System

Abstract

A system is provided.SOLUTION: The system includes a user input means for allowing a user to input vehicle type information and driving data and transmitting them to a server, a means for allowing the server to store the received vehicle type information and driving data in a database, a generation means for allowing the server to analyze the driving data and generate a fuel consumption improvement method, and a means for notifying the user of the generated fuel consumption improvement method.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] Consumers who own personal vehicles are increasingly seeking to improve fuel efficiency and enjoy a more economical driving lifestyle. However, there is currently a lack of tools that provide specific, personalized fuel efficiency improvement methods that take into account individual driving styles and fueling records. In addition, improving fuel efficiency requires comparing fuel efficiency with other users, as well as price information and campaign information from local fuel supply facilities. However, this information is scattered across multiple platforms, making it difficult to utilize it efficiently. Furthermore, there is a lack of a centralized way to manage vehicle inspection and maintenance schedules. [Means for solving the problem]

[0005] The present invention provides a user input means for a user to input vehicle model information and driving data and transmit the data to a server, and a means for the server to store the received vehicle model information and driving data in a database. The server also has a generation means for analyzing the driving data and generating fuel economy improvement methods. The generated fuel economy improvement methods are then notified to the user. The server also has a generation means for acquiring actual fuel economy data from other users of the same vehicle and generating a comparison report, enabling fuel economy comparisons between users. The server also has a generation means for collecting and storing price information and campaign information from local fuel supply facilities and generating optimal information based on the user's refueling patterns and location information. The generated information is notified to the user, enabling efficient fuel economy management. This not only allows users to improve fuel economy and enjoy a more economical driving experience, but also allows for centralized management of vehicle inspection and maintenance schedules.

[0006] The "user input means" is an interface that allows the user to input vehicle model information and driving data and transmit them to the server.

[0007] "Server" means a computer device that receives, processes, and analyzes data entered by users.

[0008] The "database" is a data storage system for systematically storing and managing received vehicle information and driving data.

[0009] The "generation means" is a processing function for generating information such as fuel efficiency improvement methods and reports based on input data.

[0010] "Fuel supply facility" refers to a facility for supplying fuel, such as a gas station.

[0011] "Price information" is information relating to the selling price of fuel.

[0012] "Campaign information" refers to information about discounts and special offers offered by fuel supply facilities.

[0013] "Driving data" refers to data related to the user's daily driving behavior, including, for example, driving distance, fuel used, driving time, and the like.

[0014] A "comparison report" is a report that summarizes the results of comparing the actual fuel consumption data of other users of the same vehicle model.

[0015] "Notification means" is a function for notifying the user of generated information and advice.

[0016] "Actual fuel consumption data" refers to fuel consumption data calculated from the distance the user actually traveled and the fuel used.

[0017] "User refueling patterns" refers to the habits of a user regarding how often and when they refuel.

[0018] "Location information" is data about a user's current location or a specific location.

[0019] "Vehicle inspection" is a periodic inspection to check whether a vehicle meets legally required standards.

[0020] "Maintenance" refers to regular maintenance work to maintain the performance and safety of a vehicle. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The present invention relates to a system that allows users to input vehicle model information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[0043] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[0044] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[0045] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[0046] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[0047] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[0048] Specific examples

[0049] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user of a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0050] Example 2: User B has registered his / her refueling habits and is checking local gas station information in the app. The server uses the collected price and campaign information to notify him / her that there is a discount at a particular gas station on the weekend. User B can take advantage of this to refuel economically.

[0051] Example 3: User C enters the schedule for vehicle inspection dates into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him to smoothly prepare for the inspection.

[0052] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle model information and driving data.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[0056] Step 2:

[0057] The terminal transmits the input information to the server.

[0058] Step 3:

[0059] The server stores the received vehicle information and driving data in a database.

[0060] Step 4:

[0061] The server retrieves the user's driving data from the database.

[0062] Step 5:

[0063] The server uses generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, it analyzes the frequency of sudden acceleration and provides advice on how to reduce it.

[0064] Step 6:

[0065] The server notifies the user of the generated fuel efficiency improvement method.

[0066] Step 7:

[0067] The device receives the notification and displays it to the user, for example, "Reducing sudden acceleration will improve fuel economy by 5%."

[0068] Step 8:

[0069] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[0070] Step 9:

[0071] The server notifies the user of the comparison report.

[0072] Step 10:

[0073] The device receives the notification and displays it to the user in a ranking format.

[0074] Step 11:

[0075] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[0076] Step 12:

[0077] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[0078] Step 13:

[0079] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[0080] Step 14:

[0081] The device receives the notification and displays it to the user.

[0082] Step 15:

[0083] Users enter vehicle inspection and maintenance schedules into the application.

[0084] Step 16:

[0085] The terminal transmits schedule information to the server.

[0086] Step 17:

[0087] The server stores the received schedule information in a database.

[0088] Step 18:

[0089] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[0090] Step 19:

[0091] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[0092] Step 20:

[0093] The device receives the reminder notification and displays it to the user.

[0094] Example 1

[0095] 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."

[0096] Conventional vehicle management systems have difficulty providing personalized advice on improving fuel efficiency based on users' driving data, and they are unable to provide a wide range of functions in a unified manner, such as comparing fuel efficiency with other users, notifying them of fuel price information, or managing vehicle inspection and maintenance schedules. This has meant that users have to use different applications and systems individually, which has often been inconvenient.

[0097] 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.

[0098] In this invention, the server includes a user input means for the user to input vehicle information and driving data and send it to the server, a means for the server to store the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method using a generative AI model, a means for notifying the user of the generated fuel economy improvement method, a means for the server to acquire fuel economy data of similar users and generate a comparison report, a means for the server to collect price information and campaign information from fuel supply facilities and generate and notify optimal information based on the user's refueling patterns and location information, and a means for the user to input vehicle inspection dates and maintenance schedules, the server to store this, and generate and notify reminders at appropriate times. This allows users to comprehensively improve fuel economy and manage their vehicles using a single system.

[0099] "User" means any individual or legal entity that owns a vehicle and uses the system.

[0100] "Vehicle information" refers to basic information such as the car's manufacturer, model, and year.

[0101] "Driving data" refers to information about a user's driving, such as daily mileage, frequency of sudden acceleration and braking, and average speed.

[0102] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[0103] "Database" refers to a collection of information in which driving data and vehicle information are stored by a server.

[0104] "Generative AI model" refers to the artificial intelligence model used by the server to analyze driving data and generate fuel efficiency improvement advice appropriate for each individual user.

[0105] "Methods for improving fuel efficiency" refers to advice on specific driving methods and actions to improve fuel efficiency.

[0106] "Notification means" refers to the means for sending messages or alerts from the server to the user's device.

[0107] "Comparison Report" refers to a report summarizing the results of comparing fuel economy data with other users.

[0108] "Fuel distribution facility" means a gas station or other facility that provides fuel.

[0109] "Price Information" refers to information regarding the price of fuel offered at a fuel supply facility.

[0110] "Campaign Information" refers to information regarding discounts and special offers offered at fuel supply facilities.

[0111] "Reminder" refers to a message or alert that is sent to the user based on a pre-set schedule.

[0112] "Inspection Date" means the date on which a vehicle is to be inspected.

[0113] "Maintenance Schedule" means a date scheduled for performing maintenance on a Vehicle.

[0114] The present invention relates to a system that allows users to input vehicle information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[0115] Hardware and software used

[0116] The present invention is implemented mainly using the following hardware and software.

[0117] Hardware: smartphones, tablets, servers, internet connections

[0118] Software: applications (installed on smartphones or tablets), database management systems, generative AI models (e.g., OpenAI's GPT-4)

[0119] Data processing and calculation flow

[0120] Users use a smartphone or tablet to enter vehicle information and driving data into the application, including distance traveled, frequency of sudden acceleration and braking, average speed, etc. The entered information is sent by the device to a server via an internet connection.

[0121] The server stores the received data in a database. This data is efficiently managed using a database management system. The server analyzes the driving data stored in the database and uses a generative AI model to generate fuel efficiency improvement advice tailored to each individual user. For example, the server analyzes the user's driving patterns and provides specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0122] The generated advice is sent from the server to the user's device, where it can be viewed on the application. The server also retrieves fuel economy data from a database of similar users and compares it with the user's fuel economy. The comparison results are displayed in a ranking format and notified to the user.

[0123] The server also collects price and campaign information from fuel supply facilities via the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a discount campaign is being held at a nearby gas station, the server will notify the user of this information.

[0124] Users can also input vehicle inspection dates and maintenance schedules into the application. The server stores the input schedule in a database and generates reminders at appropriate times to notify the user's device. For example, a notification such as "The next vehicle inspection date is November 15, 2023" may be sent.

[0125] Specific examples

[0126] Example 1:

[0127] A scenario in which a user enters daily driving data into an app and receives advice on how to improve fuel efficiency.

[0128] Prompt: "Generate fuel economy recommendations based on the user's driving data."

[0129] Example 2:

[0130] A scenario in which refueling patterns are registered and local gas station information is utilized.

[0131] Prompt: "Regularly collect pricing information and promotions at nearby gas stations."

[0132] Example 3:

[0133] Scenario for entering vehicle inspection date and receiving reminder notifications.

[0134] Prompt: "The next inspection date is November 15, 2023."

[0135] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle information and driving data.

[0136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0137] Step 1:

[0138] Using the device, users input vehicle information and driving data into the application, such as "Toyota Prius 2020 model," "Today's mileage: 50km, 5 sudden accelerations, 3 sudden brakings," and so on.

[0139] Input: Vehicle information, driving data

[0140] Output: Data sent from the device to the server

[0141] Step 2:

[0142] The device sends the entered vehicle information and driving data to a server via an internet connection. The moment the "Send" button is pressed, the information is sent to the server, and the server returns a confirmation message saying "Data saved successfully."

[0143] Input: Data entered into the terminal

[0144] Output: Data sent to the server

[0145] Step 3:

[0146] The server stores the received vehicle information and driving data in a database, which is efficiently managed using a database management system.

[0147] For example, the driving data of user A is saved as "distance traveled 50km, sudden acceleration 5 times, sudden braking 3 times."

[0148] Input: Data sent from the terminal

[0149] Output: Data stored in the database

[0150] Step 4:

[0151] The server analyzes the driving data stored in the database and generates fuel efficiency improvement advice using a generative AI model (e.g., OpenAI's GPT-4). For example, the generative AI model is used to generate specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0152] Input: Driving data stored in the database

[0153] Output: Fuel economy improvement advice

[0154] Step 5:

[0155] The server then sends the generated fuel efficiency improvement advice to the user's device, and the user confirms the specific advice on the application, such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0156] Input: Generated fuel economy improvement advice

[0157] Output: Advice sent to the user's device

[0158] Step 6:

[0159] The server retrieves fuel economy data from the database from other users of the same type, compares it with the user's fuel economy data, and displays it in a ranking format. For example, it generates a comparison report that notifies the user that "your fuel economy is in the top 20% overall."

[0160] Input: Fuel economy data of other users retrieved from the database

[0161] Output: Ranking comparison report

[0162] Step 7:

[0163] The server collects price information and campaign information from fuel supply facilities via the Internet and stores it in a database. For example, it collects information about a 10% discount campaign that is being held at a nearby gas station on the weekend.

[0164] Input: Fuel supply facility price information, campaign information

[0165] Output: Price information and campaign information stored in the database

[0166] Step 8:

[0167] The server analyzes the user's fueling patterns and location information and notifies the user of price and campaign information at the optimal time, for example, notifying the user that a discount campaign is being held at a nearby gas station.

[0168] Input: User's fueling patterns, location information

[0169] Output: Price information and campaign information notified to the user

[0170] Step 9:

[0171] Users input their vehicle inspection and maintenance dates into the application. For example, they might enter "next vehicle inspection date is November 15, 2023."

[0172] Input: Inspection date, maintenance schedule

[0173] Output: Schedule data sent to the server

[0174] Step 10:

[0175] The server stores the entered vehicle inspection dates and maintenance schedules in a database and generates reminders to notify the user at appropriate times, for example, "The next vehicle inspection date is November 15, 2023."

[0176] Input: Vehicle inspection dates and maintenance schedules stored in the database

[0177] Output: Reminder to be sent to the user

[0178] (Application example 1)

[0179] 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."

[0180] Conventional fuel economy improvement systems are limited to providing advice based on user behavior data, and do not adequately integrate or personalize information. Furthermore, fuel supply and maintenance information is not managed centrally, which often increases complexity for users.

[0181] 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.

[0182] In this invention, the server includes means for the user to input vehicle model information and driving data and transmit them to the server, means for the server to store the received vehicle model information and driving data in a database, means for the server to analyze the driving data and generate fuel economy improvement methods, means for notifying the user of the generated fuel economy improvement methods, means for the server to collect local fuel price information and campaign information based on location information and generate optimal information to support the user in economical refueling, and means for managing vehicle inspection and maintenance information and generating reminders at appropriate times. This enables fuel economy improvement and information management to meet the diverse needs of users.

[0183] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[0184] The "means for storing in a database" is a means for storing the vehicle model information and driving data received by the server in a database.

[0185] The "analysis means" is a means for analyzing the driving data received by the server and generating a method for improving fuel economy.

[0186] The "notification means" is a means for notifying the user of the generated fuel economy improvement method.

[0187] The "location information collection means" is a means by which the server collects local fuel price information and campaign information based on location information.

[0188] The "generation means" is a means for generating optimal information and reminders to assist the user in economical refueling based on the information collected by the server.

[0189] The "vehicle inspection and maintenance management means" is a means by which the server manages vehicle inspection and maintenance information and generates reminders at appropriate times.

[0190] This invention is a system in which users input vehicle model information and driving data, and based on that, a server provides personalized fuel efficiency improvement advice and centrally manages fuel price information, campaign information, and vehicle inspection and maintenance notifications. Users input vehicle model information and driving data into an application via a device such as a smartphone or tablet. The device then sends this information to the server, which then stores it in a database.

[0191] The server uses a generative AI model based on the saved driving data to generate personalized fuel economy improvement methods. For example, based on the user's driving data, it can provide specific advice such as "reducing sudden acceleration will improve fuel economy by 5%." This advice is notified to the user and can be viewed on the device.

[0192] The server also has a function to obtain the actual fuel consumption data of other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[0193] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores this data in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[0194] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications such as "The next vehicle inspection date is November 15, 2023," allowing them to prepare in advance.

[0195] For example, if a user inputs information such as "Vehicle information: general passenger car, Driving data: frequent sudden acceleration, Location: metropolitan area," the server will use the AI ​​to generate fuel efficiency improvement advice based on this information, providing specific advice such as "reducing sudden acceleration will improve fuel efficiency by 10%."

[0196] An example of a prompt sentence is, "Please generate advice to improve fuel efficiency based on the user information. The vehicle model information is 'general passenger car' and the driving data is 'frequent sudden acceleration'." In this way, the server provides the user with optimal advice and realizes multifaceted information management.

[0197] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0198] Step 1:

[0199] The user inputs vehicle model information and driving data, which are then sent to the server via the device. The input data includes vehicle model information (e.g., a typical passenger car) and driving data (e.g., frequent sudden acceleration). The device formats this information and sends it to the server as an HTTP request.

[0200] Input: Vehicle information, driving data

[0201] Output: Data sent to the server

[0202] Step 2:

[0203] The server analyzes the received vehicle information and driving data and saves it in a database. The server checks the integrity of the data and stores it in the database in an appropriate format. For example, the received data is saved in the database in JSON format.

[0204] Input: Received data

[0205] Output: Data stored in the database

[0206] Step 3:

[0207] The server uses the stored driving data to generate personalized fuel economy improvement methods using a generative AI model. Features of the driving data are extracted and input into the AI ​​model to generate specific advice (e.g., reducing sudden acceleration will improve fuel economy by 5%).

[0208] Input: Saved driving data

[0209] Output: Generated fuel economy improvement advice

[0210] Step 4:

[0211] The server notifies the user of the fuel efficiency improvement method that was generated. The server generates notification data and sends it to the user's device. It is displayed on the device as a pop-up notification or message notification.

[0212] Input: Generated fuel economy improvement advice

[0213] Output: Notification to the user's device

[0214] Step 5:

[0215] The server retrieves the actual fuel consumption data of other users of the same vehicle model and generates a comparison report. The server queries the relevant data from the database, extracts statistical information, and creates a report that displays the fuel consumption rankings.

[0216] Input: Actual fuel consumption data of other users of the same vehicle model

[0217] Output: Generated comparison report

[0218] Step 6:

[0219] The server collects price and campaign information from local fuel supply facilities, and uses an API via the Internet to retrieve the latest price and campaign information and store it in a database.

[0220] Input: Fuel price information and campaign information collected from the Internet

[0221] Output: Price information and campaign information stored in the database

[0222] Step 7:

[0223] The server generates and notifies the user of the most economical options based on their refueling patterns and location. It analyzes location information and patterns and notifies the user of the most economical options (e.g., discount information for nearby gas stations).

[0224] Input: User's fueling patterns, location information

[0225] Output: Notifying the user of the generated information

[0226] Step 8:

[0227] The server manages vehicle inspection and maintenance information and generates reminders at appropriate times based on the schedule information entered by the user, and notifies the user's device.

[0228] Input: Inspection and maintenance schedules entered by the user

[0229] Output: Generated reminder notification

[0230] 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.

[0231] This invention combines an emotion engine with a system that provides personalized advice on improving fuel economy based on user input of vehicle model information and driving data, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications. The emotion engine recognizes the user's emotions and further utilizes this data to provide more appropriate advice and reminders.

[0232] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[0233] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[0234] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[0235] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[0236] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[0237] The emotion engine recognizes the user's emotions in real time, detecting their emotional state while driving and their stress level. This allows the generated fuel efficiency improvement advice and other notifications to be adjusted based on the user's emotional state. For example, if the user is stressed, the advice can be softened, while if the user is relaxed, the advice can be detailed and specific.

[0238] Specific examples

[0239] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is feeling stressed, the advice is softened.

[0240] Example 2: User B has registered his / her refueling patterns and is checking local gas station information in the app. Based on the collected price and campaign information, the server notifies him / her that there is a discount at a specific gas station on the weekend. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it will provide him / her with detailed campaign information.

[0241] Example 3: User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[0242] As described above, this system not only achieves multifaceted improvements in fuel efficiency and an economical driving lifestyle based on the user's vehicle information, driving data, and emotional state, but also provides a more personalized experience through emotion recognition.

[0243] The processing flow will be explained below.

[0244] Step 1:

[0245] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[0246] Step 2:

[0247] The terminal transmits the input information to the server.

[0248] Step 3:

[0249] The server stores the received vehicle information and driving data in a database.

[0250] Step 4:

[0251] The server retrieves the user's driving data from the database.

[0252] Step 5:

[0253] The server uses the generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, if the user frequently accelerates suddenly, it generates advice on reducing sudden acceleration.

[0254] Step 6:

[0255] The server notifies the user how to improve fuel economy, for example, "reducing sudden acceleration will improve fuel economy by 5%."

[0256] Step 7:

[0257] The device receives the notification and displays it to the user.

[0258] Step 8:

[0259] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[0260] Step 9:

[0261] The server notifies the user with a comparison report, for example, "Your fuel economy is 90% of the average of other users."

[0262] Step 10:

[0263] The device receives the notification and displays it to the user in a ranking format.

[0264] Step 11:

[0265] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[0266] Step 12:

[0267] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[0268] Step 13:

[0269] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[0270] Step 14:

[0271] The device receives the notification and displays it to the user.

[0272] Step 15:

[0273] Users enter vehicle inspection and maintenance schedules into the application.

[0274] Step 16:

[0275] The terminal transmits schedule information to the server.

[0276] Step 17:

[0277] The server stores the received schedule information in a database.

[0278] Step 18:

[0279] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[0280] Step 19:

[0281] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[0282] Step 20:

[0283] The device receives the reminder notification and displays it to the user.

[0284] Step 21:

[0285] The emotion engine recognizes the user's emotional state in real time and collects emotional data while driving.

[0286] Step 22:

[0287] The server analyzes the user's emotional state based on emotional data and determines their stress level and relaxation state while driving.

[0288] Step 23:

[0289] The server adjusts the generated fuel efficiency improvement methods and notification content based on the user's emotional state. For example, if the user is feeling stressed, the advice will be softened.

[0290] Step 24:

[0291] The server sends tailored advice and notifications to the user.

[0292] Step 25:

[0293] The device receives the notification and displays it to the user, for example, displaying a message such as "Relax and keep driving."

[0294] Example 2

[0295] 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."

[0296] When receiving advice on fuel economy and maintenance, the advice is often uniform and does not take into account the individual user's driving situation or emotional state, making it difficult to provide effective advice.Furthermore, fuel price information and campaign information are not provided at a time that meets the needs of individual users, making it difficult to support users' economical car lifestyles.

[0297] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means by which the user inputs vehicle model information and driving data and transmits them to the server, a means by which the server stores the received vehicle model information and driving data in a database, a generation means by the server analyzing the driving data and generating a fuel economy improvement method, a generation means by the server generating personalized fuel economy improvement advice using a generation AI model, and a means by which the server notifies the user of the generated fuel economy improvement method. This makes it possible to provide more effective and timely advice and information according to the user's individual driving situation and emotional state.

[0298] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[0299] A "server" is a central computing device that receives, stores, and processes data sent by users.

[0300] "Database" means a structured data storage system for storing received vehicle type information and driving data.

[0301] The "generation means" is a means for analyzing the data acquired by the server and generating methods for improving fuel efficiency and other information.

[0302] A "generative AI model" is an artificial intelligence model that generates personalized advice and reports based on data.

[0303] "Personalized fuel economy improvement advice" is specific suggestions for improving fuel economy generated based on an individual user's driving data.

[0304] "Notification means" refers to a means for notifying the user of the generated information or advice.

[0305] The "Emotion Engine" is a function that recognizes the user's emotional state in real time and adjusts advice and notifications based on that information.

[0306] This invention is a system that provides personalized advice on improving fuel economy based on user input of vehicle information and driving data. The system centrally manages notifications of fuel price information and campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications, and uses an emotion engine to provide appropriate advice and reminders based on the user's emotional state.

[0307] First, the user uses an application on their smartphone or tablet to input vehicle information and driving data, such as the vehicle's make and model, annual mileage, average speed, etc. The device then sends the input information to the server.

[0308] The server stores the received vehicle information and driving data in a database. Database management systems such as MySQL and PostgreSQL can be used. The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4). The generative AI model generates advice for individual users on how to improve fuel efficiency based on the driving data. An example of a specific prompt is, "Please generate advice for improving fuel efficiency based on the user's driving data."

[0309] The generated advice is sent from the server to the device. Firebase Cloud Messaging can be used for this notification. For example, a notification saying "Reducing sudden acceleration will improve fuel economy by 5%" may be sent to the device. The server also has a function to obtain actual fuel economy data from other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel economy compares to other users. The comparison of actual fuel economy data is displayed in a ranking format to increase user motivation.

[0310] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[0311] Users also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. For example, a notification saying "The next vehicle inspection date is November 15, 2023" is sent to the user and displayed on the device.

[0312] The emotion engine recognizes the user's emotional state in real time. The device detects the user's emotional state using the emotion engine and sends that data to the server. The server then adjusts the generated fuel efficiency improvement methods and other notifications based on this emotional data. For example, if the user is feeling stressed, the advice can be expressed in a gentler tone. On the other hand, if the user is relaxed, specific improvement methods can be provided in detail. An example of a specific prompt is, "Please generate advice appropriate for when the user is feeling stressed."

[0313] Example 1:

[0314] User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses the generative AI model to provide personalized fuel efficiency improvement advice, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is stressed, the advice is softened.

[0315] Example prompts for generative AI models:

[0316] "Please change the advice that reducing hard acceleration will improve fuel economy by 5% to something more appropriate for when the user is stressed."

[0317] Example 2:

[0318] User B registers his / her refueling patterns and checks local gas station information on the app. Based on the collected price and campaign information, the server notifies him / her that there are discounts at specific gas stations on weekends. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it provides detailed campaign information.

[0319] Example prompts for generative AI models:

[0320] "Change discount information for nearby gas stations to details that users would like to see when they are relaxed."

[0321] Example 3:

[0322] User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[0323] Example prompts for generative AI models:

[0324] "When informing users that their next vehicle inspection date is November 15, 2023, please change the wording to something more appropriate for users with high stress levels."

[0325] In this way, this system not only provides personalized advice on improving fuel economy based on the user's vehicle information and driving data, but also utilizes an emotion engine to provide a more individualized experience based on the user's emotional state. As a whole, the system aims to support users' economical car life and improve driving efficiency.

[0326] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0327] Program processing flow

[0328] Step 1: User data entry

[0329] Users input their vehicle information and driving data into a device (smartphone or tablet), including vehicle model, manufacturer, annual mileage, average speed, etc.

[0330] Input: Vehicle information, driving data

[0331] Output: User input data

[0332] Specific action: Enter data using a form within the app and press the "Submit" button.

[0333] Step 2: Sending data to the server

[0334] The device sends the vehicle information and driving data entered by the user to the server using a secure communication protocol such as HTTPS.

[0335] Input: User-entered data

[0336] Output: Data sent to the server

[0337] Specific operation: Using the terminal's sending function, the input data is encrypted and sent to the server.

[0338] Step 3: Saving to the database

[0339] The server analyzes the received vehicle information and driving data and stores it in a database, which may use a database management system such as MySQL or PostgreSQL.

[0340] Input: Data sent to the server

[0341] Output: Status of completion of saving to database

[0342] Specific behavior: Establishes a database connection and inserts the received data into the appropriate tables.

[0343] Step 4: Analyze driving data and generate fuel economy improvement advice

[0344] The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4) to generate personalized fuel economy improvement advice.

[0345] Input: Driving data stored in the database

[0346] Output: Personalized advice

[0347] Specific operation: Driving data is obtained, and the prompt "Please generate advice for improving fuel efficiency based on the user's driving data" is input into the generative AI model to obtain advice.

[0348] Step 5: Advice Notification

[0349] The server then sends the generated fuel efficiency improvement advice to the user's device using services such as Firebase Cloud Messaging.

[0350] Input: Personalized advice

[0351] Output: Notification completion status to the terminal

[0352] Specific operation: The generated advice is sent to the device via Firebase Cloud Messaging.

[0353] Step 6: Capture actual fuel consumption data and generate comparison reports

[0354] The server obtains the actual fuel consumption data of other users of the same vehicle model and generates a comparison report.

[0355] Input: Actual fuel consumption data of other users obtained from the database

[0356] Output: Comparison report

[0357] Specific operation: Executes a database query to obtain fuel economy data from other users of the same vehicle model and creates a comparison report.

[0358] Step 7: Gather fuel price and promotion information

[0359] The server collects price and promotion information from local fuel supply facilities from the Internet and stores it in a database.

[0360] Input: Pricing and promotion information gathered from the web

[0361] Output: Status of completion of saving to database

[0362] Specific actions: Use APIs and web scraping to obtain information and store it in a database.

[0363] Step 8: Provide optimal information at the right time

[0364] The server notifies the user of the collected information at the optimal time based on the user's refueling patterns and location information.

[0365] Input: Refueling patterns, location information, price information, and campaign information stored in the database

[0366] Output: User notification

[0367] Specific operation: Analyzes the user's refueling patterns and location information and notifies them at the optimal time.

[0368] Step 9: Enter your vehicle inspection and maintenance schedule

[0369] Users enter vehicle inspection and maintenance schedules through the app.

[0370] Input: Vehicle inspection and maintenance schedule

[0371] Output: Schedule data sent to the server

[0372] Specific actions: Enter your schedule in the app and press the "Send" button.

[0373] Step 10: Generate and notify reminders

[0374] The server generates and notifies reminders at appropriate times based on the input schedule.

[0375] Input: Schedules stored in the database

[0376] Output: Reminder notification

[0377] Specific behavior: Set a timer based on your schedule and generate reminders to notify you.

[0378] Step 11: Emotion recognition and advice adjustment

[0379] The emotion engine recognizes the user's emotional state in real time and sends that data to the server, which analyzes the emotional data and adjusts the advice and notifications it generates.

[0380] Input: User emotion data

[0381] Output: Tailored advice based on emotions

[0382] How it works: The emotion engine recognizes emotions in real time and sends that data to the server, which then adjusts the tone and content of the advice based on the emotion data.

[0383] (Application example 2)

[0384] 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."

[0385] Improving automobile fuel efficiency and supporting efficient driving are important challenges, but existing systems are unable to provide personalized advice based on the user's driving data, or centrally manage fuel price information, vehicle inspection and maintenance schedules. Furthermore, they do not provide advice that takes into account the user's emotional state. This means that users cannot receive the information they need at the right time, making efficient vehicle management difficult.

[0386] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user input means through which the user inputs vehicle information and driving data and transmits them to the server, a means for storing the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method, a means for notifying the user of the generated fuel economy improvement method, a means for detecting the user's emotional state in real time using an emotion recognition engine and adjusting advice content using the information, a means for managing vehicle inspection and maintenance schedules and generating reminders at appropriate times, and a means for collecting price information and campaign information from local fuel supply facilities and notifying the user based on the user's refueling pattern and location information. This personalizes fuel economy improvement and vehicle management, making it possible to provide information at appropriate times according to the user's emotional state.

[0387] The "user input means" is a means by which a user inputs vehicle information and driving data and transmits them to the server.

[0388] The "means for storing in a database" refers to the means by which the server records and manages the vehicle information and driving data received from the user.

[0389] The "generation means" is a means by which the server analyzes the driving data and generates a fuel economy improvement method.

[0390] The "notification means" is a means for notifying the user of the generated fuel efficiency improvement method and other information.

[0391] The "emotion recognition engine" is an engine that detects the user's emotional state in real time and uses that information to adjust the content of advice.

[0392] "Means for generating reminders" refers to means for managing schedules for vehicle inspections and maintenance and notifying users at appropriate times.

[0393] The "means for collecting price information and campaign information from fuel supply facilities" refers to a means for collecting price information and campaign information from local fuel supply facilities and notifying the user of such information.

[0394] "Head-mounted displays and mobile devices" are devices that users can wear to check driving data and notifications in real time.

[0395] This invention is a system that uses the user's vehicle information and driving data to provide personalized fuel efficiency improvement advice, vehicle inspection and maintenance reminders, local fuel supply facility information, etc. Furthermore, it is possible to grasp the user's emotional state in real time using an emotion recognition engine and adjust the content of notifications accordingly.

[0396] First, the user inputs vehicle information and driving data using a smartphone, tablet, or head-mounted display. This device is equipped with a user input means that transmits the input information to a server. The server stores the received information in a database and performs the necessary data management.

[0397] The server then analyzes the saved driving data and uses a generative AI model to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or stops suddenly, the server generates specific advice on how to improve fuel efficiency by reducing these actions.

[0398] The generated fuel efficiency improvement methods are sent to the user's device. The content of the notification is adjusted according to the user's emotional state using an emotion recognition engine. For example, if the user is stressed, the advice is given in a gentle manner, while if the user is relaxed, the system provides detailed and precise improvement methods.

[0399] For vehicle inspections and maintenance, users input their schedules into their devices. The server stores this in a database and generates reminders at the appropriate times. This allows users to receive appropriate notifications when inspection or maintenance dates are approaching, allowing them to prepare in advance.

[0400] The server also collects price and promotion information from local fuel supply facilities and notifies users at the optimal time based on their fueling patterns and location, allowing them to refuel economically.

[0401] For illustrative purposes, the following prompt sentences can be used:

[0402] "Reducing sudden acceleration will improve fuel economy. Please provide specific advice based on driving data. If the user is relaxed, please provide detailed instructions on how to improve, but if the user is stressed, please use gentler language."

[0403] This generative AI model is implemented using Python, TensorFlow, and OpenCV to perform emotion recognition and data analysis. The backend uses Flask and Django, and data is managed using a real-time database such as Firebase. Head-mounted displays and mobile devices can connect with these applications to provide users with real-time information.

[0404] This system will improve fuel efficiency and personalize vehicle management, providing appropriate advice based on the user's emotional state.

[0405] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0406] Step 1:

[0407] The user inputs vehicle information and driving data into a smartphone, tablet, or head-mounted display. The input data includes the vehicle type, mileage, fuel economy, frequency of sudden acceleration and sudden stops, etc. This data is then sent from the device to a server.

[0408] Input: Vehicle information, driving data

[0409] Output: Send data to the server

[0410] Step 2:

[0411] The server stores the received vehicle information and driving data in a database. When storing the data, it validates and checks the format, and generates an appropriate error message if there is an error.

[0412] Input: Received vehicle information, driving data

[0413] Output: Vehicle information and driving data stored in the database

[0414] Step 3:

[0415] The server analyzes the stored driving data and generates fuel economy improvement methods using a generative AI model that detects specific patterns and trends and performs data calculations to identify the best improvement methods for the user.

[0416] Input: Driving data

[0417] Output: How to improve fuel economy

[0418] Step 4:

[0419] The generated fuel efficiency improvement methods are then sent to the user's device. The notification content is adjusted according to the user's emotional state detected by an emotion recognition engine. For example, if the user is under high stress, the advice will be softened.

[0420] Input: fuel efficiency improvement methods, emotional information

[0421] Output: Adjusted advice notification

[0422] Step 5:

[0423] The user inputs vehicle inspection and maintenance schedules into the terminal, which are then sent to the server and stored in a database.

[0424] Input: Vehicle inspection, maintenance schedule

[0425] Output: Sending schedule data to the server

[0426] Step 6:

[0427] The server generates reminders at appropriate times and sends them to the user's device. The timing and content of the notifications are adjusted taking into account the user's emotional state.

[0428] Input: Schedule data, emotional information

[0429] Output: Reminder notification

[0430] Step 7:

[0431] The server collects pricing and promotional information from local fuel stations, stores it in a database, and notifies users at optimal times based on their fueling patterns and location.

[0432] Input: fuel price information, campaign information

[0433] Output: Information notification at the optimal time

[0434] Step 8:

[0435] The user's device uses an emotion recognition engine to analyze the user's facial expressions and behavior in real time, and sends the resulting emotional data to the server, which then adjusts the content of notifications and fuel efficiency improvement advice based on this data.

[0436] Input: User emotion data

[0437] Output: Adjusted notification content

[0438] This system allows users to improve fuel efficiency and manage their vehicle in a personalized way, and provides appropriate advice and notifications based on their emotional state.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] [Second embodiment]

[0443] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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).

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] 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."

[0455] The present invention relates to a system that allows users to input vehicle model information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[0456] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[0457] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[0458] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[0459] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[0460] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[0461] Specific examples

[0462] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user of a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0463] Example 2: User B has registered his / her refueling habits and is checking local gas station information in the app. The server uses the collected price and campaign information to notify him / her that there is a discount at a particular gas station on the weekend. User B can take advantage of this to refuel economically.

[0464] Example 3: User C enters the schedule for vehicle inspection dates into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him to smoothly prepare for the inspection.

[0465] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle model information and driving data.

[0466] The processing flow will be explained below.

[0467] Step 1:

[0468] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[0469] Step 2:

[0470] The terminal transmits the input information to the server.

[0471] Step 3:

[0472] The server stores the received vehicle information and driving data in a database.

[0473] Step 4:

[0474] The server retrieves the user's driving data from the database.

[0475] Step 5:

[0476] The server uses generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, it analyzes the frequency of sudden acceleration and provides advice on how to reduce it.

[0477] Step 6:

[0478] The server notifies the user of the generated fuel efficiency improvement method.

[0479] Step 7:

[0480] The device receives the notification and displays it to the user, for example, "Reducing sudden acceleration will improve fuel economy by 5%."

[0481] Step 8:

[0482] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[0483] Step 9:

[0484] The server notifies the user of the comparison report.

[0485] Step 10:

[0486] The device receives the notification and displays it to the user in a ranking format.

[0487] Step 11:

[0488] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[0489] Step 12:

[0490] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[0491] Step 13:

[0492] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[0493] Step 14:

[0494] The device receives the notification and displays it to the user.

[0495] Step 15:

[0496] Users enter vehicle inspection and maintenance schedules into the application.

[0497] Step 16:

[0498] The terminal transmits schedule information to the server.

[0499] Step 17:

[0500] The server stores the received schedule information in a database.

[0501] Step 18:

[0502] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[0503] Step 19:

[0504] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[0505] Step 20:

[0506] The device receives the reminder notification and displays it to the user.

[0507] Example 1

[0508] 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."

[0509] Conventional vehicle management systems have difficulty providing personalized advice on improving fuel efficiency based on users' driving data, and they are unable to provide a wide range of functions in a unified manner, such as comparing fuel efficiency with other users, notifying them of fuel price information, or managing vehicle inspection and maintenance schedules. This has meant that users have to use different applications and systems individually, which has often been inconvenient.

[0510] 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.

[0511] In this invention, the server includes a user input means for the user to input vehicle information and driving data and send it to the server, a means for the server to store the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method using a generative AI model, a means for notifying the user of the generated fuel economy improvement method, a means for the server to acquire fuel economy data of similar users and generate a comparison report, a means for the server to collect price information and campaign information from fuel supply facilities and generate and notify optimal information based on the user's refueling patterns and location information, and a means for the user to input vehicle inspection dates and maintenance schedules, the server to store this, and generate and notify reminders at appropriate times. This allows users to comprehensively improve fuel economy and manage their vehicles using a single system.

[0512] "User" means any individual or legal entity that owns a vehicle and uses the system.

[0513] "Vehicle information" refers to basic information such as the car's manufacturer, model, and year.

[0514] "Driving data" refers to information about a user's driving, such as daily mileage, frequency of sudden acceleration and braking, and average speed.

[0515] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[0516] "Database" refers to a collection of information in which driving data and vehicle information are stored by a server.

[0517] "Generative AI model" refers to the artificial intelligence model used by the server to analyze driving data and generate fuel efficiency improvement advice appropriate for each individual user.

[0518] "Methods for improving fuel efficiency" refers to advice on specific driving methods and actions to improve fuel efficiency.

[0519] "Notification means" refers to the means for sending messages or alerts from the server to the user's device.

[0520] "Comparison Report" refers to a report summarizing the results of comparing fuel economy data with other users.

[0521] "Fuel distribution facility" means a gas station or other facility that provides fuel.

[0522] "Price Information" refers to information regarding the price of fuel offered at a fuel supply facility.

[0523] "Campaign Information" refers to information regarding discounts and special offers offered at fuel supply facilities.

[0524] "Reminder" refers to a message or alert that is sent to the user based on a pre-set schedule.

[0525] "Inspection Date" means the date on which a vehicle is to be inspected.

[0526] "Maintenance Schedule" means a date scheduled for performing maintenance on a Vehicle.

[0527] The present invention relates to a system that allows users to input vehicle information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[0528] Hardware and software used

[0529] The present invention is implemented mainly using the following hardware and software.

[0530] Hardware: smartphones, tablets, servers, internet connections

[0531] Software: applications (installed on smartphones or tablets), database management systems, generative AI models (e.g., OpenAI's GPT-4)

[0532] Data processing and calculation flow

[0533] Users use a smartphone or tablet to enter vehicle information and driving data into the application, including distance traveled, frequency of sudden acceleration and braking, average speed, etc. The entered information is sent by the device to a server via an internet connection.

[0534] The server stores the received data in a database. This data is efficiently managed using a database management system. The server analyzes the driving data stored in the database and uses a generative AI model to generate fuel efficiency improvement advice tailored to each individual user. For example, the server analyzes the user's driving patterns and provides specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0535] The generated advice is sent from the server to the user's device, where it can be viewed on the application. The server also retrieves fuel economy data from a database of similar users and compares it with the user's fuel economy. The comparison results are displayed in a ranking format and notified to the user.

[0536] The server also collects price and campaign information from fuel supply facilities via the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a discount campaign is being held at a nearby gas station, the server will notify the user of this information.

[0537] Users can also input vehicle inspection dates and maintenance schedules into the application. The server stores the input schedule in a database and generates reminders at appropriate times to notify the user's device. For example, a notification such as "The next vehicle inspection date is November 15, 2023" may be sent.

[0538] Specific examples

[0539] Example 1:

[0540] A scenario in which a user enters daily driving data into an app and receives advice on how to improve fuel efficiency.

[0541] Prompt: "Generate fuel economy recommendations based on the user's driving data."

[0542] Example 2:

[0543] A scenario in which refueling patterns are registered and local gas station information is utilized.

[0544] Prompt: "Regularly collect pricing information and promotions at nearby gas stations."

[0545] Example 3:

[0546] Scenario for entering vehicle inspection date and receiving reminder notifications.

[0547] Prompt: "The next inspection date is November 15, 2023."

[0548] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle information and driving data.

[0549] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0550] Step 1:

[0551] Using the device, users input vehicle information and driving data into the application, such as "Toyota Prius 2020 model," "Today's mileage: 50km, 5 sudden accelerations, 3 sudden brakings," and so on.

[0552] Input: Vehicle information, driving data

[0553] Output: Data sent from the device to the server

[0554] Step 2:

[0555] The device sends the entered vehicle information and driving data to a server via an internet connection. The moment the "Send" button is pressed, the information is sent to the server, and the server returns a confirmation message saying "Data saved successfully."

[0556] Input: Data entered into the terminal

[0557] Output: Data sent to the server

[0558] Step 3:

[0559] The server stores the received vehicle information and driving data in a database, which is efficiently managed using a database management system.

[0560] For example, the driving data of user A is saved as "distance traveled 50km, sudden acceleration 5 times, sudden braking 3 times."

[0561] Input: Data sent from the terminal

[0562] Output: Data stored in the database

[0563] Step 4:

[0564] The server analyzes the driving data stored in the database and generates fuel efficiency improvement advice using a generative AI model (e.g., OpenAI's GPT-4). For example, the generative AI model is used to generate specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0565] Input: Driving data stored in the database

[0566] Output: Fuel economy improvement advice

[0567] Step 5:

[0568] The server then sends the generated fuel efficiency improvement advice to the user's device, and the user confirms the specific advice on the application, such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0569] Input: Generated fuel economy improvement advice

[0570] Output: Advice sent to the user's device

[0571] Step 6:

[0572] The server retrieves fuel economy data from the database from other users of the same type, compares it with the user's fuel economy data, and displays it in a ranking format. For example, it generates a comparison report that notifies the user that "your fuel economy is in the top 20% overall."

[0573] Input: Fuel economy data of other users retrieved from the database

[0574] Output: Ranking comparison report

[0575] Step 7:

[0576] The server collects price information and campaign information from fuel supply facilities via the Internet and stores it in a database. For example, it collects information about a 10% discount campaign that is being held at a nearby gas station on the weekend.

[0577] Input: Fuel supply facility price information, campaign information

[0578] Output: Price information and campaign information stored in the database

[0579] Step 8:

[0580] The server analyzes the user's fueling patterns and location information and notifies the user of price and campaign information at the optimal time, for example, notifying the user that a discount campaign is being held at a nearby gas station.

[0581] Input: User's fueling patterns, location information

[0582] Output: Price information and campaign information notified to the user

[0583] Step 9:

[0584] Users input their vehicle inspection and maintenance dates into the application. For example, they might enter "next vehicle inspection date is November 15, 2023."

[0585] Input: Inspection date, maintenance schedule

[0586] Output: Schedule data sent to the server

[0587] Step 10:

[0588] The server stores the entered vehicle inspection dates and maintenance schedules in a database and generates reminders to notify the user at appropriate times, for example, "The next vehicle inspection date is November 15, 2023."

[0589] Input: Vehicle inspection dates and maintenance schedules stored in the database

[0590] Output: Reminder to be sent to the user

[0591] (Application example 1)

[0592] 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."

[0593] Conventional fuel economy improvement systems are limited to providing advice based on user behavior data, and do not adequately integrate or personalize information. Furthermore, fuel supply and maintenance information is not managed centrally, which often increases complexity for users.

[0594] 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.

[0595] In this invention, the server includes means for the user to input vehicle model information and driving data and transmit them to the server, means for the server to store the received vehicle model information and driving data in a database, means for the server to analyze the driving data and generate fuel economy improvement methods, means for notifying the user of the generated fuel economy improvement methods, means for the server to collect local fuel price information and campaign information based on location information and generate optimal information to support the user in economical refueling, and means for managing vehicle inspection and maintenance information and generating reminders at appropriate times. This enables fuel economy improvement and information management to meet the diverse needs of users.

[0596] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[0597] The "means for storing in a database" is a means for storing the vehicle model information and driving data received by the server in a database.

[0598] The "analysis means" is a means for analyzing the driving data received by the server and generating a method for improving fuel economy.

[0599] The "notification means" is a means for notifying the user of the generated fuel economy improvement method.

[0600] The "location information collection means" is a means by which the server collects local fuel price information and campaign information based on location information.

[0601] The "generation means" is a means for generating optimal information and reminders to assist the user in economical refueling based on the information collected by the server.

[0602] The "vehicle inspection and maintenance management means" is a means by which the server manages vehicle inspection and maintenance information and generates reminders at appropriate times.

[0603] This invention is a system in which users input vehicle model information and driving data, and based on that, a server provides personalized fuel efficiency improvement advice and centrally manages fuel price information, campaign information, and vehicle inspection and maintenance notifications. Users input vehicle model information and driving data into an application via a device such as a smartphone or tablet. The device then sends this information to the server, which then stores it in a database.

[0604] The server uses a generative AI model based on the saved driving data to generate personalized fuel economy improvement methods. For example, based on the user's driving data, it can provide specific advice such as "reducing sudden acceleration will improve fuel economy by 5%." This advice is notified to the user and can be viewed on the device.

[0605] The server also has a function to obtain the actual fuel consumption data of other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[0606] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores this data in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[0607] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications such as "The next vehicle inspection date is November 15, 2023," allowing them to prepare in advance.

[0608] For example, if a user inputs information such as "Vehicle information: general passenger car, Driving data: frequent sudden acceleration, Location: metropolitan area," the server will use the AI ​​to generate fuel efficiency improvement advice based on this information, providing specific advice such as "reducing sudden acceleration will improve fuel efficiency by 10%."

[0609] An example of a prompt sentence is, "Please generate advice to improve fuel efficiency based on the user information. The vehicle model information is 'general passenger car' and the driving data is 'frequent sudden acceleration'." In this way, the server provides the user with optimal advice and realizes multifaceted information management.

[0610] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0611] Step 1:

[0612] The user inputs vehicle model information and driving data, which are then sent to the server via the device. The input data includes vehicle model information (e.g., a typical passenger car) and driving data (e.g., frequent sudden acceleration). The device formats this information and sends it to the server as an HTTP request.

[0613] Input: Vehicle information, driving data

[0614] Output: Data sent to the server

[0615] Step 2:

[0616] The server analyzes the received vehicle information and driving data and saves it in a database. The server checks the integrity of the data and stores it in the database in an appropriate format. For example, the received data is saved in the database in JSON format.

[0617] Input: Received data

[0618] Output: Data stored in the database

[0619] Step 3:

[0620] The server uses the stored driving data to generate personalized fuel economy improvement methods using a generative AI model. Features of the driving data are extracted and input into the AI ​​model to generate specific advice (e.g., reducing sudden acceleration will improve fuel economy by 5%).

[0621] Input: Saved driving data

[0622] Output: Generated fuel economy improvement advice

[0623] Step 4:

[0624] The server notifies the user of the fuel efficiency improvement method that was generated. The server generates notification data and sends it to the user's device. It is displayed on the device as a pop-up notification or message notification.

[0625] Input: Generated fuel economy improvement advice

[0626] Output: Notification to the user's device

[0627] Step 5:

[0628] The server retrieves the actual fuel consumption data of other users of the same vehicle model and generates a comparison report. The server queries the relevant data from the database, extracts statistical information, and creates a report that displays the fuel consumption rankings.

[0629] Input: Actual fuel consumption data of other users of the same vehicle model

[0630] Output: Generated comparison report

[0631] Step 6:

[0632] The server collects price and campaign information from local fuel supply facilities, and uses an API via the Internet to retrieve the latest price and campaign information and store it in a database.

[0633] Input: Fuel price information and campaign information collected from the Internet

[0634] Output: Price information and campaign information stored in the database

[0635] Step 7:

[0636] The server generates and notifies the user of the most economical options based on their refueling patterns and location. It analyzes location information and patterns and notifies the user of the most economical options (e.g., discount information for nearby gas stations).

[0637] Input: User's fueling patterns, location information

[0638] Output: Notifying the user of the generated information

[0639] Step 8:

[0640] The server manages vehicle inspection and maintenance information and generates reminders at appropriate times based on the schedule information entered by the user, and notifies the user's device.

[0641] Input: Inspection and maintenance schedules entered by the user

[0642] Output: Generated reminder notification

[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] This invention combines an emotion engine with a system that provides personalized advice on improving fuel economy based on user input of vehicle model information and driving data, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications. The emotion engine recognizes the user's emotions and further utilizes this data to provide more appropriate advice and reminders.

[0645] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[0646] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[0647] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[0648] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[0649] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[0650] The emotion engine recognizes the user's emotions in real time, detecting their emotional state while driving and their stress level. This allows the generated fuel efficiency improvement advice and other notifications to be adjusted based on the user's emotional state. For example, if the user is stressed, the advice can be softened, while if the user is relaxed, the advice can be detailed and specific.

[0651] Specific examples

[0652] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is feeling stressed, the advice is softened.

[0653] Example 2: User B has registered his / her refueling patterns and is checking local gas station information in the app. Based on the collected price and campaign information, the server notifies him / her that there is a discount at a specific gas station on the weekend. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it will provide him / her with detailed campaign information.

[0654] Example 3: User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[0655] As described above, this system not only achieves multifaceted improvements in fuel efficiency and an economical driving lifestyle based on the user's vehicle information, driving data, and emotional state, but also provides a more personalized experience through emotion recognition.

[0656] The processing flow will be explained below.

[0657] Step 1:

[0658] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[0659] Step 2:

[0660] The terminal transmits the input information to the server.

[0661] Step 3:

[0662] The server stores the received vehicle information and driving data in a database.

[0663] Step 4:

[0664] The server retrieves the user's driving data from the database.

[0665] Step 5:

[0666] The server uses the generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, if the user frequently accelerates suddenly, it generates advice on reducing sudden acceleration.

[0667] Step 6:

[0668] The server notifies the user how to improve fuel economy, for example, "reducing sudden acceleration will improve fuel economy by 5%."

[0669] Step 7:

[0670] The device receives the notification and displays it to the user.

[0671] Step 8:

[0672] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[0673] Step 9:

[0674] The server notifies the user with a comparison report, for example, "Your fuel economy is 90% of the average of other users."

[0675] Step 10:

[0676] The device receives the notification and displays it to the user in a ranking format.

[0677] Step 11:

[0678] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[0679] Step 12:

[0680] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[0681] Step 13:

[0682] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[0683] Step 14:

[0684] The device receives the notification and displays it to the user.

[0685] Step 15:

[0686] Users enter vehicle inspection and maintenance schedules into the application.

[0687] Step 16:

[0688] The terminal transmits schedule information to the server.

[0689] Step 17:

[0690] The server stores the received schedule information in a database.

[0691] Step 18:

[0692] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[0693] Step 19:

[0694] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[0695] Step 20:

[0696] The device receives the reminder notification and displays it to the user.

[0697] Step 21:

[0698] The emotion engine recognizes the user's emotional state in real time and collects emotional data while driving.

[0699] Step 22:

[0700] The server analyzes the user's emotional state based on emotional data and determines their stress level and relaxation state while driving.

[0701] Step 23:

[0702] The server adjusts the generated fuel efficiency improvement methods and notification content based on the user's emotional state. For example, if the user is feeling stressed, the advice will be softened.

[0703] Step 24:

[0704] The server sends tailored advice and notifications to the user.

[0705] Step 25:

[0706] The device receives the notification and displays it to the user, for example, displaying a message such as "Relax and keep driving."

[0707] Example 2

[0708] 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."

[0709] When receiving advice on fuel economy and maintenance, the advice is often uniform and does not take into account the individual user's driving situation or emotional state, making it difficult to provide effective advice.Furthermore, fuel price information and campaign information are not provided at a time that meets the needs of individual users, making it difficult to support users' economical car lifestyles.

[0710] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means by which the user inputs vehicle model information and driving data and transmits them to the server, a means by which the server stores the received vehicle model information and driving data in a database, a generation means by the server analyzing the driving data and generating a fuel economy improvement method, a generation means by the server generating personalized fuel economy improvement advice using a generation AI model, and a means by which the server notifies the user of the generated fuel economy improvement method. This makes it possible to provide more effective and timely advice and information according to the user's individual driving situation and emotional state.

[0711] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[0712] A "server" is a central computing device that receives, stores, and processes data sent by users.

[0713] "Database" means a structured data storage system for storing received vehicle type information and driving data.

[0714] The "generation means" is a means for analyzing the data acquired by the server and generating methods for improving fuel efficiency and other information.

[0715] A "generative AI model" is an artificial intelligence model that generates personalized advice and reports based on data.

[0716] "Personalized fuel economy improvement advice" is specific suggestions for improving fuel economy generated based on an individual user's driving data.

[0717] "Notification means" refers to a means for notifying the user of the generated information or advice.

[0718] The "Emotion Engine" is a function that recognizes the user's emotional state in real time and adjusts advice and notifications based on that information.

[0719] This invention is a system that provides personalized advice on improving fuel economy based on user input of vehicle information and driving data. The system centrally manages notifications of fuel price information and campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications, and uses an emotion engine to provide appropriate advice and reminders based on the user's emotional state.

[0720] First, the user uses an application on their smartphone or tablet to input vehicle information and driving data, such as the vehicle's make and model, annual mileage, average speed, etc. The device then sends the input information to the server.

[0721] The server stores the received vehicle information and driving data in a database. Database management systems such as MySQL and PostgreSQL can be used. The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4). The generative AI model generates advice for individual users on how to improve fuel efficiency based on the driving data. An example of a specific prompt is, "Please generate advice for improving fuel efficiency based on the user's driving data."

[0722] The generated advice is sent from the server to the device. Firebase Cloud Messaging can be used for this notification. For example, a notification saying "Reducing sudden acceleration will improve fuel economy by 5%" may be sent to the device. The server also has a function to obtain actual fuel economy data from other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel economy compares to other users. The comparison of actual fuel economy data is displayed in a ranking format to increase user motivation.

[0723] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[0724] Users also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. For example, a notification saying "The next vehicle inspection date is November 15, 2023" is sent to the user and displayed on the device.

[0725] The emotion engine recognizes the user's emotional state in real time. The device detects the user's emotional state using the emotion engine and sends that data to the server. The server then adjusts the generated fuel efficiency improvement methods and other notifications based on this emotional data. For example, if the user is feeling stressed, the advice can be expressed in a gentler tone. On the other hand, if the user is relaxed, specific improvement methods can be provided in detail. An example of a specific prompt is, "Please generate advice appropriate for when the user is feeling stressed."

[0726] Example 1:

[0727] User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses the generative AI model to provide personalized fuel efficiency improvement advice, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is stressed, the advice is softened.

[0728] Example prompts for generative AI models:

[0729] "Please change the advice that reducing hard acceleration will improve fuel economy by 5% to something more appropriate for when the user is stressed."

[0730] Example 2:

[0731] User B registers his / her refueling patterns and checks local gas station information on the app. Based on the collected price and campaign information, the server notifies him / her that there are discounts at specific gas stations on weekends. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it provides detailed campaign information.

[0732] Example prompts for generative AI models:

[0733] "Change discount information for nearby gas stations to details that users would like to see when they are relaxed."

[0734] Example 3:

[0735] User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[0736] Example prompts for generative AI models:

[0737] "When informing users that their next vehicle inspection date is November 15, 2023, please change the wording to something more appropriate for users with high stress levels."

[0738] In this way, this system not only provides personalized advice on improving fuel economy based on the user's vehicle information and driving data, but also utilizes an emotion engine to provide a more individualized experience based on the user's emotional state. As a whole, the system aims to support users' economical car life and improve driving efficiency.

[0739] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0740] Program processing flow

[0741] Step 1: User data entry

[0742] Users input their vehicle information and driving data into a device (smartphone or tablet), including vehicle model, manufacturer, annual mileage, average speed, etc.

[0743] Input: Vehicle information, driving data

[0744] Output: User input data

[0745] Specific action: Enter data using a form within the app and press the "Submit" button.

[0746] Step 2: Sending data to the server

[0747] The device sends the vehicle information and driving data entered by the user to the server using a secure communication protocol such as HTTPS.

[0748] Input: User-entered data

[0749] Output: Data sent to the server

[0750] Specific operation: Using the terminal's sending function, the input data is encrypted and sent to the server.

[0751] Step 3: Saving to the database

[0752] The server analyzes the received vehicle information and driving data and stores it in a database, which may use a database management system such as MySQL or PostgreSQL.

[0753] Input: Data sent to the server

[0754] Output: Status of completion of saving to database

[0755] Specific behavior: Establishes a database connection and inserts the received data into the appropriate tables.

[0756] Step 4: Analyze driving data and generate fuel economy improvement advice

[0757] The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4) to generate personalized fuel economy improvement advice.

[0758] Input: Driving data stored in the database

[0759] Output: Personalized advice

[0760] Specific operation: Driving data is obtained, and the prompt "Please generate advice for improving fuel efficiency based on the user's driving data" is input into the generative AI model to obtain advice.

[0761] Step 5: Advice Notification

[0762] The server then sends the generated fuel efficiency improvement advice to the user's device using services such as Firebase Cloud Messaging.

[0763] Input: Personalized advice

[0764] Output: Notification completion status to the terminal

[0765] Specific operation: The generated advice is sent to the device via Firebase Cloud Messaging.

[0766] Step 6: Capture actual fuel consumption data and generate comparison reports

[0767] The server obtains the actual fuel consumption data of other users of the same vehicle model and generates a comparison report.

[0768] Input: Actual fuel consumption data of other users obtained from the database

[0769] Output: Comparison report

[0770] Specific operation: Executes a database query to obtain fuel economy data from other users of the same vehicle model and creates a comparison report.

[0771] Step 7: Gather fuel price and promotion information

[0772] The server collects price and promotion information from local fuel supply facilities from the Internet and stores it in a database.

[0773] Input: Pricing and promotion information gathered from the web

[0774] Output: Status of completion of saving to database

[0775] Specific actions: Use APIs and web scraping to obtain information and store it in a database.

[0776] Step 8: Provide optimal information at the right time

[0777] The server notifies the user of the collected information at the optimal time based on the user's refueling patterns and location information.

[0778] Input: Refueling patterns, location information, price information, and campaign information stored in the database

[0779] Output: User notification

[0780] Specific operation: Analyzes the user's refueling patterns and location information and notifies them at the optimal time.

[0781] Step 9: Enter your vehicle inspection and maintenance schedule

[0782] Users enter vehicle inspection and maintenance schedules through the app.

[0783] Input: Vehicle inspection and maintenance schedule

[0784] Output: Schedule data sent to the server

[0785] Specific actions: Enter your schedule in the app and press the "Send" button.

[0786] Step 10: Generate and notify reminders

[0787] The server generates and notifies reminders at appropriate times based on the input schedule.

[0788] Input: Schedules stored in the database

[0789] Output: Reminder notification

[0790] Specific behavior: Set a timer based on your schedule and generate reminders to notify you.

[0791] Step 11: Emotion recognition and advice adjustment

[0792] The emotion engine recognizes the user's emotional state in real time and sends that data to the server, which analyzes the emotional data and adjusts the advice and notifications it generates.

[0793] Input: User emotion data

[0794] Output: Tailored advice based on emotions

[0795] How it works: The emotion engine recognizes emotions in real time and sends that data to the server, which then adjusts the tone and content of the advice based on the emotion data.

[0796] (Application example 2)

[0797] 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."

[0798] Improving automobile fuel efficiency and supporting efficient driving are important challenges, but existing systems are unable to provide personalized advice based on the user's driving data, or centrally manage fuel price information, vehicle inspection and maintenance schedules. Furthermore, they do not provide advice that takes into account the user's emotional state. This means that users cannot receive the information they need at the right time, making efficient vehicle management difficult.

[0799] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user input means through which the user inputs vehicle information and driving data and transmits them to the server, a means for storing the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method, a means for notifying the user of the generated fuel economy improvement method, a means for detecting the user's emotional state in real time using an emotion recognition engine and adjusting advice content using the information, a means for managing vehicle inspection and maintenance schedules and generating reminders at appropriate times, and a means for collecting price information and campaign information from local fuel supply facilities and notifying the user based on the user's refueling pattern and location information. This personalizes fuel economy improvement and vehicle management, making it possible to provide information at appropriate times according to the user's emotional state.

[0800] The "user input means" is a means by which a user inputs vehicle information and driving data and transmits them to the server.

[0801] The "means for storing in a database" refers to the means by which the server records and manages the vehicle information and driving data received from the user.

[0802] The "generation means" is a means by which the server analyzes the driving data and generates a fuel economy improvement method.

[0803] The "notification means" is a means for notifying the user of the generated fuel efficiency improvement method and other information.

[0804] The "emotion recognition engine" is an engine that detects the user's emotional state in real time and uses that information to adjust the content of advice.

[0805] "Means for generating reminders" refers to means for managing schedules for vehicle inspections and maintenance and notifying users at appropriate times.

[0806] The "means for collecting price information and campaign information from fuel supply facilities" refers to a means for collecting price information and campaign information from local fuel supply facilities and notifying the user of such information.

[0807] "Head-mounted displays and mobile devices" are devices that users can wear to check driving data and notifications in real time.

[0808] This invention is a system that uses the user's vehicle information and driving data to provide personalized fuel efficiency improvement advice, vehicle inspection and maintenance reminders, local fuel supply facility information, etc. Furthermore, it is possible to grasp the user's emotional state in real time using an emotion recognition engine and adjust the content of notifications accordingly.

[0809] First, the user inputs vehicle information and driving data using a smartphone, tablet, or head-mounted display. This device is equipped with a user input means that transmits the input information to a server. The server stores the received information in a database and performs the necessary data management.

[0810] The server then analyzes the saved driving data and uses a generative AI model to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or stops suddenly, the server generates specific advice on how to improve fuel efficiency by reducing these actions.

[0811] The generated fuel efficiency improvement methods are sent to the user's device. The content of the notification is adjusted according to the user's emotional state using an emotion recognition engine. For example, if the user is stressed, the advice is given in a gentle manner, while if the user is relaxed, the system provides detailed and precise improvement methods.

[0812] For vehicle inspections and maintenance, users input their schedules into their devices. The server stores this in a database and generates reminders at the appropriate times. This allows users to receive appropriate notifications when inspection or maintenance dates are approaching, allowing them to prepare in advance.

[0813] The server also collects price and promotion information from local fuel supply facilities and notifies users at the optimal time based on their fueling patterns and location, allowing them to refuel economically.

[0814] For illustrative purposes, the following prompt sentences can be used:

[0815] "Reducing sudden acceleration will improve fuel economy. Please provide specific advice based on driving data. If the user is relaxed, please provide detailed instructions on how to improve, but if the user is stressed, please use gentler language."

[0816] This generative AI model is implemented using Python, TensorFlow, and OpenCV to perform emotion recognition and data analysis. The backend uses Flask and Django, and data is managed using a real-time database such as Firebase. Head-mounted displays and mobile devices can connect with these applications to provide users with real-time information.

[0817] This system will improve fuel efficiency and personalize vehicle management, providing appropriate advice based on the user's emotional state.

[0818] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0819] Step 1:

[0820] The user inputs vehicle information and driving data into a smartphone, tablet, or head-mounted display. The input data includes the vehicle type, mileage, fuel economy, frequency of sudden acceleration and sudden stops, etc. This data is then sent from the device to a server.

[0821] Input: Vehicle information, driving data

[0822] Output: Send data to the server

[0823] Step 2:

[0824] The server stores the received vehicle information and driving data in a database. When storing the data, it validates and checks the format, and generates an appropriate error message if there is an error.

[0825] Input: Received vehicle information, driving data

[0826] Output: Vehicle information and driving data stored in the database

[0827] Step 3:

[0828] The server analyzes the stored driving data and generates fuel economy improvement methods using a generative AI model that detects specific patterns and trends and performs data calculations to identify the best improvement methods for the user.

[0829] Input: Driving data

[0830] Output: How to improve fuel economy

[0831] Step 4:

[0832] The generated fuel efficiency improvement methods are then sent to the user's device. The notification content is adjusted according to the user's emotional state detected by an emotion recognition engine. For example, if the user is under high stress, the advice will be softened.

[0833] Input: fuel efficiency improvement methods, emotional information

[0834] Output: Adjusted advice notification

[0835] Step 5:

[0836] The user inputs vehicle inspection and maintenance schedules into the terminal, which are then sent to the server and stored in a database.

[0837] Input: Vehicle inspection, maintenance schedule

[0838] Output: Sending schedule data to the server

[0839] Step 6:

[0840] The server generates reminders at appropriate times and sends them to the user's device. The timing and content of the notifications are adjusted taking into account the user's emotional state.

[0841] Input: Schedule data, emotional information

[0842] Output: Reminder notification

[0843] Step 7:

[0844] The server collects pricing and promotional information from local fuel stations, stores it in a database, and notifies users at optimal times based on their fueling patterns and location.

[0845] Input: fuel price information, campaign information

[0846] Output: Information notification at the optimal time

[0847] Step 8:

[0848] The user's device uses an emotion recognition engine to analyze the user's facial expressions and behavior in real time, and sends the resulting emotional data to the server, which then adjusts the content of notifications and fuel efficiency improvement advice based on this data.

[0849] Input: User emotion data

[0850] Output: Adjusted notification content

[0851] This system allows users to improve fuel efficiency and manage their vehicle in a personalized way, and provides appropriate advice and notifications based on their emotional state.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] [Third embodiment]

[0856] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0857] 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.

[0858] 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).

[0859] 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.

[0860] 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.

[0861] 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).

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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."

[0868] The present invention relates to a system that allows users to input vehicle model information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[0869] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[0870] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[0871] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[0872] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[0873] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[0874] Specific examples

[0875] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user of a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0876] Example 2: User B has registered his / her refueling habits and is checking local gas station information in the app. The server uses the collected price and campaign information to notify him / her that there is a discount at a particular gas station on the weekend. User B can take advantage of this to refuel economically.

[0877] Example 3: User C enters the schedule for vehicle inspection dates into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him to smoothly prepare for the inspection.

[0878] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle model information and driving data.

[0879] The processing flow will be explained below.

[0880] Step 1:

[0881] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[0882] Step 2:

[0883] The terminal transmits the input information to the server.

[0884] Step 3:

[0885] The server stores the received vehicle information and driving data in a database.

[0886] Step 4:

[0887] The server retrieves the user's driving data from the database.

[0888] Step 5:

[0889] The server uses generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, it analyzes the frequency of sudden acceleration and provides advice on how to reduce it.

[0890] Step 6:

[0891] The server notifies the user of the generated fuel efficiency improvement method.

[0892] Step 7:

[0893] The device receives the notification and displays it to the user, for example, "Reducing sudden acceleration will improve fuel economy by 5%."

[0894] Step 8:

[0895] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[0896] Step 9:

[0897] The server notifies the user of the comparison report.

[0898] Step 10:

[0899] The device receives the notification and displays it to the user in a ranking format.

[0900] Step 11:

[0901] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[0902] Step 12:

[0903] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[0904] Step 13:

[0905] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[0906] Step 14:

[0907] The device receives the notification and displays it to the user.

[0908] Step 15:

[0909] Users enter vehicle inspection and maintenance schedules into the application.

[0910] Step 16:

[0911] The terminal transmits schedule information to the server.

[0912] Step 17:

[0913] The server stores the received schedule information in a database.

[0914] Step 18:

[0915] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[0916] Step 19:

[0917] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[0918] Step 20:

[0919] The device receives the reminder notification and displays it to the user.

[0920] Example 1

[0921] 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."

[0922] Conventional vehicle management systems have difficulty providing personalized advice on improving fuel efficiency based on users' driving data, and they are unable to provide a wide range of functions in a unified manner, such as comparing fuel efficiency with other users, notifying them of fuel price information, or managing vehicle inspection and maintenance schedules. This has meant that users have to use different applications and systems individually, which has often been inconvenient.

[0923] 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.

[0924] In this invention, the server includes a user input means for the user to input vehicle information and driving data and send it to the server, a means for the server to store the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method using a generative AI model, a means for notifying the user of the generated fuel economy improvement method, a means for the server to acquire fuel economy data of similar users and generate a comparison report, a means for the server to collect price information and campaign information from fuel supply facilities and generate and notify optimal information based on the user's refueling patterns and location information, and a means for the user to input vehicle inspection dates and maintenance schedules, the server to store this, and generate and notify reminders at appropriate times. This allows users to comprehensively improve fuel economy and manage their vehicles using a single system.

[0925] "User" means any individual or legal entity that owns a vehicle and uses the system.

[0926] "Vehicle information" refers to basic information such as the car's manufacturer, model, and year.

[0927] "Driving data" refers to information about a user's driving, such as daily mileage, frequency of sudden acceleration and braking, and average speed.

[0928] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[0929] "Database" refers to a collection of information in which driving data and vehicle information are stored by a server.

[0930] "Generative AI model" refers to the artificial intelligence model used by the server to analyze driving data and generate fuel efficiency improvement advice appropriate for each individual user.

[0931] "Methods for improving fuel efficiency" refers to advice on specific driving methods and actions to improve fuel efficiency.

[0932] "Notification means" refers to the means for sending messages or alerts from the server to the user's device.

[0933] "Comparison Report" refers to a report summarizing the results of comparing fuel economy data with other users.

[0934] "Fuel distribution facility" means a gas station or other facility that provides fuel.

[0935] "Price Information" refers to information regarding the price of fuel offered at a fuel supply facility.

[0936] "Campaign Information" refers to information regarding discounts and special offers offered at fuel supply facilities.

[0937] "Reminder" refers to a message or alert that is sent to the user based on a pre-set schedule.

[0938] "Inspection Date" means the date on which a vehicle is to be inspected.

[0939] "Maintenance Schedule" means a date scheduled for performing maintenance on a Vehicle.

[0940] The present invention relates to a system that allows users to input vehicle information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[0941] Hardware and software used

[0942] The present invention is implemented mainly using the following hardware and software.

[0943] Hardware: smartphones, tablets, servers, internet connections

[0944] Software: applications (installed on smartphones or tablets), database management systems, generative AI models (e.g., OpenAI's GPT-4)

[0945] Data processing and calculation flow

[0946] Users use a smartphone or tablet to enter vehicle information and driving data into the application, including distance traveled, frequency of sudden acceleration and braking, average speed, etc. The entered information is sent by the device to a server via an internet connection.

[0947] The server stores the received data in a database. This data is efficiently managed using a database management system. The server analyzes the driving data stored in the database and uses a generative AI model to generate fuel efficiency improvement advice tailored to each individual user. For example, the server analyzes the user's driving patterns and provides specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0948] The generated advice is sent from the server to the user's device, where it can be viewed on the application. The server also retrieves fuel economy data from a database of similar users and compares it with the user's fuel economy. The comparison results are displayed in a ranking format and notified to the user.

[0949] The server also collects price and campaign information from fuel supply facilities via the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a discount campaign is being held at a nearby gas station, the server will notify the user of this information.

[0950] Users can also input vehicle inspection dates and maintenance schedules into the application. The server stores the input schedule in a database and generates reminders at appropriate times to notify the user's device. For example, a notification such as "The next vehicle inspection date is November 15, 2023" may be sent.

[0951] Specific examples

[0952] Example 1:

[0953] A scenario in which a user enters daily driving data into an app and receives advice on how to improve fuel efficiency.

[0954] Prompt: "Generate fuel economy recommendations based on the user's driving data."

[0955] Example 2:

[0956] A scenario in which refueling patterns are registered and local gas station information is utilized.

[0957] Prompt: "Regularly collect pricing information and promotions at nearby gas stations."

[0958] Example 3:

[0959] Scenario for entering vehicle inspection date and receiving reminder notifications.

[0960] Prompt: "The next inspection date is November 15, 2023."

[0961] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle information and driving data.

[0962] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0963] Step 1:

[0964] Using the device, users input vehicle information and driving data into the application, such as "Toyota Prius 2020 model," "Today's mileage: 50km, 5 sudden accelerations, 3 sudden brakings," and so on.

[0965] Input: Vehicle information, driving data

[0966] Output: Data sent from the device to the server

[0967] Step 2:

[0968] The device sends the entered vehicle information and driving data to a server via an internet connection. The moment the "Send" button is pressed, the information is sent to the server, and the server returns a confirmation message saying "Data saved successfully."

[0969] Input: Data entered into the terminal

[0970] Output: Data sent to the server

[0971] Step 3:

[0972] The server stores the received vehicle information and driving data in a database, which is efficiently managed using a database management system.

[0973] For example, the driving data of user A is saved as "distance traveled 50km, sudden acceleration 5 times, sudden braking 3 times."

[0974] Input: Data sent from the terminal

[0975] Output: Data stored in the database

[0976] Step 4:

[0977] The server analyzes the driving data stored in the database and generates fuel efficiency improvement advice using a generative AI model (e.g., OpenAI's GPT-4). For example, the generative AI model is used to generate specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0978] Input: Driving data stored in the database

[0979] Output: Fuel economy improvement advice

[0980] Step 5:

[0981] The server then sends the generated fuel efficiency improvement advice to the user's device, and the user confirms the specific advice on the application, such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[0982] Input: Generated fuel economy improvement advice

[0983] Output: Advice sent to the user's device

[0984] Step 6:

[0985] The server retrieves fuel economy data from the database from other users of the same type, compares it with the user's fuel economy data, and displays it in a ranking format. For example, it generates a comparison report that notifies the user that "your fuel economy is in the top 20% overall."

[0986] Input: Fuel economy data of other users retrieved from the database

[0987] Output: Ranking comparison report

[0988] Step 7:

[0989] The server collects price information and campaign information from fuel supply facilities via the Internet and stores it in a database. For example, it collects information about a 10% discount campaign that is being held at a nearby gas station on the weekend.

[0990] Input: Fuel supply facility price information, campaign information

[0991] Output: Price information and campaign information stored in the database

[0992] Step 8:

[0993] The server analyzes the user's fueling patterns and location information and notifies the user of price and campaign information at the optimal time, for example, notifying the user that a discount campaign is being held at a nearby gas station.

[0994] Input: User's fueling patterns, location information

[0995] Output: Price information and campaign information notified to the user

[0996] Step 9:

[0997] Users input their vehicle inspection and maintenance dates into the application. For example, they might enter "next vehicle inspection date is November 15, 2023."

[0998] Input: Inspection date, maintenance schedule

[0999] Output: Schedule data sent to the server

[1000] Step 10:

[1001] The server stores the entered vehicle inspection dates and maintenance schedules in a database and generates reminders to notify the user at appropriate times, for example, "The next vehicle inspection date is November 15, 2023."

[1002] Input: Vehicle inspection dates and maintenance schedules stored in the database

[1003] Output: Reminder to be sent to the user

[1004] (Application example 1)

[1005] 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."

[1006] Conventional fuel economy improvement systems are limited to providing advice based on user behavior data, and do not adequately integrate or personalize information. Furthermore, fuel supply and maintenance information is not managed centrally, which often increases complexity for users.

[1007] 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.

[1008] In this invention, the server includes means for the user to input vehicle model information and driving data and transmit them to the server, means for the server to store the received vehicle model information and driving data in a database, means for the server to analyze the driving data and generate fuel economy improvement methods, means for notifying the user of the generated fuel economy improvement methods, means for the server to collect local fuel price information and campaign information based on location information and generate optimal information to support the user in economical refueling, and means for managing vehicle inspection and maintenance information and generating reminders at appropriate times. This enables fuel economy improvement and information management to meet the diverse needs of users.

[1009] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[1010] The "means for storing in a database" is a means for storing the vehicle model information and driving data received by the server in a database.

[1011] The "analysis means" is a means for analyzing the driving data received by the server and generating a method for improving fuel economy.

[1012] The "notification means" is a means for notifying the user of the generated fuel economy improvement method.

[1013] The "location information collection means" is a means by which the server collects local fuel price information and campaign information based on location information.

[1014] The "generation means" is a means for generating optimal information and reminders to assist the user in economical refueling based on the information collected by the server.

[1015] The "vehicle inspection and maintenance management means" is a means by which the server manages vehicle inspection and maintenance information and generates reminders at appropriate times.

[1016] This invention is a system in which users input vehicle model information and driving data, and based on that, a server provides personalized fuel efficiency improvement advice and centrally manages fuel price information, campaign information, and vehicle inspection and maintenance notifications. Users input vehicle model information and driving data into an application via a device such as a smartphone or tablet. The device then sends this information to the server, which then stores it in a database.

[1017] The server uses a generative AI model based on the saved driving data to generate personalized fuel economy improvement methods. For example, based on the user's driving data, it can provide specific advice such as "reducing sudden acceleration will improve fuel economy by 5%." This advice is notified to the user and can be viewed on the device.

[1018] The server also has a function to obtain the actual fuel consumption data of other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[1019] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores this data in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[1020] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications such as "The next vehicle inspection date is November 15, 2023," allowing them to prepare in advance.

[1021] For example, if a user inputs information such as "Vehicle information: general passenger car, Driving data: frequent sudden acceleration, Location: metropolitan area," the server will use the AI ​​to generate fuel efficiency improvement advice based on this information, providing specific advice such as "reducing sudden acceleration will improve fuel efficiency by 10%."

[1022] An example of a prompt sentence is, "Please generate advice to improve fuel efficiency based on the user information. The vehicle model information is 'general passenger car' and the driving data is 'frequent sudden acceleration'." In this way, the server provides the user with optimal advice and realizes multifaceted information management.

[1023] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1024] Step 1:

[1025] The user inputs vehicle model information and driving data, which are then sent to the server via the device. The input data includes vehicle model information (e.g., a typical passenger car) and driving data (e.g., frequent sudden acceleration). The device formats this information and sends it to the server as an HTTP request.

[1026] Input: Vehicle information, driving data

[1027] Output: Data sent to the server

[1028] Step 2:

[1029] The server analyzes the received vehicle information and driving data and saves it in a database. The server checks the integrity of the data and stores it in the database in an appropriate format. For example, the received data is saved in the database in JSON format.

[1030] Input: Received data

[1031] Output: Data stored in the database

[1032] Step 3:

[1033] The server uses the stored driving data to generate personalized fuel economy improvement methods using a generative AI model. Features of the driving data are extracted and input into the AI ​​model to generate specific advice (e.g., reducing sudden acceleration will improve fuel economy by 5%).

[1034] Input: Saved driving data

[1035] Output: Generated fuel economy improvement advice

[1036] Step 4:

[1037] The server notifies the user of the fuel efficiency improvement method that was generated. The server generates notification data and sends it to the user's device. It is displayed on the device as a pop-up notification or message notification.

[1038] Input: Generated fuel economy improvement advice

[1039] Output: Notification to the user's device

[1040] Step 5:

[1041] The server retrieves the actual fuel consumption data of other users of the same vehicle model and generates a comparison report. The server queries the relevant data from the database, extracts statistical information, and creates a report that displays the fuel consumption rankings.

[1042] Input: Actual fuel consumption data of other users of the same vehicle model

[1043] Output: Generated comparison report

[1044] Step 6:

[1045] The server collects price and campaign information from local fuel supply facilities, and uses an API via the Internet to retrieve the latest price and campaign information and store it in a database.

[1046] Input: Fuel price information and campaign information collected from the Internet

[1047] Output: Price information and campaign information stored in the database

[1048] Step 7:

[1049] The server generates and notifies the user of the most economical options based on their refueling patterns and location. It analyzes location information and patterns and notifies the user of the most economical options (e.g., discount information for nearby gas stations).

[1050] Input: User's fueling patterns, location information

[1051] Output: Notifying the user of the generated information

[1052] Step 8:

[1053] The server manages vehicle inspection and maintenance information and generates reminders at appropriate times based on the schedule information entered by the user, and notifies the user's device.

[1054] Input: Inspection and maintenance schedules entered by the user

[1055] Output: Generated reminder notification

[1056] 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.

[1057] This invention combines an emotion engine with a system that provides personalized advice on improving fuel economy based on user input of vehicle model information and driving data, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications. The emotion engine recognizes the user's emotions and further utilizes this data to provide more appropriate advice and reminders.

[1058] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[1059] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[1060] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[1061] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[1062] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[1063] The emotion engine recognizes the user's emotions in real time, detecting their emotional state while driving and their stress level. This allows the generated fuel efficiency improvement advice and other notifications to be adjusted based on the user's emotional state. For example, if the user is stressed, the advice can be softened, while if the user is relaxed, the advice can be detailed and specific.

[1064] Specific examples

[1065] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is feeling stressed, the advice is softened.

[1066] Example 2: User B has registered his / her refueling patterns and is checking local gas station information in the app. Based on the collected price and campaign information, the server notifies him / her that there is a discount at a specific gas station on the weekend. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it will provide him / her with detailed campaign information.

[1067] Example 3: User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[1068] As described above, this system not only achieves multifaceted improvements in fuel efficiency and an economical driving lifestyle based on the user's vehicle information, driving data, and emotional state, but also provides a more personalized experience through emotion recognition.

[1069] The processing flow will be explained below.

[1070] Step 1:

[1071] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[1072] Step 2:

[1073] The terminal transmits the input information to the server.

[1074] Step 3:

[1075] The server stores the received vehicle information and driving data in a database.

[1076] Step 4:

[1077] The server retrieves the user's driving data from the database.

[1078] Step 5:

[1079] The server uses the generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, if the user frequently accelerates suddenly, it generates advice on reducing sudden acceleration.

[1080] Step 6:

[1081] The server notifies the user how to improve fuel economy, for example, "reducing sudden acceleration will improve fuel economy by 5%."

[1082] Step 7:

[1083] The device receives the notification and displays it to the user.

[1084] Step 8:

[1085] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[1086] Step 9:

[1087] The server notifies the user with a comparison report, for example, "Your fuel economy is 90% of the average of other users."

[1088] Step 10:

[1089] The device receives the notification and displays it to the user in a ranking format.

[1090] Step 11:

[1091] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[1092] Step 12:

[1093] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[1094] Step 13:

[1095] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[1096] Step 14:

[1097] The device receives the notification and displays it to the user.

[1098] Step 15:

[1099] Users enter vehicle inspection and maintenance schedules into the application.

[1100] Step 16:

[1101] The terminal transmits schedule information to the server.

[1102] Step 17:

[1103] The server stores the received schedule information in a database.

[1104] Step 18:

[1105] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[1106] Step 19:

[1107] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[1108] Step 20:

[1109] The device receives the reminder notification and displays it to the user.

[1110] Step 21:

[1111] The emotion engine recognizes the user's emotional state in real time and collects emotional data while driving.

[1112] Step 22:

[1113] The server analyzes the user's emotional state based on emotional data and determines their stress level and relaxation state while driving.

[1114] Step 23:

[1115] The server adjusts the generated fuel efficiency improvement methods and notification content based on the user's emotional state. For example, if the user is feeling stressed, the advice will be softened.

[1116] Step 24:

[1117] The server sends tailored advice and notifications to the user.

[1118] Step 25:

[1119] The device receives the notification and displays it to the user, for example, displaying a message such as "Relax and keep driving."

[1120] Example 2

[1121] 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."

[1122] When receiving advice on fuel economy and maintenance, the advice is often uniform and does not take into account the individual user's driving situation or emotional state, making it difficult to provide effective advice.Furthermore, fuel price information and campaign information are not provided at a time that meets the needs of individual users, making it difficult to support users' economical car lifestyles.

[1123] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means by which the user inputs vehicle model information and driving data and transmits them to the server, a means by which the server stores the received vehicle model information and driving data in a database, a generation means by the server analyzing the driving data and generating a fuel economy improvement method, a generation means by the server generating personalized fuel economy improvement advice using a generation AI model, and a means by which the server notifies the user of the generated fuel economy improvement method. This makes it possible to provide more effective and timely advice and information according to the user's individual driving situation and emotional state.

[1124] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[1125] A "server" is a central computing device that receives, stores, and processes data sent by users.

[1126] "Database" means a structured data storage system for storing received vehicle type information and driving data.

[1127] The "generation means" is a means for analyzing the data acquired by the server and generating methods for improving fuel efficiency and other information.

[1128] A "generative AI model" is an artificial intelligence model that generates personalized advice and reports based on data.

[1129] "Personalized fuel economy improvement advice" is specific suggestions for improving fuel economy generated based on an individual user's driving data.

[1130] "Notification means" refers to a means for notifying the user of the generated information or advice.

[1131] The "Emotion Engine" is a function that recognizes the user's emotional state in real time and adjusts advice and notifications based on that information.

[1132] This invention is a system that provides personalized advice on improving fuel economy based on user input of vehicle information and driving data. The system centrally manages notifications of fuel price information and campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications, and uses an emotion engine to provide appropriate advice and reminders based on the user's emotional state.

[1133] First, the user uses an application on their smartphone or tablet to input vehicle information and driving data, such as the vehicle's make and model, annual mileage, average speed, etc. The device then sends the input information to the server.

[1134] The server stores the received vehicle information and driving data in a database. Database management systems such as MySQL and PostgreSQL can be used. The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4). The generative AI model generates advice for individual users on how to improve fuel efficiency based on the driving data. An example of a specific prompt is, "Please generate advice for improving fuel efficiency based on the user's driving data."

[1135] The generated advice is sent from the server to the device. Firebase Cloud Messaging can be used for this notification. For example, a notification saying "Reducing sudden acceleration will improve fuel economy by 5%" may be sent to the device. The server also has a function to obtain actual fuel economy data from other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel economy compares to other users. The comparison of actual fuel economy data is displayed in a ranking format to increase user motivation.

[1136] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[1137] Users also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. For example, a notification saying "The next vehicle inspection date is November 15, 2023" is sent to the user and displayed on the device.

[1138] The emotion engine recognizes the user's emotional state in real time. The device detects the user's emotional state using the emotion engine and sends that data to the server. The server then adjusts the generated fuel efficiency improvement methods and other notifications based on this emotional data. For example, if the user is feeling stressed, the advice can be expressed in a gentler tone. On the other hand, if the user is relaxed, specific improvement methods can be provided in detail. An example of a specific prompt is, "Please generate advice appropriate for when the user is feeling stressed."

[1139] Example 1:

[1140] User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses the generative AI model to provide personalized fuel efficiency improvement advice, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is stressed, the advice is softened.

[1141] Example prompts for generative AI models:

[1142] "Please change the advice that reducing hard acceleration will improve fuel economy by 5% to something more appropriate for when the user is stressed."

[1143] Example 2:

[1144] User B registers his / her refueling patterns and checks local gas station information on the app. Based on the collected price and campaign information, the server notifies him / her that there are discounts at specific gas stations on weekends. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it provides detailed campaign information.

[1145] Example prompts for generative AI models:

[1146] "Change discount information for nearby gas stations to details that users would like to see when they are relaxed."

[1147] Example 3:

[1148] User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[1149] Example prompts for generative AI models:

[1150] "When informing users that their next vehicle inspection date is November 15, 2023, please change the wording to something more appropriate for users with high stress levels."

[1151] In this way, this system not only provides personalized advice on improving fuel economy based on the user's vehicle information and driving data, but also utilizes an emotion engine to provide a more individualized experience based on the user's emotional state. As a whole, the system aims to support users' economical car life and improve driving efficiency.

[1152] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1153] Program processing flow

[1154] Step 1: User data entry

[1155] Users input their vehicle information and driving data into a device (smartphone or tablet), including vehicle model, manufacturer, annual mileage, average speed, etc.

[1156] Input: Vehicle information, driving data

[1157] Output: User input data

[1158] Specific action: Enter data using a form within the app and press the "Submit" button.

[1159] Step 2: Sending data to the server

[1160] The device sends the vehicle information and driving data entered by the user to the server using a secure communication protocol such as HTTPS.

[1161] Input: User-entered data

[1162] Output: Data sent to the server

[1163] Specific operation: Using the terminal's sending function, the input data is encrypted and sent to the server.

[1164] Step 3: Saving to the database

[1165] The server analyzes the received vehicle information and driving data and stores it in a database, which may use a database management system such as MySQL or PostgreSQL.

[1166] Input: Data sent to the server

[1167] Output: Status of completion of saving to database

[1168] Specific behavior: Establishes a database connection and inserts the received data into the appropriate tables.

[1169] Step 4: Analyze driving data and generate fuel economy improvement advice

[1170] The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4) to generate personalized fuel economy improvement advice.

[1171] Input: Driving data stored in the database

[1172] Output: Personalized advice

[1173] Specific operation: Driving data is obtained, and the prompt "Please generate advice for improving fuel efficiency based on the user's driving data" is input into the generative AI model to obtain advice.

[1174] Step 5: Advice Notification

[1175] The server then sends the generated fuel efficiency improvement advice to the user's device using services such as Firebase Cloud Messaging.

[1176] Input: Personalized advice

[1177] Output: Notification completion status to the terminal

[1178] Specific operation: The generated advice is sent to the device via Firebase Cloud Messaging.

[1179] Step 6: Capture actual fuel consumption data and generate comparison reports

[1180] The server obtains the actual fuel consumption data of other users of the same vehicle model and generates a comparison report.

[1181] Input: Actual fuel consumption data of other users obtained from the database

[1182] Output: Comparison report

[1183] Specific operation: Executes a database query to obtain fuel economy data from other users of the same vehicle model and creates a comparison report.

[1184] Step 7: Gather fuel price and promotion information

[1185] The server collects price and promotion information from local fuel supply facilities from the Internet and stores it in a database.

[1186] Input: Pricing and promotion information gathered from the web

[1187] Output: Status of completion of saving to database

[1188] Specific actions: Use APIs and web scraping to obtain information and store it in a database.

[1189] Step 8: Provide optimal information at the right time

[1190] The server notifies the user of the collected information at the optimal time based on the user's refueling patterns and location information.

[1191] Input: Refueling patterns, location information, price information, and campaign information stored in the database

[1192] Output: User notification

[1193] Specific operation: Analyzes the user's refueling patterns and location information and notifies them at the optimal time.

[1194] Step 9: Enter your vehicle inspection and maintenance schedule

[1195] Users enter vehicle inspection and maintenance schedules through the app.

[1196] Input: Vehicle inspection and maintenance schedule

[1197] Output: Schedule data sent to the server

[1198] Specific actions: Enter your schedule in the app and press the "Send" button.

[1199] Step 10: Generate and notify reminders

[1200] The server generates and notifies reminders at appropriate times based on the input schedule.

[1201] Input: Schedules stored in the database

[1202] Output: Reminder notification

[1203] Specific behavior: Set a timer based on your schedule and generate reminders to notify you.

[1204] Step 11: Emotion recognition and advice adjustment

[1205] The emotion engine recognizes the user's emotional state in real time and sends that data to the server, which analyzes the emotional data and adjusts the advice and notifications it generates.

[1206] Input: User emotion data

[1207] Output: Tailored advice based on emotions

[1208] How it works: The emotion engine recognizes emotions in real time and sends that data to the server, which then adjusts the tone and content of the advice based on the emotion data.

[1209] (Application example 2)

[1210] 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."

[1211] Improving automobile fuel efficiency and supporting efficient driving are important challenges, but existing systems are unable to provide personalized advice based on the user's driving data, or centrally manage fuel price information, vehicle inspection and maintenance schedules. Furthermore, they do not provide advice that takes into account the user's emotional state. This means that users cannot receive the information they need at the right time, making efficient vehicle management difficult.

[1212] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user input means through which the user inputs vehicle information and driving data and transmits them to the server, a means for storing the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method, a means for notifying the user of the generated fuel economy improvement method, a means for detecting the user's emotional state in real time using an emotion recognition engine and adjusting advice content using the information, a means for managing vehicle inspection and maintenance schedules and generating reminders at appropriate times, and a means for collecting price information and campaign information from local fuel supply facilities and notifying the user based on the user's refueling pattern and location information. This personalizes fuel economy improvement and vehicle management, making it possible to provide information at appropriate times according to the user's emotional state.

[1213] The "user input means" is a means by which a user inputs vehicle information and driving data and transmits them to the server.

[1214] The "means for storing in a database" refers to the means by which the server records and manages the vehicle information and driving data received from the user.

[1215] The "generation means" is a means by which the server analyzes the driving data and generates a fuel economy improvement method.

[1216] The "notification means" is a means for notifying the user of the generated fuel efficiency improvement method and other information.

[1217] The "emotion recognition engine" is an engine that detects the user's emotional state in real time and uses that information to adjust the content of advice.

[1218] "Means for generating reminders" refers to means for managing schedules for vehicle inspections and maintenance and notifying users at appropriate times.

[1219] The "means for collecting price information and campaign information from fuel supply facilities" refers to a means for collecting price information and campaign information from local fuel supply facilities and notifying the user of such information.

[1220] "Head-mounted displays and mobile devices" are devices that users can wear to check driving data and notifications in real time.

[1221] This invention is a system that uses the user's vehicle information and driving data to provide personalized fuel efficiency improvement advice, vehicle inspection and maintenance reminders, local fuel supply facility information, etc. Furthermore, it is possible to grasp the user's emotional state in real time using an emotion recognition engine and adjust the content of notifications accordingly.

[1222] First, the user inputs vehicle information and driving data using a smartphone, tablet, or head-mounted display. This device is equipped with a user input means that transmits the input information to a server. The server stores the received information in a database and performs the necessary data management.

[1223] The server then analyzes the saved driving data and uses a generative AI model to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or stops suddenly, the server generates specific advice on how to improve fuel efficiency by reducing these actions.

[1224] The generated fuel efficiency improvement methods are sent to the user's device. The content of the notification is adjusted according to the user's emotional state using an emotion recognition engine. For example, if the user is stressed, the advice is given in a gentle manner, while if the user is relaxed, the system provides detailed and precise improvement methods.

[1225] For vehicle inspections and maintenance, users input their schedules into their devices. The server stores this in a database and generates reminders at the appropriate times. This allows users to receive appropriate notifications when inspection or maintenance dates are approaching, allowing them to prepare in advance.

[1226] The server also collects price and promotion information from local fuel supply facilities and notifies users at the optimal time based on their fueling patterns and location, allowing them to refuel economically.

[1227] For illustrative purposes, the following prompt sentences can be used:

[1228] "Reducing sudden acceleration will improve fuel economy. Please provide specific advice based on driving data. If the user is relaxed, please provide detailed instructions on how to improve, but if the user is stressed, please use gentler language."

[1229] This generative AI model is implemented using Python, TensorFlow, and OpenCV to perform emotion recognition and data analysis. The backend uses Flask and Django, and data is managed using a real-time database such as Firebase. Head-mounted displays and mobile devices can connect with these applications to provide users with real-time information.

[1230] This system will improve fuel efficiency and personalize vehicle management, providing appropriate advice based on the user's emotional state.

[1231] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1232] Step 1:

[1233] The user inputs vehicle information and driving data into a smartphone, tablet, or head-mounted display. The input data includes the vehicle type, mileage, fuel economy, frequency of sudden acceleration and sudden stops, etc. This data is then sent from the device to a server.

[1234] Input: Vehicle information, driving data

[1235] Output: Send data to the server

[1236] Step 2:

[1237] The server stores the received vehicle information and driving data in a database. When storing the data, it validates and checks the format, and generates an appropriate error message if there is an error.

[1238] Input: Received vehicle information, driving data

[1239] Output: Vehicle information and driving data stored in the database

[1240] Step 3:

[1241] The server analyzes the stored driving data and generates fuel economy improvement methods using a generative AI model that detects specific patterns and trends and performs data calculations to identify the best improvement methods for the user.

[1242] Input: Driving data

[1243] Output: How to improve fuel economy

[1244] Step 4:

[1245] The generated fuel efficiency improvement methods are then sent to the user's device. The notification content is adjusted according to the user's emotional state detected by an emotion recognition engine. For example, if the user is under high stress, the advice will be softened.

[1246] Input: fuel efficiency improvement methods, emotional information

[1247] Output: Adjusted advice notification

[1248] Step 5:

[1249] The user inputs vehicle inspection and maintenance schedules into the terminal, which are then sent to the server and stored in a database.

[1250] Input: Vehicle inspection, maintenance schedule

[1251] Output: Sending schedule data to the server

[1252] Step 6:

[1253] The server generates reminders at appropriate times and sends them to the user's device. The timing and content of the notifications are adjusted taking into account the user's emotional state.

[1254] Input: Schedule data, emotional information

[1255] Output: Reminder notification

[1256] Step 7:

[1257] The server collects pricing and promotional information from local fuel stations, stores it in a database, and notifies users at optimal times based on their fueling patterns and location.

[1258] Input: fuel price information, campaign information

[1259] Output: Information notification at the optimal time

[1260] Step 8:

[1261] The user's device uses an emotion recognition engine to analyze the user's facial expressions and behavior in real time, and sends the resulting emotional data to the server, which then adjusts the content of notifications and fuel efficiency improvement advice based on this data.

[1262] Input: User emotion data

[1263] Output: Adjusted notification content

[1264] This system allows users to improve fuel efficiency and manage their vehicle in a personalized way, and provides appropriate advice and notifications based on their emotional state.

[1265] 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.

[1266] 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.

[1267] 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.

[1268] [Fourth embodiment]

[1269] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1270] 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.

[1271] 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).

[1272] 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.

[1273] 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.

[1274] 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).

[1275] 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.

[1276] 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.

[1277] 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.

[1278] 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.

[1279] 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.

[1280] 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.

[1281] 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."

[1282] The present invention relates to a system that allows users to input vehicle model information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[1283] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[1284] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[1285] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[1286] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[1287] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[1288] Specific examples

[1289] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user of a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[1290] Example 2: User B has registered his / her refueling habits and is checking local gas station information in the app. The server uses the collected price and campaign information to notify him / her that there is a discount at a particular gas station on the weekend. User B can take advantage of this to refuel economically.

[1291] Example 3: User C enters the schedule for vehicle inspection dates into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him to smoothly prepare for the inspection.

[1292] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle model information and driving data.

[1293] The processing flow will be explained below.

[1294] Step 1:

[1295] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[1296] Step 2:

[1297] The terminal transmits the input information to the server.

[1298] Step 3:

[1299] The server stores the received vehicle information and driving data in a database.

[1300] Step 4:

[1301] The server retrieves the user's driving data from the database.

[1302] Step 5:

[1303] The server uses generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, it analyzes the frequency of sudden acceleration and provides advice on how to reduce it.

[1304] Step 6:

[1305] The server notifies the user of the generated fuel efficiency improvement method.

[1306] Step 7:

[1307] The device receives the notification and displays it to the user, for example, "Reducing sudden acceleration will improve fuel economy by 5%."

[1308] Step 8:

[1309] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[1310] Step 9:

[1311] The server notifies the user of the comparison report.

[1312] Step 10:

[1313] The device receives the notification and displays it to the user in a ranking format.

[1314] Step 11:

[1315] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[1316] Step 12:

[1317] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[1318] Step 13:

[1319] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[1320] Step 14:

[1321] The device receives the notification and displays it to the user.

[1322] Step 15:

[1323] Users enter vehicle inspection and maintenance schedules into the application.

[1324] Step 16:

[1325] The terminal transmits schedule information to the server.

[1326] Step 17:

[1327] The server stores the received schedule information in a database.

[1328] Step 18:

[1329] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[1330] Step 19:

[1331] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[1332] Step 20:

[1333] The device receives the reminder notification and displays it to the user.

[1334] Example 1

[1335] 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."

[1336] Conventional vehicle management systems have difficulty providing personalized advice on improving fuel efficiency based on users' driving data, and they are unable to provide a wide range of functions in a unified manner, such as comparing fuel efficiency with other users, notifying them of fuel price information, or managing vehicle inspection and maintenance schedules. This has meant that users have to use different applications and systems individually, which has often been inconvenient.

[1337] 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.

[1338] In this invention, the server includes a user input means for the user to input vehicle information and driving data and send it to the server, a means for the server to store the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method using a generative AI model, a means for notifying the user of the generated fuel economy improvement method, a means for the server to acquire fuel economy data of similar users and generate a comparison report, a means for the server to collect price information and campaign information from fuel supply facilities and generate and notify optimal information based on the user's refueling patterns and location information, and a means for the user to input vehicle inspection dates and maintenance schedules, the server to store this, and generate and notify reminders at appropriate times. This allows users to comprehensively improve fuel economy and manage their vehicles using a single system.

[1339] "User" means any individual or legal entity that owns a vehicle and uses the system.

[1340] "Vehicle information" refers to basic information such as the car's manufacturer, model, and year.

[1341] "Driving data" refers to information about a user's driving, such as daily mileage, frequency of sudden acceleration and braking, and average speed.

[1342] A "server" is a computer system that receives, stores, and analyzes data sent by users.

[1343] "Database" refers to a collection of information in which driving data and vehicle information are stored by a server.

[1344] "Generative AI model" refers to the artificial intelligence model used by the server to analyze driving data and generate fuel efficiency improvement advice appropriate for each individual user.

[1345] "Methods for improving fuel efficiency" refers to advice on specific driving methods and actions to improve fuel efficiency.

[1346] "Notification means" refers to the means for sending messages or alerts from the server to the user's device.

[1347] "Comparison Report" refers to a report summarizing the results of comparing fuel economy data with other users.

[1348] "Fuel distribution facility" means a gas station or other facility that provides fuel.

[1349] "Price Information" refers to information regarding the price of fuel offered at a fuel supply facility.

[1350] "Campaign Information" refers to information regarding discounts and special offers offered at fuel supply facilities.

[1351] "Reminder" refers to a message or alert that is sent to the user based on a pre-set schedule.

[1352] "Inspection Date" means the date on which a vehicle is to be inspected.

[1353] "Maintenance Schedule" means a date scheduled for performing maintenance on a Vehicle.

[1354] The present invention relates to a system that allows users to input vehicle information and driving data, and based on that data, provides personalized advice on improving fuel efficiency, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and notifications of vehicle inspections and maintenance.

[1355] Hardware and software used

[1356] The present invention is implemented mainly using the following hardware and software.

[1357] Hardware: smartphones, tablets, servers, internet connections

[1358] Software: applications (installed on smartphones or tablets), database management systems, generative AI models (e.g., OpenAI's GPT-4)

[1359] Data processing and calculation flow

[1360] Users use a smartphone or tablet to enter vehicle information and driving data into the application, including distance traveled, frequency of sudden acceleration and braking, average speed, etc. The entered information is sent by the device to a server via an internet connection.

[1361] The server stores the received data in a database. This data is efficiently managed using a database management system. The server analyzes the driving data stored in the database and uses a generative AI model to generate fuel efficiency improvement advice tailored to each individual user. For example, the server analyzes the user's driving patterns and provides specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[1362] The generated advice is sent from the server to the user's device, where it can be viewed on the application. The server also retrieves fuel economy data from a database of similar users and compares it with the user's fuel economy. The comparison results are displayed in a ranking format and notified to the user.

[1363] The server also collects price and campaign information from fuel supply facilities via the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a discount campaign is being held at a nearby gas station, the server will notify the user of this information.

[1364] Users can also input vehicle inspection dates and maintenance schedules into the application. The server stores the input schedule in a database and generates reminders at appropriate times to notify the user's device. For example, a notification such as "The next vehicle inspection date is November 15, 2023" may be sent.

[1365] Specific examples

[1366] Example 1:

[1367] A scenario in which a user enters daily driving data into an app and receives advice on how to improve fuel efficiency.

[1368] Prompt: "Generate fuel economy recommendations based on the user's driving data."

[1369] Example 2:

[1370] A scenario in which refueling patterns are registered and local gas station information is utilized.

[1371] Prompt: "Regularly collect pricing information and promotions at nearby gas stations."

[1372] Example 3:

[1373] Scenario for entering vehicle inspection date and receiving reminder notifications.

[1374] Prompt: "The next inspection date is November 15, 2023."

[1375] As described above, this system provides a means for achieving multifaceted improvements in fuel efficiency and an economical car life based on the user's vehicle information and driving data.

[1376] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1377] Step 1:

[1378] Using the device, users input vehicle information and driving data into the application, such as "Toyota Prius 2020 model," "Today's mileage: 50km, 5 sudden accelerations, 3 sudden brakings," and so on.

[1379] Input: Vehicle information, driving data

[1380] Output: Data sent from the device to the server

[1381] Step 2:

[1382] The device sends the entered vehicle information and driving data to a server via an internet connection. The moment the "Send" button is pressed, the information is sent to the server, and the server returns a confirmation message saying "Data saved successfully."

[1383] Input: Data entered into the terminal

[1384] Output: Data sent to the server

[1385] Step 3:

[1386] The server stores the received vehicle information and driving data in a database, which is efficiently managed using a database management system.

[1387] For example, the driving data of user A is saved as "distance traveled 50km, sudden acceleration 5 times, sudden braking 3 times."

[1388] Input: Data sent from the terminal

[1389] Output: Data stored in the database

[1390] Step 4:

[1391] The server analyzes the driving data stored in the database and generates fuel efficiency improvement advice using a generative AI model (e.g., OpenAI's GPT-4). For example, the generative AI model is used to generate specific advice such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[1392] Input: Driving data stored in the database

[1393] Output: Fuel economy improvement advice

[1394] Step 5:

[1395] The server then sends the generated fuel efficiency improvement advice to the user's device, and the user confirms the specific advice on the application, such as "reducing sudden acceleration will improve fuel efficiency by 5%."

[1396] Input: Generated fuel economy improvement advice

[1397] Output: Advice sent to the user's device

[1398] Step 6:

[1399] The server retrieves fuel economy data from the database from other users of the same type, compares it with the user's fuel economy data, and displays it in a ranking format. For example, it generates a comparison report that notifies the user that "your fuel economy is in the top 20% overall."

[1400] Input: Fuel economy data of other users retrieved from the database

[1401] Output: Ranking comparison report

[1402] Step 7:

[1403] The server collects price information and campaign information from fuel supply facilities via the Internet and stores it in a database. For example, it collects information about a 10% discount campaign that is being held at a nearby gas station on the weekend.

[1404] Input: Fuel supply facility price information, campaign information

[1405] Output: Price information and campaign information stored in the database

[1406] Step 8:

[1407] The server analyzes the user's fueling patterns and location information and notifies the user of price and campaign information at the optimal time, for example, notifying the user that a discount campaign is being held at a nearby gas station.

[1408] Input: User's fueling patterns, location information

[1409] Output: Price information and campaign information notified to the user

[1410] Step 9:

[1411] Users input their vehicle inspection and maintenance dates into the application. For example, they might enter "next vehicle inspection date is November 15, 2023."

[1412] Input: Inspection date, maintenance schedule

[1413] Output: Schedule data sent to the server

[1414] Step 10:

[1415] The server stores the entered vehicle inspection dates and maintenance schedules in a database and generates reminders to notify the user at appropriate times, for example, "The next vehicle inspection date is November 15, 2023."

[1416] Input: Vehicle inspection dates and maintenance schedules stored in the database

[1417] Output: Reminder to be sent to the user

[1418] (Application example 1)

[1419] 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."

[1420] Conventional fuel economy improvement systems are limited to providing advice based on user behavior data, and do not adequately integrate or personalize information. Furthermore, fuel supply and maintenance information is not managed centrally, which often increases complexity for users.

[1421] 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.

[1422] In this invention, the server includes means for the user to input vehicle model information and driving data and transmit them to the server, means for the server to store the received vehicle model information and driving data in a database, means for the server to analyze the driving data and generate fuel economy improvement methods, means for notifying the user of the generated fuel economy improvement methods, means for the server to collect local fuel price information and campaign information based on location information and generate optimal information to support the user in economical refueling, and means for managing vehicle inspection and maintenance information and generating reminders at appropriate times. This enables fuel economy improvement and information management to meet the diverse needs of users.

[1423] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[1424] The "means for storing in a database" is a means for storing the vehicle model information and driving data received by the server in a database.

[1425] The "analysis means" is a means for analyzing the driving data received by the server and generating a method for improving fuel economy.

[1426] The "notification means" is a means for notifying the user of the generated fuel economy improvement method.

[1427] The "location information collection means" is a means by which the server collects local fuel price information and campaign information based on location information.

[1428] The "generation means" is a means for generating optimal information and reminders to assist the user in economical refueling based on the information collected by the server.

[1429] The "vehicle inspection and maintenance management means" is a means by which the server manages vehicle inspection and maintenance information and generates reminders at appropriate times.

[1430] This invention is a system in which users input vehicle model information and driving data, and based on that, a server provides personalized fuel efficiency improvement advice and centrally manages fuel price information, campaign information, and vehicle inspection and maintenance notifications. Users input vehicle model information and driving data into an application via a device such as a smartphone or tablet. The device then sends this information to the server, which then stores it in a database.

[1431] The server uses a generative AI model based on the saved driving data to generate personalized fuel economy improvement methods. For example, based on the user's driving data, it can provide specific advice such as "reducing sudden acceleration will improve fuel economy by 5%." This advice is notified to the user and can be viewed on the device.

[1432] The server also has a function to obtain the actual fuel consumption data of other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[1433] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores this data in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[1434] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications such as "The next vehicle inspection date is November 15, 2023," allowing them to prepare in advance.

[1435] For example, if a user inputs information such as "Vehicle information: general passenger car, Driving data: frequent sudden acceleration, Location: metropolitan area," the server will use the AI ​​to generate fuel efficiency improvement advice based on this information, providing specific advice such as "reducing sudden acceleration will improve fuel efficiency by 10%."

[1436] An example of a prompt sentence is, "Please generate advice to improve fuel efficiency based on the user information. The vehicle model information is 'general passenger car' and the driving data is 'frequent sudden acceleration'." In this way, the server provides the user with optimal advice and realizes multifaceted information management.

[1437] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1438] Step 1:

[1439] The user inputs vehicle model information and driving data, which are then sent to the server via the device. The input data includes vehicle model information (e.g., a typical passenger car) and driving data (e.g., frequent sudden acceleration). The device formats this information and sends it to the server as an HTTP request.

[1440] Input: Vehicle information, driving data

[1441] Output: Data sent to the server

[1442] Step 2:

[1443] The server analyzes the received vehicle information and driving data and saves it in a database. The server checks the integrity of the data and stores it in the database in an appropriate format. For example, the received data is saved in the database in JSON format.

[1444] Input: Received data

[1445] Output: Data stored in the database

[1446] Step 3:

[1447] The server uses the stored driving data to generate personalized fuel economy improvement methods using a generative AI model. Features of the driving data are extracted and input into the AI ​​model to generate specific advice (e.g., reducing sudden acceleration will improve fuel economy by 5%).

[1448] Input: Saved driving data

[1449] Output: Generated fuel economy improvement advice

[1450] Step 4:

[1451] The server notifies the user of the fuel efficiency improvement method that was generated. The server generates notification data and sends it to the user's device. It is displayed on the device as a pop-up notification or message notification.

[1452] Input: Generated fuel economy improvement advice

[1453] Output: Notification to the user's device

[1454] Step 5:

[1455] The server retrieves the actual fuel consumption data of other users of the same vehicle model and generates a comparison report. The server queries the relevant data from the database, extracts statistical information, and creates a report that displays the fuel consumption rankings.

[1456] Input: Actual fuel consumption data of other users of the same vehicle model

[1457] Output: Generated comparison report

[1458] Step 6:

[1459] The server collects price and campaign information from local fuel supply facilities, and uses an API via the Internet to retrieve the latest price and campaign information and store it in a database.

[1460] Input: Fuel price information and campaign information collected from the Internet

[1461] Output: Price information and campaign information stored in the database

[1462] Step 7:

[1463] The server generates and notifies the user of the most economical options based on their refueling patterns and location. It analyzes location information and patterns and notifies the user of the most economical options (e.g., discount information for nearby gas stations).

[1464] Input: User's fueling patterns, location information

[1465] Output: Notifying the user of the generated information

[1466] Step 8:

[1467] The server manages vehicle inspection and maintenance information and generates reminders at appropriate times based on the schedule information entered by the user, and notifies the user's device.

[1468] Input: Inspection and maintenance schedules entered by the user

[1469] Output: Generated reminder notification

[1470] 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.

[1471] This invention combines an emotion engine with a system that provides personalized advice on improving fuel economy based on user input of vehicle model information and driving data, and also centrally manages fuel price information, campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications. The emotion engine recognizes the user's emotions and further utilizes this data to provide more appropriate advice and reminders.

[1472] Users input vehicle information and driving data into the application via a device (e.g., a smartphone or tablet), which then sends the input information to the server, which then stores the data in a database.

[1473] The server analyzes the driving data stored in the database and uses generative AI to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or brakes suddenly, it can provide advice on how to improve fuel efficiency by reducing these actions. The generated advice is notified to the user and can be viewed on their device.

[1474] Furthermore, the server has the function of acquiring the actual fuel consumption data of other users of the same vehicle model and generating a comparison report. This allows users to understand how their fuel consumption compares to other users. It is also possible to display the fuel consumption in a ranking format, which can increase user motivation.

[1475] The server also collects price and promotion information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notifies the user at the most appropriate time. For example, if a nearby gas station is running a discount campaign, the server can notify the user of this information, helping them to refuel economically.

[1476] Users can also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. Users can receive notifications when the next inspection or maintenance date approaches, allowing them to prepare in advance. For example, a notification such as "The next inspection date is November 15, 2023" is sent to the user and displayed on the device.

[1477] The emotion engine recognizes the user's emotions in real time, detecting their emotional state while driving and their stress level. This allows the generated fuel efficiency improvement advice and other notifications to be adjusted based on the user's emotional state. For example, if the user is stressed, the advice can be softened, while if the user is relaxed, the advice can be detailed and specific.

[1478] Specific examples

[1479] Example 1: User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses generative AI to provide personalized advice on improving fuel efficiency, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is feeling stressed, the advice is softened.

[1480] Example 2: User B has registered his / her refueling patterns and is checking local gas station information in the app. Based on the collected price and campaign information, the server notifies him / her that there is a discount at a specific gas station on the weekend. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it will provide him / her with detailed campaign information.

[1481] Example 3: User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[1482] As described above, this system not only achieves multifaceted improvements in fuel efficiency and an economical driving lifestyle based on the user's vehicle information, driving data, and emotional state, but also provides a more personalized experience through emotion recognition.

[1483] The processing flow will be explained below.

[1484] Step 1:

[1485] The user opens the application and enters vehicle information and driving data (e.g., distance driven, fuel used, driving time).

[1486] Step 2:

[1487] The terminal transmits the input information to the server.

[1488] Step 3:

[1489] The server stores the received vehicle information and driving data in a database.

[1490] Step 4:

[1491] The server retrieves the user's driving data from the database.

[1492] Step 5:

[1493] The server uses the generated AI to analyze driving data and generate fuel efficiency improvement methods. For example, if the user frequently accelerates suddenly, it generates advice on reducing sudden acceleration.

[1494] Step 6:

[1495] The server notifies the user how to improve fuel economy, for example, "reducing sudden acceleration will improve fuel economy by 5%."

[1496] Step 7:

[1497] The device receives the notification and displays it to the user.

[1498] Step 8:

[1499] The server retrieves the actual fuel consumption data of other users of the same vehicle model from the database and generates a comparison report.

[1500] Step 9:

[1501] The server notifies the user with a comparison report, for example, "Your fuel economy is 90% of the average of other users."

[1502] Step 10:

[1503] The device receives the notification and displays it to the user in a ranking format.

[1504] Step 11:

[1505] The server collects price information and promotional information from local fuel supply facilities from the Internet and stores it in a database.

[1506] Step 12:

[1507] The server generates information on the most suitable fuel supply facilities based on the user's refueling patterns and location information.

[1508] Step 13:

[1509] The server notifies the user of the generated information, for example, "There is a discount campaign at a nearby gas station."

[1510] Step 14:

[1511] The device receives the notification and displays it to the user.

[1512] Step 15:

[1513] Users enter vehicle inspection and maintenance schedules into the application.

[1514] Step 16:

[1515] The terminal transmits schedule information to the server.

[1516] Step 17:

[1517] The server stores the received schedule information in a database.

[1518] Step 18:

[1519] When a vehicle inspection or maintenance date approaches, the server uses AI to generate a reminder.

[1520] Step 19:

[1521] The server notifies the user of reminders, for example, "The next vehicle inspection date is November 15, 2023."

[1522] Step 20:

[1523] The device receives the reminder notification and displays it to the user.

[1524] Step 21:

[1525] The emotion engine recognizes the user's emotional state in real time and collects emotional data while driving.

[1526] Step 22:

[1527] The server analyzes the user's emotional state based on emotional data and determines their stress level and relaxation state while driving.

[1528] Step 23:

[1529] The server adjusts the generated fuel efficiency improvement methods and notification content based on the user's emotional state. For example, if the user is feeling stressed, the advice will be softened.

[1530] Step 24:

[1531] The server sends tailored advice and notifications to the user.

[1532] Step 25:

[1533] The device receives the notification and displays it to the user, for example, displaying a message such as "Relax and keep driving."

[1534] Example 2

[1535] 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."

[1536] When receiving advice on fuel economy and maintenance, the advice is often uniform and does not take into account the individual user's driving situation or emotional state, making it difficult to provide effective advice.Furthermore, fuel price information and campaign information are not provided at a time that meets the needs of individual users, making it difficult to support users' economical car lifestyles.

[1537] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a user input means by which the user inputs vehicle model information and driving data and transmits them to the server, a means by which the server stores the received vehicle model information and driving data in a database, a generation means by the server analyzing the driving data and generating a fuel economy improvement method, a generation means by the server generating personalized fuel economy improvement advice using a generation AI model, and a means by which the server notifies the user of the generated fuel economy improvement method. This makes it possible to provide more effective and timely advice and information according to the user's individual driving situation and emotional state.

[1538] The "user input means" is a means by which the user inputs vehicle type information and driving data and transmits them to the server.

[1539] A "server" is a central computing device that receives, stores, and processes data sent by users.

[1540] "Database" means a structured data storage system for storing received vehicle type information and driving data.

[1541] The "generation means" is a means for analyzing the data acquired by the server and generating methods for improving fuel efficiency and other information.

[1542] A "generative AI model" is an artificial intelligence model that generates personalized advice and reports based on data.

[1543] "Personalized fuel economy improvement advice" is specific suggestions for improving fuel economy generated based on an individual user's driving data.

[1544] "Notification means" refers to a means for notifying the user of the generated information or advice.

[1545] The "Emotion Engine" is a function that recognizes the user's emotional state in real time and adjusts advice and notifications based on that information.

[1546] This invention is a system that provides personalized advice on improving fuel economy based on user input of vehicle information and driving data. The system centrally manages notifications of fuel price information and campaign information, comparison of actual fuel consumption data, and vehicle inspection and maintenance notifications, and uses an emotion engine to provide appropriate advice and reminders based on the user's emotional state.

[1547] First, the user uses an application on their smartphone or tablet to input vehicle information and driving data, such as the vehicle's make and model, annual mileage, average speed, etc. The device then sends the input information to the server.

[1548] The server stores the received vehicle information and driving data in a database. Database management systems such as MySQL and PostgreSQL can be used. The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4). The generative AI model generates advice for individual users on how to improve fuel efficiency based on the driving data. An example of a specific prompt is, "Please generate advice for improving fuel efficiency based on the user's driving data."

[1549] The generated advice is sent from the server to the device. Firebase Cloud Messaging can be used for this notification. For example, a notification saying "Reducing sudden acceleration will improve fuel economy by 5%" may be sent to the device. The server also has a function to obtain actual fuel economy data from other users of the same vehicle model and generate a comparison report. This allows users to understand how their fuel economy compares to other users. The comparison of actual fuel economy data is displayed in a ranking format to increase user motivation.

[1550] In addition, the server collects price and campaign information from local fuel supply facilities (such as gas stations) from the Internet and stores it in a database. This information is analyzed based on the user's refueling patterns and location information, and notified to the user at the optimal time. For example, if a nearby gas station is running a discount campaign, the server will notify the user of this information to help them refuel economically.

[1551] Users also enter schedules for vehicle inspections and maintenance into the application. The server stores these schedules in a database and generates reminders at appropriate times. For example, a notification saying "The next vehicle inspection date is November 15, 2023" is sent to the user and displayed on the device.

[1552] The emotion engine recognizes the user's emotional state in real time. The device detects the user's emotional state using the emotion engine and sends that data to the server. The server then adjusts the generated fuel efficiency improvement methods and other notifications based on this emotional data. For example, if the user is feeling stressed, the advice can be expressed in a gentler tone. On the other hand, if the user is relaxed, specific improvement methods can be provided in detail. An example of a specific prompt is, "Please generate advice appropriate for when the user is feeling stressed."

[1553] Example 1:

[1554] User A registers his / her car information in the app and inputs daily fueling records and driving data. The server receives this information and uses the generative AI model to provide personalized fuel efficiency improvement advice, notifying the user with a specific suggestion such as "reducing sudden acceleration will improve fuel efficiency by 5%." At the same time, if the emotion engine determines that the user is stressed, the advice is softened.

[1555] Example prompts for generative AI models:

[1556] "Please change the advice that reducing hard acceleration will improve fuel economy by 5% to something more appropriate for when the user is stressed."

[1557] Example 2:

[1558] User B registers his / her refueling patterns and checks local gas station information on the app. Based on the collected price and campaign information, the server notifies him / her that there are discounts at specific gas stations on weekends. User B can take advantage of this to refuel economically. If the emotion engine detects that the user is relaxed, it provides detailed campaign information.

[1559] Example prompts for generative AI models:

[1560] "Change discount information for nearby gas stations to details that users would like to see when they are relaxed."

[1561] Example 3:

[1562] User C enters the schedule for his / her vehicle inspection date into the app, and the server generates reminders at the appropriate time. User C receives a notification that "The next vehicle inspection date is November 15, 2023," allowing him / her to smoothly prepare for the inspection. If the emotion engine recognizes that the user's stress level is high, it can send a reminder in advance to give him / her some breathing room.

[1563] Example prompts for generative AI models:

[1564] "When informing users that their next vehicle inspection date is November 15, 2023, please change the wording to something more appropriate for users with high stress levels."

[1565] In this way, this system not only provides personalized advice on improving fuel economy based on the user's vehicle information and driving data, but also utilizes an emotion engine to provide a more individualized experience based on the user's emotional state. As a whole, the system aims to support users' economical car life and improve driving efficiency.

[1566] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1567] Program processing flow

[1568] Step 1: User data entry

[1569] Users input their vehicle information and driving data into a device (smartphone or tablet), including vehicle model, manufacturer, annual mileage, average speed, etc.

[1570] Input: Vehicle information, driving data

[1571] Output: User input data

[1572] Specific action: Enter data using a form within the app and press the "Submit" button.

[1573] Step 2: Sending data to the server

[1574] The device sends the vehicle information and driving data entered by the user to the server using a secure communication protocol such as HTTPS.

[1575] Input: User-entered data

[1576] Output: Data sent to the server

[1577] Specific operation: Using the terminal's sending function, the input data is encrypted and sent to the server.

[1578] Step 3: Saving to the database

[1579] The server analyzes the received vehicle information and driving data and stores it in a database, which may use a database management system such as MySQL or PostgreSQL.

[1580] Input: Data sent to the server

[1581] Output: Status of completion of saving to database

[1582] Specific behavior: Establishes a database connection and inserts the received data into the appropriate tables.

[1583] Step 4: Analyze driving data and generate fuel economy improvement advice

[1584] The server analyzes the driving data stored in the database using a generative AI model (e.g., GPT-4) to generate personalized fuel economy improvement advice.

[1585] Input: Driving data stored in the database

[1586] Output: Personalized advice

[1587] Specific operation: Driving data is obtained, and the prompt "Please generate advice for improving fuel efficiency based on the user's driving data" is input into the generative AI model to obtain advice.

[1588] Step 5: Advice Notification

[1589] The server then sends the generated fuel efficiency improvement advice to the user's device using services such as Firebase Cloud Messaging.

[1590] Input: Personalized advice

[1591] Output: Notification completion status to the terminal

[1592] Specific operation: The generated advice is sent to the device via Firebase Cloud Messaging.

[1593] Step 6: Capture actual fuel consumption data and generate comparison reports

[1594] The server obtains the actual fuel consumption data of other users of the same vehicle model and generates a comparison report.

[1595] Input: Actual fuel consumption data of other users obtained from the database

[1596] Output: Comparison report

[1597] Specific operation: Executes a database query to obtain fuel economy data from other users of the same vehicle model and creates a comparison report.

[1598] Step 7: Gather fuel price and promotion information

[1599] The server collects price and promotion information from local fuel supply facilities from the Internet and stores it in a database.

[1600] Input: Pricing and promotion information gathered from the web

[1601] Output: Status of completion of saving to database

[1602] Specific actions: Use APIs and web scraping to obtain information and store it in a database.

[1603] Step 8: Provide optimal information at the right time

[1604] The server notifies the user of the collected information at the optimal time based on the user's refueling patterns and location information.

[1605] Input: Refueling patterns, location information, price information, and campaign information stored in the database

[1606] Output: User notification

[1607] Specific operation: Analyzes the user's refueling patterns and location information and notifies them at the optimal time.

[1608] Step 9: Enter your vehicle inspection and maintenance schedule

[1609] Users enter vehicle inspection and maintenance schedules through the app.

[1610] Input: Vehicle inspection and maintenance schedule

[1611] Output: Schedule data sent to the server

[1612] Specific actions: Enter your schedule in the app and press the "Send" button.

[1613] Step 10: Generate and notify reminders

[1614] The server generates and notifies reminders at appropriate times based on the input schedule.

[1615] Input: Schedules stored in the database

[1616] Output: Reminder notification

[1617] Specific behavior: Set a timer based on your schedule and generate reminders to notify you.

[1618] Step 11: Emotion recognition and advice adjustment

[1619] The emotion engine recognizes the user's emotional state in real time and sends that data to the server, which analyzes the emotional data and adjusts the advice and notifications it generates.

[1620] Input: User emotion data

[1621] Output: Tailored advice based on emotions

[1622] How it works: The emotion engine recognizes emotions in real time and sends that data to the server, which then adjusts the tone and content of the advice based on the emotion data.

[1623] (Application example 2)

[1624] 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."

[1625] Improving automobile fuel efficiency and supporting efficient driving are important challenges, but existing systems are unable to provide personalized advice based on the user's driving data, or centrally manage fuel price information, vehicle inspection and maintenance schedules. Furthermore, they do not provide advice that takes into account the user's emotional state. This means that users cannot receive the information they need at the right time, making efficient vehicle management difficult.

[1626] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a user input means through which the user inputs vehicle information and driving data and transmits them to the server, a means for storing the received vehicle information and driving data in a database, a generation means for the server to analyze the driving data and generate a fuel economy improvement method, a means for notifying the user of the generated fuel economy improvement method, a means for detecting the user's emotional state in real time using an emotion recognition engine and adjusting advice content using the information, a means for managing vehicle inspection and maintenance schedules and generating reminders at appropriate times, and a means for collecting price information and campaign information from local fuel supply facilities and notifying the user based on the user's refueling pattern and location information. This personalizes fuel economy improvement and vehicle management, making it possible to provide information at appropriate times according to the user's emotional state.

[1627] The "user input means" is a means by which a user inputs vehicle information and driving data and transmits them to the server.

[1628] The "means for storing in a database" refers to the means by which the server records and manages the vehicle information and driving data received from the user.

[1629] The "generation means" is a means by which the server analyzes the driving data and generates a fuel economy improvement method.

[1630] The "notification means" is a means for notifying the user of the generated fuel efficiency improvement method and other information.

[1631] The "emotion recognition engine" is an engine that detects the user's emotional state in real time and uses that information to adjust the content of advice.

[1632] "Means for generating reminders" refers to means for managing schedules for vehicle inspections and maintenance and notifying users at appropriate times.

[1633] The "means for collecting price information and campaign information from fuel supply facilities" refers to a means for collecting price information and campaign information from local fuel supply facilities and notifying the user of such information.

[1634] "Head-mounted displays and mobile devices" are devices that users can wear to check driving data and notifications in real time.

[1635] This invention is a system that uses the user's vehicle information and driving data to provide personalized fuel efficiency improvement advice, vehicle inspection and maintenance reminders, local fuel supply facility information, etc. Furthermore, it is possible to grasp the user's emotional state in real time using an emotion recognition engine and adjust the content of notifications accordingly.

[1636] First, the user inputs vehicle information and driving data using a smartphone, tablet, or head-mounted display. This device is equipped with a user input means that transmits the input information to a server. The server stores the received information in a database and performs the necessary data management.

[1637] The server then analyzes the saved driving data and uses a generative AI model to generate fuel efficiency improvement methods tailored to each individual user. For example, if a user frequently accelerates or stops suddenly, the server generates specific advice on how to improve fuel efficiency by reducing these actions.

[1638] The generated fuel efficiency improvement methods are sent to the user's device. The content of the notification is adjusted according to the user's emotional state using an emotion recognition engine. For example, if the user is stressed, the advice is given in a gentle manner, while if the user is relaxed, the system provides detailed and precise improvement methods.

[1639] For vehicle inspections and maintenance, users input their schedules into their devices. The server stores this in a database and generates reminders at the appropriate times. This allows users to receive appropriate notifications when inspection or maintenance dates are approaching, allowing them to prepare in advance.

[1640] The server also collects price and promotion information from local fuel supply facilities and notifies users at the optimal time based on their fueling patterns and location, allowing them to refuel economically.

[1641] For illustrative purposes, the following prompt sentences can be used:

[1642] "Reducing sudden acceleration will improve fuel economy. Please provide specific advice based on driving data. If the user is relaxed, please provide detailed instructions on how to improve, but if the user is stressed, please use gentler language."

[1643] This generative AI model is implemented using Python, TensorFlow, and OpenCV to perform emotion recognition and data analysis. The backend uses Flask and Django, and data is managed using a real-time database such as Firebase. Head-mounted displays and mobile devices can connect with these applications to provide users with real-time information.

[1644] This system will improve fuel efficiency and personalize vehicle management, providing appropriate advice based on the user's emotional state.

[1645] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1646] Step 1:

[1647] The user inputs vehicle information and driving data into a smartphone, tablet, or head-mounted display. The input data includes the vehicle type, mileage, fuel economy, frequency of sudden acceleration and sudden stops, etc. This data is then sent from the device to a server.

[1648] Input: Vehicle information, driving data

[1649] Output: Send data to the server

[1650] Step 2:

[1651] The server stores the received vehicle information and driving data in a database. When storing the data, it validates and checks the format, and generates an appropriate error message if there is an error.

[1652] Input: Received vehicle information, driving data

[1653] Output: Vehicle information and driving data stored in the database

[1654] Step 3:

[1655] The server analyzes the stored driving data and generates fuel economy improvement methods using a generative AI model that detects specific patterns and trends and performs data calculations to identify the best improvement methods for the user.

[1656] Input: Driving data

[1657] Output: How to improve fuel economy

[1658] Step 4:

[1659] The generated fuel efficiency improvement methods are then sent to the user's device. The notification content is adjusted according to the user's emotional state detected by an emotion recognition engine. For example, if the user is under high stress, the advice will be softened.

[1660] Input: fuel efficiency improvement methods, emotional information

[1661] Output: Adjusted advice notification

[1662] Step 5:

[1663] The user inputs vehicle inspection and maintenance schedules into the terminal, which are then sent to the server and stored in a database.

[1664] Input: Vehicle inspection, maintenance schedule

[1665] Output: Sending schedule data to the server

[1666] Step 6:

[1667] The server generates reminders at appropriate times and sends them to the user's device. The timing and content of the notifications are adjusted taking into account the user's emotional state.

[1668] Input: Schedule data, emotional information

[1669] Output: Reminder notification

[1670] Step 7:

[1671] The server collects pricing and promotional information from local fuel stations, stores it in a database, and notifies users at optimal times based on their fueling patterns and location.

[1672] Input: fuel price information, campaign information

[1673] Output: Information notification at the optimal time

[1674] Step 8:

[1675] The user's device uses an emotion recognition engine to analyze the user's facial expressions and behavior in real time, and sends the resulting emotional data to the server, which then adjusts the content of notifications and fuel efficiency improvement advice based on this data.

[1676] Input: User emotion data

[1677] Output: Adjusted notification content

[1678] This system allows users to improve fuel efficiency and manage their vehicle in a personalized way, and provides appropriate advice and notifications based on their emotional state.

[1679] 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.

[1680] 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.

[1681] 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.

[1682] 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.

[1683] 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.

[1684] 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.

[1685] 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).

[1686] 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.

[1687] 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."

[1688] 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.

[1689] 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).

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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.

[1696] 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.

[1697] 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.

[1698] 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.

[1699] 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.

[1700] The following is further disclosed regarding the above embodiment.

[1701] (Claim 1)

[1702] A user input means for a user to input vehicle type information and driving data and transmit them to a server;

[1703] A means for storing the vehicle model information and driving data received by the server in a database;

[1704] A generating means for generating a fuel efficiency improvement method by analyzing the driving data by the server;

[1705] The system includes a means for notifying a user of the generated fuel efficiency improvement method.

[1706] (Claim 2)

[1707] The system according to claim 1, further comprising a generating means for generating a comparison report by the server acquiring actual fuel consumption data of other users of the same vehicle model.

[1708] (Claim 3)

[1709] A server collects and stores price information and promotional information from local fuel supply facilities, and generates optimal information based on the user's fueling patterns and location information;

[1710] 10. The system of claim 1, further comprising means for notifying a user of the generated information.

[1711] "Example 1"

[1712] (Claim 1)

[1713] a user input means for a user to input vehicle information and driving data and transmit the data to the server;

[1714] means for storing the vehicle information and driving data received by the server in a database;

[1715] A generating means for generating a fuel efficiency improvement method by using a generating AI model by the server analyzing the driving data;

[1716] a means for notifying a user of the generated fuel efficiency improvement method;

[1717] A means for the server to obtain fuel economy data of other users of the same type and generate a comparison report;

[1718] A means for the server to collect price information and campaign information of fuel supply facilities, generate optimal information based on the user's fuel supply patterns and location information, and notify the user;

[1719] A method for users to input vehicle inspection dates and maintenance dates, and the server stores them and generates and notifies reminders at appropriate times.

[1720] A system including:

[1721] (Claim 2)

[1722] The system according to claim 1, further comprising means for the server to analyze the fuel economy data of other users of the same type of vehicle, compare it with the driving data of the user, and display it in a ranking format.

[1723] (Claim 3)

[1724] The system of claim 1, wherein the server inputs a prompt sentence into a generative AI model to generate fuel efficiency improvement advice.

[1725] "Application Example 1"

[1726] (Claim 1)

[1727] A means for a user to input vehicle model information and driving data and transmit them to a server;

[1728] A means for storing the vehicle model information and driving data received by the server in a database;

[1729] A generating means for generating a fuel efficiency improvement method by analyzing the driving data by the server;

[1730] a means for notifying a user of the generated fuel efficiency improvement method;

[1731] a generating means for the server to collect local fuel price information and campaign information based on the location information and generate optimal information to support the user in economical refueling;

[1732] A system that manages vehicle inspection and maintenance information and includes a generating means for generating reminders at appropriate times.

[1733] (Claim 2)

[1734] The system according to claim 1, further comprising a generating means for generating a comparison report by the server acquiring actual fuel consumption data of other users of the same vehicle model.

[1735] (Claim 3)

[1736] A server collects and stores price information and promotional information from local fuel supply facilities, and generates optimal information based on the user's fueling patterns and location information;

[1737] 10. The system of claim 1, further comprising means for notifying a user of the generated information.

[1738] "Example 2: Combining Emotion Engines"

[1739] (Claim 1)

[1740] A user input means for a user to input vehicle type information and driving data and transmit them to a server;

[1741] A means for storing the vehicle model information and driving data received by the server in a database;

[1742] A generating means for generating a fuel efficiency improvement method by analyzing the driving data by the server;

[1743] A generating means for generating personalized fuel economy improvement advice using a generating AI model by a server;

[1744] The system includes a means for notifying a user of the generated fuel efficiency improvement method.

[1745] (Claim 2)

[1746] The system according to claim 1, further comprising a generating means for generating a comparison report by the server acquiring actual fuel consumption data of other users of the same vehicle model.

[1747] (Claim 3)

[1748] A server collects and stores price information and promotional information from local fuel supply facilities, and generates optimal information based on the user's fueling patterns and location information;

[1749] A means to recognize the user's emotional state through an emotion engine and tailor advice and notifications;

[1750] 10. The system of claim 1, further comprising means for notifying a user of the generated information.

[1751] "Application example 2 when combining emotion engines"

[1752] (Claim 1)

[1753] a user input means for a user to input vehicle information and driving data and transmit the data to a server;

[1754] A means for storing the vehicle information and driving data received by the server in a database;

[1755] A generating means for generating a fuel efficiency improvement method by analyzing the driving data by the server;

[1756] a means for notifying a user of the generated fuel efficiency improvement method;

[1757] A means of detecting the user's emotional state in real time using an emotion recognition engine and using that information to adjust the content of advice;

[1758] A means to manage vehicle inspection and maintenance schedules and generate timely reminders,

[1759] A system that includes a means for collecting pricing and promotional information from local fueling facilities and providing notifications based on a user's fueling patterns and location.

[1760] (Claim 2)

[1761] The system according to claim 1, further comprising a generating means for generating a comparison report by the server acquiring actual fuel consumption data of other users of the same vehicle.

[1762] (Claim 3)

[1763] 10. The system according to claim 1, further comprising means for detecting the emotional state of the user using a head-mounted display or a mobile device and displaying fuel efficiency improvement advice and various notifications. [Explanation of symbols]

[1764] 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 user input means for a user to input vehicle type information and driving data and transmit them to a server; A means for storing the vehicle model information and driving data received by the server in a database; A generating means for generating a fuel efficiency improvement method by analyzing the driving data by the server; The system includes a means for notifying a user of the generated fuel efficiency improvement method.

2. The system according to claim 1, further comprising a generating unit for generating a comparison report by the server acquiring actual fuel consumption data of other users of the same vehicle model.

3. A server collects and stores price information and campaign information from local fuel supply facilities, and generates optimal information based on the user's fuel supply patterns and location information; 10. The system of claim 1, further comprising means for notifying a user of the generated information.

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