Specifically, the device checks the Internet connection, transmits the data if the connection is established, and then checks the transmission status.

[1614] Step 4:

[1615] The server checks the integrity of the received sensor data and emotion data, and if there are no problems, stores it in a database. The input is the raw data sent from the device, and the output is the organized data storage status. Specifically, the server converts the received data into the correct format and inserts it into the database.

[1616] Step 5:

[1617] The server uses generative artificial intelligence (AI) means to analyze the driving patterns, CO2 emissions, driving efficiency, and the user's emotional state in real time based on the received data. The input is sensor data and emotional data, and the output is the analysis results. Specifically, the data is input into the generative AI platform, and the algorithm is executed to obtain the required analysis results.

[1618] Step 6:

[1619] Based on the analysis results, the server uses the recommendation generation means to generate optimal eco-driving advice for the user and sends it to the terminal. The input is the analysis results obtained from the generative artificial intelligence means, and the output is eco-driving advice. Specifically, the server creates feedback content based on the analysis results and sends it to the terminal.

[1620] Step 7:

[1621] The device notifies the user of the received eco-driving advice through a user interface means. The input is the eco-driving advice from the server, and the output is a notification to the user. Specifically, this is done by displaying a pop-up notification on the device screen or displaying detailed advice on a dashboard within the app.

[1622] Step 8:

[1623] The user will review their driving behavior based on the provided eco-driving advice and adjust their behavior according to their emotional state. The input is notifications from the device and dashboard information, and the output is improvements to the user's driving behavior and emotional state. Specifically, the user will take actions such as avoiding sudden acceleration and playing music for relaxation.

[1624] 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.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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).

[1631] 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.

[1632] 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."

[1633] 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.

[1634] 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).

[1635] 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.

[1636] 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.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] The following is further disclosed regarding the above embodiment.

[1646] (Claim 1)

[1647] a sensor means provided in the terminal for acquiring acceleration, speed, and position information of the vehicle;

[1648] a data transmission means for transmitting the acquired sensor data to a server at regular intervals;

[1649] a generative artificial intelligence means provided in the server and configured to analyze the received sensor data in real time;

[1650] a recommendation generating means for transmitting the eco-driving advice generated by the generative artificial intelligence means to the terminal;

[1651] a user interface means in the terminal that notifies the user of the eco-driving advice transmitted by the recommendation generation means;

[1652] A system including:

[1653] (Claim 2)

[1654] The system according to claim 1, characterized in that the user interface means has a dashboard for displaying the analysis results of the user's driving behavior, and is equipped with a notification means for promoting reduction in gasoline costs and improvement of awareness of safe driving.

[1655] (Claim 3)

[1656] 2. The system according to claim 1, wherein said data transmission means includes a buffering and retry function for checking whether a transmission has been successful or not, and for retransmitting data that has failed.

[1657] "Example 1"

[1658] (Claim 1)

[1659] a sensor means provided in the terminal for acquiring acceleration, speed, and position information of the vehicle;

[1660] a data transmission means for transmitting the acquired sensor data to a server at regular intervals;

[1661] a generative artificial intelligence means provided in the server and configured to analyze the received sensor data in real time;

[1662] a recommendation generating means for transmitting the eco-driving advice generated by the generative artificial intelligence means to the terminal;

[1663] The generative artificial intelligence means calculates driving patterns, CO2 emissions, and driving efficiency;

[1664] a buffering and retry function for storing data in a buffer and attempting to retransmit the data when the data transmission means fails to transmit the data;

[1665] a user interface means in the terminal that notifies the user of the eco-driving advice transmitted by the recommendation generation means;

[1666] A system including:

[1667] (Claim 2)

[1668] The system according to claim 1, characterized in that the user interface means has a dashboard for displaying the analysis results of the user's driving behavior, and is equipped with a notification means for promoting reduction in gasoline costs and improvement of awareness of safe driving.

[1669] (Claim 3)

[1670] 2. The system according to claim 1, wherein said data transmission means includes a buffering and retry function for checking whether a transmission has been successful or not, and for retransmitting data that has failed.

[1671] "Application Example 1"

[1672] (Claim 1)

[1673] a sensor means provided in the terminal for acquiring acceleration, speed, and position information of the vehicle;

[1674] a data transmission means for transmitting the acquired sensor data to a server at regular intervals;

[1675] a generative artificial intelligence means provided in the server and configured to analyze the received sensor data in real time;

[1676] a recommendation generating means for transmitting the eco-driving advice generated by the generative artificial intelligence means to the terminal;

[1677] a user interface means in the terminal that notifies the user of the eco-driving advice transmitted by the recommendation generation means;

[1678] an analysis means for calculating driving patterns, CO2 emissions, and driving efficiency in real time using a generative artificial intelligence model;

[1679] a pop-up notification means for displaying the eco-driving advice as a pop-up notification at regular intervals on the user terminal;

[1680] A system including:

[1681] (Claim 2)

[1682] The system according to claim 1, characterized in that the user interface means has a dashboard for displaying the analysis results of the user's driving behavior, and is equipped with a notification means for promoting reduction in gasoline costs and improvement of awareness of safe driving.

[1683] (Claim 3)

[1684] 2. The system according to claim 1, wherein said data transmission means includes a buffering and retry function for checking whether a transmission has been successful or not, and for retransmitting data that has failed.

[1685] "Example 2: Combining Emotion Engines"

[1686] (Claim 1)

[1687] a sensor means provided in the terminal for acquiring acceleration, speed, and position information of the vehicle;

[1688] emotion engine means provided in the terminal for acquiring the user's facial expression, voice, body temperature, heart rate, etc.;

[1689] a data transmission means for transmitting the acquired sensor data and emotion data to a server at regular intervals;

[1690] a generative artificial intelligence means provided in the server for analyzing the received sensor data and emotion data in real time;

[1691] a recommendation generating means for transmitting the eco-driving advice generated by the generative artificial intelligence means to the terminal;

[1692] a user interface means in the terminal that notifies the user of the eco-driving advice transmitted by the recommendation generation means;

[1693] A system including:

[1694] (Claim 2)

[1695] The system according to claim 1, characterized in that the user interface means has a dashboard for displaying the user's driving behavior and emotion analysis results, and is provided with a notification means for promoting fuel cost reduction and improved awareness of safe driving.

[1696] (Claim 3)

[1697] 2. The system according to claim 1, wherein said data transmission means includes a buffering and retry function for checking whether transmission has been successful or not, and for retransmitting failed data.

[1698] "Application example 2 when combining emotion engines"

[1699] (Claim 1)

[1700] a sensor means provided in the terminal for acquiring acceleration, speed, and position information of the vehicle;

[1701] a data transmission means for transmitting the acquired sensor data to a server at regular intervals;

[1702] emotion engine means for acquiring emotion data by analyzing the user's facial expression, voice, body temperature, and heart rate;

[1703] emotion data transmission means for transmitting the acquired emotion data to a server;

[1704] a generative artificial intelligence means provided in the server for analyzing the received sensor data and emotion data in real time;

[1705] a recommendation generating means for transmitting the eco-driving advice generated by the generative artificial intelligence means to the terminal;

[1706] a user interface means in the terminal that notifies the user of the eco-driving advice transmitted by the recommendation generation means;

[1707] A system including:

[1708] (Claim 2)

[1709] The system according to claim 1, characterized in that the user interface means has a dashboard for displaying the analysis results of the user's driving behavior and emotional data, and is provided with a notification means for promoting reduction in gasoline costs, improvement in awareness of safe driving, and emotional relaxation.

[1710] (Claim 3)

[1711] 2. The system according to claim 1, wherein the data transmission means and emotion data transmission means include a buffering and retry function for checking whether transmission has been successful or not, and for retransmitting failed data. [Explanation of symbols]

[1712] 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 sensor means provided in the terminal for acquiring acceleration, speed, and position information of the vehicle; a data transmission means for transmitting the acquired sensor data to a server at regular intervals; a generative artificial intelligence means provided in the server and configured to analyze the received sensor data in real time; a recommendation generating means for transmitting the eco-driving advice generated by the generative artificial intelligence means to the terminal; a user interface means in the terminal that notifies the user of the eco-driving advice transmitted by the recommendation generation means; A system including:

2. The system according to claim 1, characterized in that the user interface means has a dashboard for displaying the analysis results of the user's driving behavior, and is provided with a notification means for promoting reduction in gasoline costs and improvement of awareness of safe driving.

3. 2. The system according to claim 1, wherein said data transmission means includes a buffering and retry function for checking whether a transmission has been successful or not, and for retransmitting data that has failed.

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