System

Abstract

A system is provided.SOLUTION: A system comprising: means for capturing an image of a vehicle by a user and transmitting the image via a communication application; means for identifying a model and a grade of the vehicle using an image recognition algorithm; means for allowing the user to input a model year, a mileage, and a degree of damage after identifying the model and the grade; means for acquiring market price information by referring to a database based on the input information; means for calculating an assessment based on the market price information; and means for notifying the user of the calculated assessment.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern society, assessing the selling price of a vehicle can be a time-consuming process. Conventional bulk appraisal services often involve annoying sales calls, making it difficult for users to use. This places a significant burden on users who are considering selling a car or who want to regularly check its value. To solve this problem, it was necessary to provide a new system that would allow anyone to easily find out the value of their vehicle. [Means for solving the problem]

[0005] The present invention provides a system that allows users to instantly find out the estimated value of a vehicle by taking a photo of the vehicle using a smartphone and sending the image via a communication application. Specifically, the system uses an image recognition algorithm to identify the vehicle's model and grade, and then prompts the user to enter the year, mileage, and degree of damage. The system then references a database to obtain market price information and calculates the estimated value using artificial intelligence. The system also includes a function to notify the calculated estimated value via the communication application, store past appraisal history, and periodically notify the user of the latest estimated value based on market trends. The system also includes a function to provide users with a link to easily apply for the appraisal service. This allows users to quickly and accurately check the value of their vehicle without any hassle.

[0006] "Vehicle" means an automobile, truck, bus, or other motor vehicle.

[0007] "Image recognition algorithm" refers to a computer program that identifies specific features or patterns in an image to identify the make and model of a vehicle.

[0008] "Vehicle type" refers to a classification of a particular make and model vehicle.

[0009] "Grade" refers to different variations in equipment and specifications within a particular vehicle model.

[0010] "Model year" refers to the year the vehicle was manufactured.

[0011] "Distance traveled" refers to the total distance traveled by a vehicle, typically expressed in kilometers.

[0012] "Damage severity" refers to a scale that assesses the level of damage found on the exterior or interior of a vehicle.

[0013] A "database" refers to a collection of information that records past vehicle transaction information and market trends and makes them easily searchable and accessible.

[0014] "Appraised value" refers to the expected price to be paid based on the market value of the vehicle.

[0015] "Communications application" refers to software that enables users to send and receive messages, images, and other data.

[0016] "Artificial intelligence" refers to computer systems that have the ability to process large amounts of data, learn from that data, and make predictions and decisions. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] MODE FOR CARRYING OUT THE INVENTION

[0039] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[0040] System configuration

[0041] The system includes the following main elements:

[0042] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[0043] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[0044] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[0045] 4. Database: A database for storing vehicle market price information and past transaction data.

[0046] 5. Artificial intelligence model: A program for calculating appraisal values.

[0047] Program processing

[0048] User submitted image:

[0049] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., LINE). When the user sends the picture, the image data is stored on the LINE server.

[0050] Image reception and analysis:

[0051] The server receives images sent by users through the LINE API and forwards them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. This information is returned to the user's server as a response.

[0052] User input:

[0053] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[0054] Database lookup and valuation calculation:

[0055] The server retrieves market price information for the vehicle based on the model year, mileage, and damage information received from the user, and inputs the acquired data into an artificial intelligence model to calculate the estimated value.

[0056] Valuation Notification:

[0057] The server generates a message to notify the user of the calculated assessment amount and transmits it via the communication application.

[0058] Store and update appraisal history:

[0059] The server stores the valuation results and user information in a database. It periodically checks market trends and notifies users of the latest valuation amount if there is any price movement.

[0060] Simplified service delivery:

[0061] If the user is satisfied with the valuation amount, they can easily apply for the valuation service by clicking on a link on the server, which will be provided along with the valuation amount notification to the user.

[0062] Specific examples

[0063] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from a database, and an artificial intelligence model calculates an appraisal value. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated regularly, and the user simply clicks a link to complete the appraisal service application.

[0064] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[0068] Step 2:

[0069] The device sends image data to LINE's server based on the user's operation.

[0070] Step 3:

[0071] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[0072] Step 4:

[0073] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[0074] Step 5:

[0075] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[0076] Step 6:

[0077] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[0078] Step 7:

[0079] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[0080] Step 8:

[0081] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[0082] Step 9:

[0083] The database returns historical data and market price information matching the input information to the server.

[0084] Step 10:

[0085] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[0086] Step 11:

[0087] The server (user-side server) prepares a message to notify the user of the assessed value and sends it via a communication application.

[0088] Step 12:

[0089] The user checks the estimated amount through a message sent from the server and applies for the appraisal service if necessary.

[0090] Step 13:

[0091] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[0092] Step 14:

[0093] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[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 appraisal methods require users to physically bring in their vehicles, which takes time and effort. Furthermore, the appraisal process is cumbersome, and obtaining an accurate appraisal value requires the presence of an appraiser with specialized knowledge. This means that the needs of users who want a quick and easy vehicle appraisal cannot be fully met.

[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 means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the type and model of the vehicle using an image recognition program, means for having the user input the year, mileage, and degree of damage after identifying the type and grade, means for referencing an information holder based on the input information and obtaining market valuation information, means for calculating an appraised price based on the market valuation information, and means for notifying the user of the calculated appraised price. This allows a user to easily and quickly check the appraised value of their vehicle using their smartphone.

[0099] "User" refers to the individual or entity that takes and transmits images of a vehicle via a communications application and receives assessment information.

[0100] "Vehicle" refers to any movable vehicle that is subject to appraisal, such as a car, motorcycle, or truck.

[0101] "Communications application" refers to software that runs on a smartphone or other device and allows it to send and receive data or information.

[0102] An "image recognition program" refers to an algorithm or software that identifies and classifies specific objects or features from input image data.

[0103] "Vehicle type" refers to the classification of different models produced by a particular automobile manufacturer.

[0104] "Model" refers to each version or type that is further subdivided within a particular vehicle model.

[0105] An "information holder" refers to a database or storage system that stores information necessary for vehicle appraisal, such as market prices and past transaction data.

[0106] "Market valuation information" refers to vehicle value information calculated based on current market trends and past transaction data.

[0107] "Assessed Value" refers to the current market value of the vehicle as calculated through the appraisal process.

[0108] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[0109] System configuration

[0110] The system includes the following main elements:

[0111] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[0112] 2. Communication applications: Applications for sending and receiving image data (e.g. messaging apps).

[0113] 3. Image recognition program: Software to identify the make and model of a vehicle from images (e.g., TensorFlow, OpenCV).

[0114] 4. Information holder: A database for storing vehicle market price information and past transaction data.

[0115] 5. Generative AI model: A program for calculating the appraisal price (e.g., an artificial intelligence model using TensorFlow).

[0116] Program processing

[0117] User submitted image:

[0118] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., a messaging app). When the user sends the picture, the image data is stored on the application's server.

[0119] Image reception and analysis:

[0120] The server receives the image sent by the user through the messaging app's API and forwards it to an image recognition server, which uses an image recognition program to identify the make and model of the vehicle. This information is returned as a response to the user's server.

[0121] User input:

[0122] Based on the identified make and model, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and extent of damage, and the user replies to the message to enter the year, mileage, and extent of damage information.

[0123] Information holder reference and valuation calculation:

[0124] The server obtains market valuation information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs the obtained data into a generative AI model to calculate the estimated price.

[0125] Estimated price notification:

[0126] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[0127] Store and update appraisal history:

[0128] The server stores the valuation results and user information in an information holder. It periodically checks market trends and notifies users of the latest valuation price if there is a price change.

[0129] Simplified service delivery:

[0130] If the user is satisfied with the estimated price, they can simply apply for the appraisal service by clicking on a link on the server, which will be provided when the user receives the notification of the estimated price.

[0131] Specific examples

[0132] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z model and sends it via a messaging app. The server receives this image and uses an image recognition program to identify the make and model of the vehicle. The server then prompts the user to enter the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor damage). Based on this information, market valuation information is obtained from the information holder, and an appraisal price is calculated using a generative AI model. The calculated appraisal price (e.g., 1 million yen) is notified to the user via the messaging app and saved in the information holder along with previous information. This appraisal price is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[0133] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[0134] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0135] Step 1:

[0136] The user launches the camera app on their smartphone and takes a picture of the vehicle.

[0137] This image is temporarily stored in the device's memory, and the user then opens a communication application (e.g., a messaging app) and prepares this image as a message to send.

[0138] Input: Vehicle image

[0139] Output: Message prepared for sending by the communication application

[0140] Step 2:

[0141] The user takes a picture of the vehicle and sends it to the server via a messaging app.

[0142] The server stores the images received via the API in temporary memory.

[0143] Input: A vehicle image sent by the user

[0144] Output: Vehicle images stored on the server

[0145] Step 3:

[0146] The server transfers the stored vehicle images to an image recognition server.

[0147] The image recognition server launches an image recognition program (e.g., TensorFlow, OpenCV) and identifies the vehicle model and grade from the image.

[0148] Input: Vehicle image

[0149] Output: Identified car model and grade

[0150] Step 4:

[0151] The image recognition server returns the identified vehicle model and grade information to the user's server as a response.

[0152] Based on this information, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage.

[0153] The user replies to the message and enters the year, mileage and extent of damage.

[0154] Input: Identified vehicle model and grade, user input (year, mileage, degree of damage)

[0155] Output: Prompt message sent to the user's smartphone, retrieved vehicle information

[0156] Step 5:

[0157] The server refers to the information holder based on the information received from the user, such as the model year, mileage, and degree of damage.

[0158] The server acquires market evaluation information of the target vehicle from the information holder.

[0159] Input: User input (year, mileage, degree of damage)

[0160] Output: Obtained market valuation information

[0161] Step 6:

[0162] The server inputs the acquired market valuation information into a generative AI model to calculate the appraisal price.

[0163] The generative AI model (e.g., a model using TensorFlow) performs the necessary data processing and calculations based on the input data to calculate the appraisal price.

[0164] Input: Market valuation information

[0165] Output: Calculated valuation price

[0166] Step 7:

[0167] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[0168] Input: Calculated valuation price

[0169] Output: A message to be sent to the user informing them of the quoted price.

[0170] Step 8:

[0171] The server stores the assessment results and the information provided by the user in an information holder.

[0172] The server then periodically checks market trends, updates the valuation price as needed, and notifies the user of the latest valuation price.

[0173] Input: Assessment results, user information, periodic market trend data

[0174] Output: Updated valuation price, notification message to user

[0175] Step 9:

[0176] If the user is satisfied with the assessed price, they can complete the application for the assessment service by clicking on a link on the server.

[0177] This link will be provided within the quote notification message.

[0178] Input: User action (click on link)

[0179] Output: Notification of completion of application for appraisal service

[0180] (Application example 1)

[0181] 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."

[0182] Conventional vehicle appraisal systems require users to complete the required procedures, requiring a great deal of time and effort, making it difficult to quickly obtain an appraisal value. Furthermore, it is not possible to easily obtain the latest appraisal value based on market trends or post a vehicle sales advertisement at a fair price based on the appraisal value. A system that solves these problems and allows users to easily and quickly find out the appraisal value of a vehicle and post a sales advertisement at a fair price is needed.

[0183] 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.

[0184] In this invention, the server

[0185] means for a user to take an image of the vehicle using an image capture device and transmit the image via a communication application;

[0186] means for identifying the make and model of the vehicle using an image recognition algorithm;

[0187] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[0188] a means for referencing a database based on the input information and obtaining market price information;

[0189] A means of calculating the appraisal value based on market price information,

[0190] a means for notifying the user of the calculated valuation;

[0191] A means for automatically displaying an estimated value when a user posts a vehicle for sale advertisement;

[0192] Includes.

[0193] This allows users to easily and quickly find out the estimated value of their vehicle, enabling them to post a vehicle sales ad at a fair price.

[0194] "Photography device" refers to equipment used by a user to take images of a vehicle.

[0195] A "communication application" is software that allows users to send and receive images and data they have taken.

[0196] An "image recognition algorithm" is a program that analyzes vehicle images and identifies the vehicle model and grade.

[0197] "Model year" is information indicating the year the vehicle was manufactured.

[0198] "Distance traveled" is information indicating the total distance traveled by the vehicle so far.

[0199] "Damage level" is information indicating the state of damage related to the appearance and functionality of the vehicle.

[0200] A "database" is a system for storing market price information and data necessary for appraisals and for searching such data.

[0201] "Market Price Information" is data regarding the current market value of a vehicle.

[0202] "Appraised value" is the amount resulting from a monetary evaluation of the vehicle's value.

[0203] "Notification means" is a function for informing the user of the calculated appraisal amount.

[0204] "User" means an individual or corporation that uses the System to appraise vehicles and post sales advertisements.

[0205] "Vehicle sales advertisement" refers to advertisement content posted by a user to sell a vehicle.

[0206] A system embodying this invention includes the following main elements:

[0207] 1. Camera: This refers to the device used by the user to take images of the vehicle. Specifically, this refers to the camera on a smartphone.

[0208] 2. Communication application: Software that allows users to send images they have taken. For example, a general messaging app (e.g., a communication application) can be used as a communication application.

[0209] 3. Image recognition algorithm: A program that analyzes captured images of a vehicle and identifies the vehicle model and grade. An image recognition model using TensorFlow falls into this category.

[0210] 4. Database: A system for storing market price information and data required for appraisal. For example, a MySQL database is used.

[0211] 5. Market Value Information: This is data about the current market value of the vehicle, based on which the valuation is calculated.

[0212] 6. Valuation calculation method: An AI model for calculating valuation based on market price information. An AI model using Keras falls into this category.

[0213] 7. Notification Method: This is the communication method to inform the user of the calculated valuation amount. Notification is sent via the communication application API.

[0214] Next, the specific processing flow of each element will be explained.

[0215] First, the user takes a picture of the vehicle using the smartphone camera and sends it via a communication application. The image is then stored on a server and analyzed by an image recognition algorithm, which identifies the make and model of the vehicle.

[0216] Based on the identified make and model, the server sends a prompt message to the user asking them to enter the year, mileage, and damage level, using the following prompt text:

[0217] “You’ve uploaded a picture of your vehicle, now you need to enter some information.

[0218] Year:

[0219] Mileage (km):

[0220] Damage level (minor / moderate / severe):

[0221] "

[0222] Your estimated price is being calculated...

[0223] The information entered by the user regarding the model year, mileage, and degree of damage is sent to a database by the server, where market price information is referenced, and the AI ​​model then calculates the appraisal value based on that information.

[0224] The calculated valuation value is notified to the user via the communication application's API. For example, a message saying "The valuation value is 1 million yen" is sent. At the same time, a function is provided that automatically displays the valuation value when a user tries to post a vehicle sales ad.

[0225] This system allows users to easily find out the estimated value of their vehicle and post a vehicle sales advertisement at an appropriate price based on the estimated value.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The user takes a picture of the vehicle using the camera on the device (smartphone).

[0229] Input: Image of a vehicle taken using a smartphone camera

[0230] Output: Image file of the photographed vehicle

[0231] Step 2:

[0232] The user uses a communication application on their device (smartphone) to send images of the vehicle they have taken to the server.

[0233] Input: Image file of the vehicle, communication application

[0234] Output: Image files of the vehicle sent to the server through the communication application

[0235] Step 3:

[0236] The server uses the communication application's API to receive vehicle images sent by the user and send them to an image recognition algorithm.

[0237] Input: User-submitted vehicle image file

[0238] Output: Image of the vehicle sent to the image recognition algorithm

[0239] Step 4:

[0240] The server uses an image recognition algorithm (TensorFlow model) to identify the vehicle model and grade.

[0241] Input: Vehicle image received by the server

[0242] Output: Identified vehicle model and grade information

[0243] Step 5:

[0244] Based on the identified information on the vehicle model and grade, the server generates a prompt message prompting the user to input the year, mileage, and degree of damage, and transmits the message to the user via the communication application.

[0245] Input: Identified vehicle model and grade information

[0246] Output: Generated prompt (e.g., "Year: Mileage (km): Damage level (minor / moderate / severe): ")

[0247] Step 6:

[0248] The user follows the prompts on the device (smartphone) to enter the model year, mileage, and degree of damage, and then sends the information to the server.

[0249] Input: Year, mileage, and damage information

[0250] Output: Input information sent to the server through a communication application

[0251] Step 7:

[0252] The server retrieves market price information from a database (MySQL) based on the model year, mileage, and degree of damage information received from the user.

[0253] Input: Year, mileage, and damage information received from the user

[0254] Output: Market price information retrieved from the database

[0255] Step 8:

[0256] The server combines the acquired market price information with the identified vehicle model and grade, and user-entered information, and calculates the appraisal value using an AI model (Keras).

[0257] Input: Market price information, specified vehicle model and grade, user-entered model year, mileage, and damage level

[0258] Output: Calculated valuation amount

[0259] Step 9:

[0260] The server notifies the user of the calculated assessment amount via the communication application.

[0261] Input: Calculated valuation amount

[0262] Output: Valuation notification sent to user (e.g. "The valuation is 1 million yen")

[0263] Step 10:

[0264] When a user posts a vehicle sales advertisement, the server automatically displays the calculated estimated value, allowing the user to post the sales advertisement at a fair price.

[0265] Input: Calculated valuation amount

[0266] Output: Estimated price displayed in vehicle sales advertisement

[0267] 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.

[0268] MODE FOR CARRYING OUT THE INVENTION

[0269] This invention combines a system that allows users to easily check the estimated value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. This system allows users to instantly know the estimated value by taking a picture of the vehicle and sending it via a communication application. It also recognizes the user's emotions and provides feedback and suggestions based on those emotions, making it more friendly and improving the user experience.

[0270] System configuration

[0271] The system includes the following main elements:

[0272] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[0273] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[0274] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[0275] 4. Database: A database for storing vehicle market price information and past transaction data.

[0276] 5. Artificial intelligence model: A program for calculating appraisal values.

[0277] 6. Emotion Engine: A program that recognizes the user's emotions and tailors feedback and suggestions accordingly.

[0278] Program processing

[0279] User submitted image:

[0280] Users take pictures of the vehicle using their smartphone camera. They then send the images using a communication application (e.g., LINE). When users send the images, the image data is stored on the LINE server.

[0281] Image reception and analysis:

[0282] The server receives images sent by users through the LINE API and transfers them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. The identified information is returned to the user's server as a response.

[0283] User input:

[0284] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[0285] Database lookup and valuation calculation:

[0286] The server retrieves market price information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs this data into an artificial intelligence model to calculate the vehicle's estimated value.

[0287] Emotion Recognition and Feedback Regulation:

[0288] The server sends the text messages and voice inputs received from the user to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the emotion and adjusts the next feedback or action accordingly. For example, if the user expresses dissatisfaction, it sends a message offering an explanation or additional support.

[0289] Assessment Notification and History Storage:

[0290] The server prepares a message to notify the user of the calculated valuation amount and sends it via a communication application. It also stores the valuation result and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[0291] Simplified service delivery:

[0292] If the user is satisfied with the estimated price, they can easily apply for the appraisal service by clicking on a link on the server, which will be provided along with the notification of the estimated price.

[0293] Specific examples

[0294] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from the database, and an artificial intelligence model is used to calculate an appraisal value. Furthermore, the system analyzes the user's text and voice input using an emotion engine and sends additional support messages if the user is concerned. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[0295] In this way, the system of the present invention not only allows users to easily and quickly check the estimated value of their vehicle, but also provides appropriate support and feedback from the emotion engine, improving the user experience.

[0296] The processing flow will be explained below.

[0297] Step 1:

[0298] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[0299] Step 2:

[0300] The device sends image data to LINE's server based on the user's operation.

[0301] Step 3:

[0302] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[0303] Step 4:

[0304] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[0305] Step 5:

[0306] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[0307] Step 6:

[0308] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[0309] Step 7:

[0310] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[0311] Step 8:

[0312] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[0313] Step 9:

[0314] The database returns historical data and market price information matching the input information to the server.

[0315] Step 10:

[0316] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[0317] Step 11:

[0318] The server (user-side server) retrieves the user's text message and analyzes it using an emotion engine.

[0319] Step 12:

[0320] The server (emotion engine) identifies the user's emotions from the text message and determines whether there is any anxiety or doubt.

[0321] Step 13:

[0322] The server (user-side server) adjusts the content of the valuation notification based on the emotions identified by the emotion engine, for example adding a detailed explanation or support link if the user is feeling anxious.

[0323] Step 14:

[0324] The server (user-side server) sends the coordinated message to the user via a communication application.

[0325] Step 15:

[0326] The user checks the estimated price via a message sent from the server and provides feedback if necessary.

[0327] Step 16:

[0328] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[0329] Step 17:

[0330] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[0331] Step 18:

[0332] If the user is satisfied with the appraisal amount, they can easily apply for the appraisal service by simply clicking on a link on the server.

[0333] Step 19:

[0334] The server (user-side server) forwards the application information to the assessment service provider, and the assessment process begins.

[0335] Example 2

[0336] 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."

[0337] Conventional vehicle appraisal systems require users to provide images of their vehicle and input the necessary information to calculate the appraisal value, which is a complex and time-consuming process. Furthermore, if the appraisal results do not meet user expectations, dissatisfaction and questions tend to arise, and appropriate feedback is not provided. To address these issues, improvements are needed to enable users to easily and quickly check the appraisal value and increase satisfaction.

[0338] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0339] In this invention, the server includes means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and emotion engine means for recognizing the user's emotions and adjusting or suggesting feedback based on the emotions. This allows the user to not only easily and quickly check the appraisal value of their vehicle, but also receive appropriate feedback based on emotion recognition.

[0340] "User" refers to the person who takes a picture of the vehicle, enters the information, and checks the appraisal value.

[0341] "Vehicle" refers to the means of transportation, such as a car or motorcycle, that is the subject of the appraisal.

[0342] "Image" refers to photographic data taken by a user of a vehicle.

[0343] "Communication application" refers to software that provides messaging services such as LINE and sends and receives data between users and servers.

[0344] "Image recognition algorithm" refers to a program for identifying the vehicle model and grade from a photographed image.

[0345] "Identify" refers to identifying and clarifying the vehicle model and grade from an image of the vehicle.

[0346] "Model year" refers to the year the vehicle was manufactured.

[0347] "Distance traveled" refers to the total distance traveled by the vehicle.

[0348] "Extent of damage" refers to the state of damage to the exterior of the vehicle.

[0349] "Database" refers to a collection of information that stores vehicle market price information and past transaction data.

[0350] "Referring" refers to searching for information in a database and obtaining the required data.

[0351] "Market Price Information" refers to data regarding the value of a vehicle in the current market.

[0352] "Appraised value" refers to the assessed amount calculated based on the market value of the vehicle.

[0353] "Notify" refers to informing the user of information such as the appraisal value.

[0354] An "emotion engine" is a program that recognizes a user's emotions and adjusts feedback and suggestions based on them.

[0355] This invention is a system that allows users to instantly check the estimated value of a vehicle by taking a picture of the vehicle using a smartphone camera and sending it via a communication application (e.g., LINE).The system also incorporates an emotion engine that recognizes the user's emotions, and can improve the user experience by providing feedback and suggestions according to the user's emotions.

[0356] When a user takes a picture of a vehicle with their smartphone and sends it via LINE or other services, the image data is stored on LINE's server. The server receives the image using LINE's API and forwards it to an image recognition server. The image recognition server uses an image recognition algorithm (e.g., YOLO, ResNet) to identify the vehicle's model and grade. This identified information is sent back to the server, which then proceeds to the next step of processing.

[0357] The server then uses the identified vehicle model and grade information to send a message prompting the user to enter the year, mileage, and degree of damage. The user enters this information and replies. Using this information, the server references a database to obtain market price information. This information is then fed into an artificial intelligence model (e.g., XGBoost, TensorFlow-based model) to calculate the vehicle's estimated value.

[0358] The server also analyzes the text and voice messages entered by the user using an emotion engine (e.g., IBM Watson Tone Analyzer) and provides appropriate feedback and suggestions based on the analysis results. For example, if the user is feeling anxious, the server may send an additional support message to pique the user's interest.

[0359] Once this process is complete, the server generates a message informing the user of the calculated valuation amount and sends it via LINE. The system also stores the valuation results and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[0360] Furthermore, the message also includes a link that allows users who are satisfied with the appraisal price to easily apply for the appraisal service. Simply clicking on this link will complete the application process.

[0361] As a concrete example, consider the case where a user takes a picture of a 2018 model vehicle and sends it via LINE. In this case, the server receives the image and uses an image recognition algorithm to identify the model and grade. The server then sends a message prompting the user to enter the year, mileage, and degree of damage. For example, prompts such as "Please tell us the year of your vehicle," "Please enter the mileage," and "Please tell us the degree of damage" are sent. Once the user provides this information, the server references the database and calculates the appraisal value using an artificial intelligence model. If the user feels anxious or dissatisfied, the emotion engine provides appropriate support.

[0362] Such a system not only allows users to easily and quickly check the estimated value of their vehicle, but also improves the user experience by providing feedback based on emotion recognition.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1:

[0365] The user takes an image of the vehicle using a smartphone. The user opens a camera app and takes images of the vehicle from multiple angles. The input of this action is the smartphone camera, and the output is the image data of the captured vehicle.

[0366] Step 2:

[0367] The user takes a photo and sends it using a communication application (e.g., LINE). The user opens the LINE app and sends the image to a dedicated contact. The input to this process is the captured image data, and the output is the image data stored on the LINE server.

[0368] Step 3:

[0369] The server receives images sent by users via the LINE API. The server downloads the image data and transfers it to the image recognition server. The input to this process is the image data stored on the LINE server, and the output is the image data sent to the image recognition server.

[0370] Step 4:

[0371] The image recognition server uses an image recognition algorithm to identify the vehicle model and grade. The image recognition algorithm (e.g., YOLO, ResNet) analyzes the image data and identifies the vehicle model (e.g., Toyota Prius) and grade (e.g., Z grade). The input to this process is the image data sent to the image recognition server, and the output is the identified vehicle model and grade information.

[0372] Step 5:

[0373] Based on the identified information on the car model and grade, the server sends a message to the user to prompt them to enter the year, mileage, and degree of damage. For example, it sends messages such as "Please tell us the year of your car," "Please enter the mileage," and "Please tell us the degree of damage." The input of this process is the identified information on the car model and grade, and the output is a prompt message for the user.

[0374] Step 6:

[0375] The user replies to a message from the server and inputs information about the model year, mileage, and degree of damage. The user sends a response in text format. The input of this process is the text data returned by the user, and the output is the model year, mileage, and degree of damage information.

[0376] Step 7:

[0377] The server queries the database based on the information received from the user. The database is searched to obtain market price information. The input to this process is model year, mileage, and damage level information, and the output is market price data.

[0378] Step 8:

[0379] The server inputs the acquired market price data into an AI model to calculate the vehicle's valuation. The AI ​​model (e.g., XGBoost or TensorFlow-based model) analyzes the data and calculates the valuation. The input of this process is market price data, and the output is the valuation.

[0380] Step 9:

[0381] The server sends the text or voice message received from the user to the emotion engine to analyze the user's emotion. The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the text or voice data and identifies the user's emotion. The input of this process is the text or voice message from the user, and the output is the analyzed emotion data.

[0382] Step 10:

[0383] The server adjusts the feedback and suggestions based on the analysis results and sends a feedback message to the user. For example, if the user expresses dissatisfaction, it sends an additional support message. The input of this process is the analyzed emotion data, and the output is a feedback message.

[0384] Step 11:

[0385] The server prepares a message to notify the user of the calculated valuation amount and sends it via LINE. The notification message includes the valuation amount and a link to apply for the valuation service. The input for this process is the valuation amount, and the output is the notification message.

[0386] Step 12:

[0387] We provide a system in which the server saves the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount. The input of this process is the appraisal results and user information, and the output is the data saved in the database.

[0388] Step 13:

[0389] If the user is satisfied with the valuation amount, they can simply click on the link to apply for the valuation service. Once the user clicks on the link, the application for the valuation service is completed. The input of this process is the user's click action, and the output is the completed application for the valuation service.

[0390] (Application example 2)

[0391] 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."

[0392] While existing vehicle appraisal systems allow users to easily and quickly check the appraisal value, they lack feedback and suggestions that take into account the user's emotional state, which hinders the user experience.Furthermore, food delivery services that use food images lack a means to make suggestions that reflect the user's emotions, which limits the comprehensiveness of their services.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0394] In this invention, the server includes means for a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and means for recognizing the user's emotions and providing further feedback or suggestions based on the emotions. This not only allows the user to easily and quickly check the appraisal value, but also allows them to receive appropriate feedback or suggestions according to their emotions, significantly improving the user experience.

[0395] "Vehicle image" is visual data showing the exterior of a vehicle, taken by a user using a smartphone camera.

[0396] A "communication application" is a platform for users to send and receive images they have taken, including, for example, messaging apps and social media apps.

[0397] An "image recognition algorithm" is a calculation procedure for automatically extracting specific vehicle information (model and grade) from input image data.

[0398] "Vehicle model and grade" is classification information that indicates the vehicle category and detailed specifications.

[0399] "Model year" refers to the year in which the vehicle was manufactured.

[0400] "Distance traveled" refers to the total distance traveled by the vehicle, as measured by the odometer.

[0401] "Damage level" is information indicating the level of damage to the exterior of the vehicle.

[0402] A "database" is a collection of information for managing data such as vehicle market price information and past appraisal history in a certain format.

[0403] "Market price information" refers to market data relating to vehicle transaction prices, values, etc.

[0404] "Appraisal Value" is the estimated value of the vehicle calculated based on image recognition algorithms and databases.

[0405] "Emotion recognition" refers to analyzing the emotional state from text or voice input by the user.

[0406] "Feedback" refers to providing answers or suggestions based on the user's emotional state.

[0407] "Suggestions" are recommendations based on the user's input and emotional state to provide optimal actions or support.

[0408] The present invention combines a system that allows users to easily check the appraisal value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. The specific system configuration and processing procedure for implementing this invention are described below.

[0409] System configuration

[0410] The invention mainly comprises the following elements:

[0411] 1. Smartphone: A device that allows users to take pictures of vehicles and food and provide information through text or voice input.

[0412] 2. Communication applications: These are applications that allow users to send and receive images and messages using platforms such as LINE.

[0413] 3. Image recognition algorithm: This algorithm is used to identify the make and model of a vehicle or food from a captured image. It uses existing image recognition services such as AWS Rekognition.

[0414] 4. Database: A database such as MongoDB or Firebase for storing and managing vehicle market price information, past appraisal history, and menu information.

[0415] 5. Artificial intelligence model: A program that calculates appropriate proposals and valuations based on vehicle and food information. The model is built using TensorFlow and other tools.

[0416] 6. Emotion Engine: A program that analyzes the user's emotions from text and voice and provides feedback and suggestions based on that state. Emotion analysis is performed using IBM Watson and Google Cloud Natural Language API.

[0417] Explanation of program processing

[0418] User submitted image:

[0419] Users can take pictures of vehicles or food using their smartphone camera and send them to a server via a communication application such as LINE, with the option to enter additional information via text or voice.

[0420] Image reception and analysis:

[0421] The server receives images sent by users through LINE's API and passes them to an image recognition algorithm, which then identifies the vehicle model and grade, or food.

[0422] User input:

[0423] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional information (year, mileage, degree of damage, etc.) The user provides the additional information by replying to this message.

[0424] Database lookup and valuation calculation:

[0425] Based on the information provided, the server references a database to obtain market price information and past appraisal history for the vehicle, and inputs this data into an AI model to calculate the vehicle's appraisal value and food recommendations.

[0426] Emotion Recognition and Feedback Regulation:

[0427] Text and voice messages from users are sent to the emotion engine, which analyzes their emotions and generates appropriate feedback and suggestions based on the analysis results.

[0428] Notifications and History Retention:

[0429] The calculated valuation amount and proposal details are notified to the user via LINE etc., and this information is simultaneously saved in a database. If the user is satisfied with the valuation, they are also provided with a link to easily apply for the service.

[0430] Specific examples

[0431] For example, consider the case where a user opens the "Emotional Food Order" app by typing "I feel a bit tired today." The emotion engine recognizes this "feeling of fatigue," and the AI ​​model suggests a menu with a relaxing effect (chicken salad and herbal tea). Based on this information, the app notifies the user, "You seem tired today. How about a relaxing chicken salad and herbal tea?"

[0432] Example prompt for a generative AI model:

[0433] User: Opens the food delivery app "Emotional Food Order" on his smartphone and types, "I feel kind of tired today."

[0434] Emotion engine: Recognizing "fatigue" from text.

[0435] AI model: Suggests chicken salad and herbal tea as a menu that is good for recovering from fatigue.

[0436] App: "You seem tired today. Would you like a relaxing chicken salad and herbal tea?"

[0437] This allows users to easily receive suggestions that correspond to their emotions, improving their service experience.

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] Users use their smartphones to take pictures of vehicles or food and send the images to a server via a communication application such as LINE.

[0441] Input: A user-taken image of a vehicle or food.

[0442] Output: Image data transferred to the server via LINE's API.

[0443] Step 2:

[0444] The server receives image data sent by the user through LINE's API and transfers the image to an image recognition algorithm.

[0445] Input: Image data received by the server.

[0446] Output: The image sent to the image recognition algorithm.

[0447] Step 3:

[0448] Using image recognition algorithms, the server identifies the make and model of the vehicle or the food item.

[0449] Input: The image sent to the image recognition algorithm.

[0450] Output: Identified car model and model, or food information.

[0451] Step 4:

[0452] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional required information (such as model year, mileage, and degree of damage).

[0453] Input: Identified car model and model, or food information.

[0454] Output: A message sent to your smartphone prompting you to enter additional information.

[0455] Step 5:

[0456] The user enters additional information (such as model year, mileage, and degree of damage) on their smartphone and sends this information to the server.

[0457] Input: Additional information entered by the user.

[0458] Output: Additional information sent to the server.

[0459] Step 6:

[0460] The server refers to the database based on the additional information sent by the user and obtains the relevant market price information and past appraisal history.

[0461] Input: User additional information and market price information in the database.

[0462] Output: Obtained market price information and past appraisal history.

[0463] Step 7:

[0464] The server inputs the acquired data into an artificial intelligence model to calculate a vehicle valuation or food recommendations.

[0465] Inputs: Market price information, past appraisal history, artificial intelligence model.

[0466] Output: Calculated valuation or proposal.

[0467] Step 8:

[0468] Text and voice from the user are sent to the emotion engine, and the server analyzes the user's emotions.

[0469] Input: text and voice data, emotion engine.

[0470] Output: Parsed emotional state.

[0471] Step 9:

[0472] Based on the analysis results of the emotion engine, the server generates appropriate feedback and suggestions and notifies the user via LINE or other means.

[0473] Input: Parsed emotional state, template for feedback and suggestions.

[0474] Output: Feedback and suggestions that are communicated to the user.

[0475] Step 10:

[0476] When the user checks the notified estimated price and proposal content and applies for the service, the server provides a link for easily applying for the service.

[0477] Input: What to notify the user.

[0478] Output: A link to apply for the service.

[0479] 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.

[0480] 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.

[0481] 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.

[0482] [Second embodiment]

[0483] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0484] 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.

[0485] 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).

[0486] 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.

[0487] 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.

[0488] 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).

[0489] 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.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] In the smart glasses 214, 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.

[0494] 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."

[0495] MODE FOR CARRYING OUT THE INVENTION

[0496] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[0497] System configuration

[0498] The system includes the following main elements:

[0499] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[0500] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[0501] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[0502] 4. Database: A database for storing vehicle market price information and past transaction data.

[0503] 5. Artificial intelligence model: A program for calculating appraisal values.

[0504] Program processing

[0505] User submitted image:

[0506] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., LINE). When the user sends the picture, the image data is stored on the LINE server.

[0507] Image reception and analysis:

[0508] The server receives images sent by users through the LINE API and forwards them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. This information is returned to the user's server as a response.

[0509] User input:

[0510] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[0511] Database lookup and valuation calculation:

[0512] The server retrieves market price information for the vehicle based on the model year, mileage, and damage information received from the user, and inputs the acquired data into an artificial intelligence model to calculate the estimated value.

[0513] Valuation Notification:

[0514] The server generates a message to notify the user of the calculated assessment amount and transmits it via the communication application.

[0515] Store and update appraisal history:

[0516] The server stores the valuation results and user information in a database. It periodically checks market trends and notifies users of the latest valuation amount if there is any price movement.

[0517] Simplified service delivery:

[0518] If the user is satisfied with the valuation amount, they can easily apply for the valuation service by clicking on a link on the server, which will be provided along with the valuation amount notification to the user.

[0519] Specific examples

[0520] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from a database, and an artificial intelligence model calculates an appraisal value. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated regularly, and the user simply clicks a link to complete the appraisal service application.

[0521] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[0522] The processing flow will be explained below.

[0523] Step 1:

[0524] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[0525] Step 2:

[0526] The device sends image data to LINE's server based on the user's operation.

[0527] Step 3:

[0528] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[0529] Step 4:

[0530] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[0531] Step 5:

[0532] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[0533] Step 6:

[0534] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[0535] Step 7:

[0536] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[0537] Step 8:

[0538] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[0539] Step 9:

[0540] The database returns historical data and market price information matching the input information to the server.

[0541] Step 10:

[0542] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[0543] Step 11:

[0544] The server (user-side server) prepares a message to notify the user of the assessed value and sends it via a communication application.

[0545] Step 12:

[0546] The user checks the estimated amount through a message sent from the server and applies for the appraisal service if necessary.

[0547] Step 13:

[0548] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[0549] Step 14:

[0550] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[0551] Example 1

[0552] 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."

[0553] Conventional vehicle appraisal methods require users to physically bring in their vehicles, which takes time and effort. Furthermore, the appraisal process is cumbersome, and obtaining an accurate appraisal value requires the presence of an appraiser with specialized knowledge. This means that the needs of users who want a quick and easy vehicle appraisal cannot be fully met.

[0554] 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.

[0555] In this invention, the server includes means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the type and model of the vehicle using an image recognition program, means for having the user input the year, mileage, and degree of damage after identifying the type and grade, means for referencing an information holder based on the input information and obtaining market valuation information, means for calculating an appraised price based on the market valuation information, and means for notifying the user of the calculated appraised price. This allows a user to easily and quickly check the appraised value of their vehicle using their smartphone.

[0556] "User" refers to the individual or entity that takes and transmits images of a vehicle via a communications application and receives assessment information.

[0557] "Vehicle" refers to any movable vehicle that is subject to appraisal, such as a car, motorcycle, or truck.

[0558] "Communications application" refers to software that runs on a smartphone or other device and allows it to send and receive data or information.

[0559] An "image recognition program" refers to an algorithm or software that identifies and classifies specific objects or features from input image data.

[0560] "Vehicle type" refers to the classification of different models produced by a particular automobile manufacturer.

[0561] "Model" refers to each version or type that is further subdivided within a particular vehicle model.

[0562] An "information holder" refers to a database or storage system that stores information necessary for vehicle appraisal, such as market prices and past transaction data.

[0563] "Market valuation information" refers to vehicle value information calculated based on current market trends and past transaction data.

[0564] "Assessed Value" refers to the current market value of the vehicle as calculated through the appraisal process.

[0565] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[0566] System configuration

[0567] The system includes the following main elements:

[0568] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[0569] 2. Communication applications: Applications for sending and receiving image data (e.g. messaging apps).

[0570] 3. Image recognition program: Software to identify the make and model of a vehicle from images (e.g., TensorFlow, OpenCV).

[0571] 4. Information holder: A database for storing vehicle market price information and past transaction data.

[0572] 5. Generative AI model: A program for calculating the appraisal price (e.g., an artificial intelligence model using TensorFlow).

[0573] Program processing

[0574] User submitted image:

[0575] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., a messaging app). When the user sends the picture, the image data is stored on the application's server.

[0576] Image reception and analysis:

[0577] The server receives the image sent by the user through the messaging app's API and forwards it to an image recognition server, which uses an image recognition program to identify the make and model of the vehicle. This information is returned as a response to the user's server.

[0578] User input:

[0579] Based on the identified make and model, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and extent of damage, and the user replies to the message to enter the year, mileage, and extent of damage information.

[0580] Information holder reference and valuation calculation:

[0581] The server obtains market valuation information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs the obtained data into a generative AI model to calculate the estimated price.

[0582] Estimated price notification:

[0583] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[0584] Store and update appraisal history:

[0585] The server stores the valuation results and user information in an information holder. It periodically checks market trends and notifies users of the latest valuation price if there is a price change.

[0586] Simplified service delivery:

[0587] If the user is satisfied with the estimated price, they can simply apply for the appraisal service by clicking on a link on the server, which will be provided when the user receives the notification of the estimated price.

[0588] Specific examples

[0589] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z model and sends it via a messaging app. The server receives this image and uses an image recognition program to identify the make and model of the vehicle. The server then prompts the user to enter the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor damage). Based on this information, market valuation information is obtained from the information holder, and an appraisal price is calculated using a generative AI model. The calculated appraisal price (e.g., 1 million yen) is notified to the user via the messaging app and saved in the information holder along with previous information. This appraisal price is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[0590] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[0591] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0592] Step 1:

[0593] The user launches the camera app on their smartphone and takes a picture of the vehicle.

[0594] This image is temporarily stored in the device's memory, and the user then opens a communication application (e.g., a messaging app) and prepares this image as a message to send.

[0595] Input: Vehicle image

[0596] Output: Message prepared for sending by the communication application

[0597] Step 2:

[0598] The user takes a picture of the vehicle and sends it to the server via a messaging app.

[0599] The server stores the images received via the API in temporary memory.

[0600] Input: A vehicle image sent by the user

[0601] Output: Vehicle images stored on the server

[0602] Step 3:

[0603] The server transfers the stored vehicle images to an image recognition server.

[0604] The image recognition server launches an image recognition program (e.g., TensorFlow, OpenCV) and identifies the vehicle model and grade from the image.

[0605] Input: Vehicle image

[0606] Output: Identified car model and grade

[0607] Step 4:

[0608] The image recognition server returns the identified vehicle model and grade information to the user's server as a response.

[0609] Based on this information, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage.

[0610] The user replies to the message and enters the year, mileage and extent of damage.

[0611] Input: Identified vehicle model and grade, user input (year, mileage, degree of damage)

[0612] Output: Prompt message sent to the user's smartphone, retrieved vehicle information

[0613] Step 5:

[0614] The server refers to the information holder based on the information received from the user, such as the model year, mileage, and degree of damage.

[0615] The server acquires market evaluation information of the target vehicle from the information holder.

[0616] Input: User input (year, mileage, degree of damage)

[0617] Output: Obtained market valuation information

[0618] Step 6:

[0619] The server inputs the acquired market valuation information into a generative AI model to calculate the appraisal price.

[0620] The generative AI model (e.g., a model using TensorFlow) performs the necessary data processing and calculations based on the input data to calculate the appraisal price.

[0621] Input: Market valuation information

[0622] Output: Calculated valuation price

[0623] Step 7:

[0624] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[0625] Input: Calculated valuation price

[0626] Output: A message to be sent to the user informing them of the quoted price.

[0627] Step 8:

[0628] The server stores the assessment results and the information provided by the user in an information holder.

[0629] The server then periodically checks market trends, updates the valuation price as needed, and notifies the user of the latest valuation price.

[0630] Input: Assessment results, user information, periodic market trend data

[0631] Output: Updated valuation price, notification message to user

[0632] Step 9:

[0633] If the user is satisfied with the assessed price, they can complete the application for the assessment service by clicking on a link on the server.

[0634] This link will be provided within the quote notification message.

[0635] Input: User action (click on link)

[0636] Output: Notification of completion of application for appraisal service

[0637] (Application example 1)

[0638] 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."

[0639] Conventional vehicle appraisal systems require users to complete the required procedures, requiring a great deal of time and effort, making it difficult to quickly obtain an appraisal value. Furthermore, it is not possible to easily obtain the latest appraisal value based on market trends or post a vehicle sales advertisement at a fair price based on the appraisal value. A system that solves these problems and allows users to easily and quickly find out the appraisal value of a vehicle and post a sales advertisement at a fair price is needed.

[0640] 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.

[0641] In this invention, the server

[0642] means for a user to take an image of the vehicle using an image capture device and transmit the image via a communication application;

[0643] means for identifying the make and model of the vehicle using an image recognition algorithm;

[0644] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[0645] a means for referencing a database based on the input information and obtaining market price information;

[0646] A means of calculating the appraisal value based on market price information,

[0647] a means for notifying the user of the calculated valuation;

[0648] A means for automatically displaying an estimated value when a user posts a vehicle for sale advertisement;

[0649] Includes.

[0650] This allows users to easily and quickly find out the estimated value of their vehicle, enabling them to post a vehicle sales ad at a fair price.

[0651] "Photography device" refers to equipment used by a user to take images of a vehicle.

[0652] A "communication application" is software that allows users to send and receive images and data they have taken.

[0653] An "image recognition algorithm" is a program that analyzes vehicle images and identifies the vehicle model and grade.

[0654] "Model year" is information indicating the year the vehicle was manufactured.

[0655] "Distance traveled" is information indicating the total distance traveled by the vehicle so far.

[0656] "Damage level" is information indicating the state of damage related to the appearance and functionality of the vehicle.

[0657] A "database" is a system for storing market price information and data necessary for appraisals and for searching such data.

[0658] "Market Price Information" is data regarding the current market value of a vehicle.

[0659] "Appraised value" is the amount resulting from a monetary evaluation of the vehicle's value.

[0660] "Notification means" is a function for informing the user of the calculated appraisal amount.

[0661] "User" means an individual or corporation that uses the System to appraise vehicles and post sales advertisements.

[0662] "Vehicle sales advertisement" refers to advertisement content posted by a user to sell a vehicle.

[0663] A system embodying this invention includes the following main elements:

[0664] 1. Camera: This refers to the device used by the user to take images of the vehicle. Specifically, this refers to the camera on a smartphone.

[0665] 2. Communication application: Software that allows users to send images they have taken. For example, a general messaging app (e.g., a communication application) can be used as a communication application.

[0666] 3. Image recognition algorithm: A program that analyzes captured images of a vehicle and identifies the vehicle model and grade. An image recognition model using TensorFlow falls into this category.

[0667] 4. Database: A system for storing market price information and data required for appraisal. For example, a MySQL database is used.

[0668] 5. Market Value Information: This is data about the current market value of the vehicle, based on which the valuation is calculated.

[0669] 6. Valuation calculation method: An AI model for calculating valuation based on market price information. An AI model using Keras falls into this category.

[0670] 7. Notification Method: This is the communication method to inform the user of the calculated valuation amount. Notification is sent via the communication application API.

[0671] Next, the specific processing flow of each element will be explained.

[0672] First, the user takes a picture of the vehicle using the smartphone camera and sends it via a communication application. The image is then stored on a server and analyzed by an image recognition algorithm, which identifies the make and model of the vehicle.

[0673] Based on the identified make and model, the server sends a prompt message to the user asking them to enter the year, mileage, and damage level, using the following prompt text:

[0674] “You’ve uploaded a picture of your vehicle, now you need to enter some information.

[0675] Year:

[0676] Mileage (km):

[0677] Damage level (minor / moderate / severe):

[0678] "

[0679] Your estimated price is being calculated...

[0680] The information entered by the user regarding the model year, mileage, and degree of damage is sent to a database by the server, where market price information is referenced, and the AI ​​model then calculates the appraisal value based on that information.

[0681] The calculated valuation value is notified to the user via the communication application's API. For example, a message saying "The valuation value is 1 million yen" is sent. At the same time, a function is provided that automatically displays the valuation value when a user tries to post a vehicle sales ad.

[0682] This system allows users to easily find out the estimated value of their vehicle and post a vehicle sales advertisement at an appropriate price based on the estimated value.

[0683] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0684] Step 1:

[0685] The user takes a picture of the vehicle using the camera on the device (smartphone).

[0686] Input: Image of a vehicle taken using a smartphone camera

[0687] Output: Image file of the photographed vehicle

[0688] Step 2:

[0689] The user uses a communication application on their device (smartphone) to send images of the vehicle they have taken to the server.

[0690] Input: Image file of the vehicle, communication application

[0691] Output: Image files of the vehicle sent to the server through the communication application

[0692] Step 3:

[0693] The server uses the communication application's API to receive vehicle images sent by the user and send them to an image recognition algorithm.

[0694] Input: User-submitted vehicle image file

[0695] Output: Image of the vehicle sent to the image recognition algorithm

[0696] Step 4:

[0697] The server uses an image recognition algorithm (TensorFlow model) to identify the vehicle model and grade.

[0698] Input: Vehicle image received by the server

[0699] Output: Identified vehicle model and grade information

[0700] Step 5:

[0701] Based on the identified information on the vehicle model and grade, the server generates a prompt message prompting the user to input the year, mileage, and degree of damage, and transmits the message to the user via the communication application.

[0702] Input: Identified vehicle model and grade information

[0703] Output: Generated prompt (e.g., "Year: Mileage (km): Damage level (minor / moderate / severe): ")

[0704] Step 6:

[0705] The user follows the prompts on the device (smartphone) to enter the model year, mileage, and degree of damage, and then sends the information to the server.

[0706] Input: Year, mileage, and damage information

[0707] Output: Input information sent to the server through a communication application

[0708] Step 7:

[0709] The server retrieves market price information from a database (MySQL) based on the model year, mileage, and degree of damage information received from the user.

[0710] Input: Year, mileage, and damage information received from the user

[0711] Output: Market price information retrieved from the database

[0712] Step 8:

[0713] The server combines the acquired market price information with the identified vehicle model and grade, and user-entered information, and calculates the appraisal value using an AI model (Keras).

[0714] Input: Market price information, specified vehicle model and grade, user-entered model year, mileage, and damage level

[0715] Output: Calculated valuation amount

[0716] Step 9:

[0717] The server notifies the user of the calculated assessment amount via the communication application.

[0718] Input: Calculated valuation amount

[0719] Output: Valuation notification sent to user (e.g. "The valuation is 1 million yen")

[0720] Step 10:

[0721] When a user posts a vehicle sales advertisement, the server automatically displays the calculated estimated value, allowing the user to post the sales advertisement at a fair price.

[0722] Input: Calculated valuation amount

[0723] Output: Estimated price displayed in vehicle sales advertisement

[0724] 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.

[0725] MODE FOR CARRYING OUT THE INVENTION

[0726] This invention combines a system that allows users to easily check the estimated value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. This system allows users to instantly know the estimated value by taking a picture of the vehicle and sending it via a communication application. It also recognizes the user's emotions and provides feedback and suggestions based on those emotions, making it more friendly and improving the user experience.

[0727] System configuration

[0728] The system includes the following main elements:

[0729] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[0730] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[0731] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[0732] 4. Database: A database for storing vehicle market price information and past transaction data.

[0733] 5. Artificial intelligence model: A program for calculating appraisal values.

[0734] 6. Emotion Engine: A program that recognizes the user's emotions and tailors feedback and suggestions accordingly.

[0735] Program processing

[0736] User submitted image:

[0737] Users take pictures of the vehicle using their smartphone camera. They then send the images using a communication application (e.g., LINE). When users send the images, the image data is stored on the LINE server.

[0738] Image reception and analysis:

[0739] The server receives images sent by users through the LINE API and transfers them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. The identified information is returned to the user's server as a response.

[0740] User input:

[0741] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[0742] Database lookup and valuation calculation:

[0743] The server retrieves market price information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs this data into an artificial intelligence model to calculate the vehicle's estimated value.

[0744] Emotion Recognition and Feedback Regulation:

[0745] The server sends the text messages and voice inputs received from the user to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the emotion and adjusts the next feedback or action accordingly. For example, if the user expresses dissatisfaction, it sends a message offering an explanation or additional support.

[0746] Assessment Notification and History Storage:

[0747] The server prepares a message to notify the user of the calculated valuation amount and sends it via a communication application. It also stores the valuation result and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[0748] Simplified service delivery:

[0749] If the user is satisfied with the estimated price, they can easily apply for the appraisal service by clicking on a link on the server, which will be provided along with the notification of the estimated price.

[0750] Specific examples

[0751] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from the database, and an artificial intelligence model is used to calculate an appraisal value. Furthermore, the system analyzes the user's text and voice input using an emotion engine and sends additional support messages if the user is concerned. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[0752] In this way, the system of the present invention not only allows users to easily and quickly check the estimated value of their vehicle, but also provides appropriate support and feedback from the emotion engine, improving the user experience.

[0753] The processing flow will be explained below.

[0754] Step 1:

[0755] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[0756] Step 2:

[0757] The device sends image data to LINE's server based on the user's operation.

[0758] Step 3:

[0759] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[0760] Step 4:

[0761] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[0762] Step 5:

[0763] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[0764] Step 6:

[0765] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[0766] Step 7:

[0767] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[0768] Step 8:

[0769] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[0770] Step 9:

[0771] The database returns historical data and market price information matching the input information to the server.

[0772] Step 10:

[0773] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[0774] Step 11:

[0775] The server (user-side server) retrieves the user's text message and analyzes it using an emotion engine.

[0776] Step 12:

[0777] The server (emotion engine) identifies the user's emotions from the text message and determines whether there is any anxiety or doubt.

[0778] Step 13:

[0779] The server (user-side server) adjusts the content of the valuation notification based on the emotions identified by the emotion engine, for example adding a detailed explanation or support link if the user is feeling anxious.

[0780] Step 14:

[0781] The server (user-side server) sends the coordinated message to the user via a communication application.

[0782] Step 15:

[0783] The user checks the estimated price via a message sent from the server and provides feedback if necessary.

[0784] Step 16:

[0785] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[0786] Step 17:

[0787] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[0788] Step 18:

[0789] If the user is satisfied with the appraisal amount, they can easily apply for the appraisal service by simply clicking on a link on the server.

[0790] Step 19:

[0791] The server (user-side server) forwards the application information to the assessment service provider, and the assessment process begins.

[0792] Example 2

[0793] 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."

[0794] Conventional vehicle appraisal systems require users to provide images of their vehicle and input the necessary information to calculate the appraisal value, which is a complex and time-consuming process. Furthermore, if the appraisal results do not meet user expectations, dissatisfaction and questions tend to arise, and appropriate feedback is not provided. To address these issues, improvements are needed to enable users to easily and quickly check the appraisal value and increase satisfaction.

[0795] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0796] In this invention, the server includes means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and emotion engine means for recognizing the user's emotions and adjusting or suggesting feedback based on the emotions. This allows the user to not only easily and quickly check the appraisal value of their vehicle, but also receive appropriate feedback based on emotion recognition.

[0797] "User" refers to the person who takes a picture of the vehicle, enters the information, and checks the appraisal value.

[0798] "Vehicle" refers to the means of transportation, such as a car or motorcycle, that is the subject of the appraisal.

[0799] "Image" refers to photographic data taken by a user of a vehicle.

[0800] "Communication application" refers to software that provides messaging services such as LINE and sends and receives data between users and servers.

[0801] "Image recognition algorithm" refers to a program for identifying the vehicle model and grade from a photographed image.

[0802] "Identify" refers to identifying and clarifying the vehicle model and grade from an image of the vehicle.

[0803] "Model year" refers to the year the vehicle was manufactured.

[0804] "Distance traveled" refers to the total distance traveled by the vehicle.

[0805] "Extent of damage" refers to the state of damage to the exterior of the vehicle.

[0806] "Database" refers to a collection of information that stores vehicle market price information and past transaction data.

[0807] "Referring" refers to searching for information in a database and obtaining the required data.

[0808] "Market Price Information" refers to data regarding the value of a vehicle in the current market.

[0809] "Appraised value" refers to the assessed amount calculated based on the market value of the vehicle.

[0810] "Notify" refers to informing the user of information such as the appraisal value.

[0811] An "emotion engine" is a program that recognizes a user's emotions and adjusts feedback and suggestions based on them.

[0812] This invention is a system that allows users to instantly check the estimated value of a vehicle by taking a picture of the vehicle using a smartphone camera and sending it via a communication application (e.g., LINE).The system also incorporates an emotion engine that recognizes the user's emotions, and can improve the user experience by providing feedback and suggestions according to the user's emotions.

[0813] When a user takes a picture of a vehicle with their smartphone and sends it via LINE or other services, the image data is stored on LINE's server. The server receives the image using LINE's API and forwards it to an image recognition server. The image recognition server uses an image recognition algorithm (e.g., YOLO, ResNet) to identify the vehicle's model and grade. This identified information is sent back to the server, which then proceeds to the next step of processing.

[0814] The server then uses the identified vehicle model and grade information to send a message prompting the user to enter the year, mileage, and degree of damage. The user enters this information and replies. Using this information, the server references a database to obtain market price information. This information is then fed into an artificial intelligence model (e.g., XGBoost, TensorFlow-based model) to calculate the vehicle's estimated value.

[0815] The server also analyzes the text and voice messages entered by the user using an emotion engine (e.g., IBM Watson Tone Analyzer) and provides appropriate feedback and suggestions based on the analysis results. For example, if the user is feeling anxious, the server may send an additional support message to pique the user's interest.

[0816] Once this process is complete, the server generates a message informing the user of the calculated valuation amount and sends it via LINE. The system also stores the valuation results and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[0817] Furthermore, the message also includes a link that allows users who are satisfied with the appraisal price to easily apply for the appraisal service. Simply clicking on this link will complete the application process.

[0818] As a concrete example, consider the case where a user takes a picture of a 2018 model vehicle and sends it via LINE. In this case, the server receives the image and uses an image recognition algorithm to identify the model and grade. The server then sends a message prompting the user to enter the year, mileage, and degree of damage. For example, prompts such as "Please tell us the year of your vehicle," "Please enter the mileage," and "Please tell us the degree of damage" are sent. Once the user provides this information, the server references the database and calculates the appraisal value using an artificial intelligence model. If the user feels anxious or dissatisfied, the emotion engine provides appropriate support.

[0819] Such a system not only allows users to easily and quickly check the estimated value of their vehicle, but also improves the user experience by providing feedback based on emotion recognition.

[0820] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0821] Step 1:

[0822] The user takes an image of the vehicle using a smartphone. The user opens a camera app and takes images of the vehicle from multiple angles. The input of this action is the smartphone camera, and the output is the image data of the captured vehicle.

[0823] Step 2:

[0824] The user takes a photo and sends it using a communication application (e.g., LINE). The user opens the LINE app and sends the image to a dedicated contact. The input to this process is the captured image data, and the output is the image data stored on the LINE server.

[0825] Step 3:

[0826] The server receives images sent by users via the LINE API. The server downloads the image data and transfers it to the image recognition server. The input to this process is the image data stored on the LINE server, and the output is the image data sent to the image recognition server.

[0827] Step 4:

[0828] The image recognition server uses an image recognition algorithm to identify the vehicle model and grade. The image recognition algorithm (e.g., YOLO, ResNet) analyzes the image data and identifies the vehicle model (e.g., Toyota Prius) and grade (e.g., Z grade). The input to this process is the image data sent to the image recognition server, and the output is the identified vehicle model and grade information.

[0829] Step 5:

[0830] Based on the identified information on the car model and grade, the server sends a message to the user to prompt them to enter the year, mileage, and degree of damage. For example, it sends messages such as "Please tell us the year of your car," "Please enter the mileage," and "Please tell us the degree of damage." The input of this process is the identified information on the car model and grade, and the output is a prompt message for the user.

[0831] Step 6:

[0832] The user replies to a message from the server and inputs information about the model year, mileage, and degree of damage. The user sends a response in text format. The input of this process is the text data returned by the user, and the output is the model year, mileage, and degree of damage information.

[0833] Step 7:

[0834] The server queries the database based on the information received from the user. The database is searched to obtain market price information. The input to this process is model year, mileage, and damage level information, and the output is market price data.

[0835] Step 8:

[0836] The server inputs the acquired market price data into an AI model to calculate the vehicle's valuation. The AI ​​model (e.g., XGBoost or TensorFlow-based model) analyzes the data and calculates the valuation. The input of this process is market price data, and the output is the valuation.

[0837] Step 9:

[0838] The server sends the text or voice message received from the user to the emotion engine to analyze the user's emotion. The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the text or voice data and identifies the user's emotion. The input of this process is the text or voice message from the user, and the output is the analyzed emotion data.

[0839] Step 10:

[0840] The server adjusts the feedback and suggestions based on the analysis results and sends a feedback message to the user. For example, if the user expresses dissatisfaction, it sends an additional support message. The input of this process is the analyzed emotion data, and the output is a feedback message.

[0841] Step 11:

[0842] The server prepares a message to notify the user of the calculated valuation amount and sends it via LINE. The notification message includes the valuation amount and a link to apply for the valuation service. The input for this process is the valuation amount, and the output is the notification message.

[0843] Step 12:

[0844] We provide a system in which the server saves the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount. The input of this process is the appraisal results and user information, and the output is the data saved in the database.

[0845] Step 13:

[0846] If the user is satisfied with the valuation amount, they can simply click on the link to apply for the valuation service. Once the user clicks on the link, the application for the valuation service is completed. The input of this process is the user's click action, and the output is the completed application for the valuation service.

[0847] (Application example 2)

[0848] 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."

[0849] While existing vehicle appraisal systems allow users to easily and quickly check the appraisal value, they lack feedback and suggestions that take into account the user's emotional state, which hinders the user experience.Furthermore, food delivery services that use food images lack a means to make suggestions that reflect the user's emotions, which limits the comprehensiveness of their services.

[0850] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0851] In this invention, the server includes means for a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and means for recognizing the user's emotions and providing further feedback or suggestions based on the emotions. This not only allows the user to easily and quickly check the appraisal value, but also allows them to receive appropriate feedback or suggestions according to their emotions, significantly improving the user experience.

[0852] "Vehicle image" is visual data showing the exterior of a vehicle, taken by a user using a smartphone camera.

[0853] A "communication application" is a platform for users to send and receive images they have taken, including, for example, messaging apps and social media apps.

[0854] An "image recognition algorithm" is a calculation procedure for automatically extracting specific vehicle information (model and grade) from input image data.

[0855] "Vehicle model and grade" is classification information that indicates the vehicle category and detailed specifications.

[0856] "Model year" refers to the year in which the vehicle was manufactured.

[0857] "Distance traveled" refers to the total distance traveled by the vehicle, as measured by the odometer.

[0858] "Damage level" is information indicating the level of damage to the exterior of the vehicle.

[0859] A "database" is a collection of information for managing data such as vehicle market price information and past appraisal history in a certain format.

[0860] "Market price information" refers to market data relating to vehicle transaction prices, values, etc.

[0861] "Appraisal Value" is the estimated value of the vehicle calculated based on image recognition algorithms and databases.

[0862] "Emotion recognition" refers to analyzing the emotional state from text or voice input by the user.

[0863] "Feedback" refers to providing answers or suggestions based on the user's emotional state.

[0864] "Suggestions" are recommendations based on the user's input and emotional state to provide optimal actions or support.

[0865] The present invention combines a system that allows users to easily check the appraisal value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. The specific system configuration and processing procedure for implementing this invention are described below.

[0866] System configuration

[0867] The invention mainly comprises the following elements:

[0868] 1. Smartphone: A device that allows users to take pictures of vehicles and food and provide information through text or voice input.

[0869] 2. Communication applications: These are applications that allow users to send and receive images and messages using platforms such as LINE.

[0870] 3. Image recognition algorithm: This algorithm is used to identify the make and model of a vehicle or food from a captured image. It uses existing image recognition services such as AWS Rekognition.

[0871] 4. Database: A database such as MongoDB or Firebase for storing and managing vehicle market price information, past appraisal history, and menu information.

[0872] 5. Artificial intelligence model: A program that calculates appropriate proposals and valuations based on vehicle and food information. The model is built using TensorFlow and other tools.

[0873] 6. Emotion Engine: A program that analyzes the user's emotions from text and voice and provides feedback and suggestions based on that state. Emotion analysis is performed using IBM Watson and Google Cloud Natural Language API.

[0874] Explanation of program processing

[0875] User submitted image:

[0876] Users can take pictures of vehicles or food using their smartphone camera and send them to a server via a communication application such as LINE, with the option to enter additional information via text or voice.

[0877] Image reception and analysis:

[0878] The server receives images sent by users through LINE's API and passes them to an image recognition algorithm, which then identifies the vehicle model and grade, or food.

[0879] User input:

[0880] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional information (year, mileage, degree of damage, etc.) The user provides the additional information by replying to this message.

[0881] Database lookup and valuation calculation:

[0882] Based on the information provided, the server references a database to obtain market price information and past appraisal history for the vehicle, and inputs this data into an AI model to calculate the vehicle's appraisal value and food recommendations.

[0883] Emotion Recognition and Feedback Regulation:

[0884] Text and voice messages from users are sent to the emotion engine, which analyzes their emotions and generates appropriate feedback and suggestions based on the analysis results.

[0885] Notifications and History Retention:

[0886] The calculated valuation amount and proposal details are notified to the user via LINE etc., and this information is simultaneously saved in a database. If the user is satisfied with the valuation, they are also provided with a link to easily apply for the service.

[0887] Specific examples

[0888] For example, consider the case where a user opens the "Emotional Food Order" app by typing "I feel a bit tired today." The emotion engine recognizes this "feeling of fatigue," and the AI ​​model suggests a menu with a relaxing effect (chicken salad and herbal tea). Based on this information, the app notifies the user, "You seem tired today. How about a relaxing chicken salad and herbal tea?"

[0889] Example prompt for a generative AI model:

[0890] User: Opens the food delivery app "Emotional Food Order" on his smartphone and types, "I feel kind of tired today."

[0891] Emotion engine: Recognizing "fatigue" from text.

[0892] AI model: Suggests chicken salad and herbal tea as a menu that is good for recovering from fatigue.

[0893] App: "You seem tired today. Would you like a relaxing chicken salad and herbal tea?"

[0894] This allows users to easily receive suggestions that correspond to their emotions, improving their service experience.

[0895] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0896] Step 1:

[0897] Users use their smartphones to take pictures of vehicles or food and send the images to a server via a communication application such as LINE.

[0898] Input: A user-taken image of a vehicle or food.

[0899] Output: Image data transferred to the server via LINE's API.

[0900] Step 2:

[0901] The server receives image data sent by the user through LINE's API and transfers the image to an image recognition algorithm.

[0902] Input: Image data received by the server.

[0903] Output: The image sent to the image recognition algorithm.

[0904] Step 3:

[0905] Using image recognition algorithms, the server identifies the make and model of the vehicle or the food item.

[0906] Input: The image sent to the image recognition algorithm.

[0907] Output: Identified car model and model, or food information.

[0908] Step 4:

[0909] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional required information (such as model year, mileage, and degree of damage).

[0910] Input: Identified car model and model, or food information.

[0911] Output: A message sent to your smartphone prompting you to enter additional information.

[0912] Step 5:

[0913] The user enters additional information (such as model year, mileage, and degree of damage) on their smartphone and sends this information to the server.

[0914] Input: Additional information entered by the user.

[0915] Output: Additional information sent to the server.

[0916] Step 6:

[0917] The server refers to the database based on the additional information sent by the user and obtains the relevant market price information and past appraisal history.

[0918] Input: User additional information and market price information in the database.

[0919] Output: Obtained market price information and past appraisal history.

[0920] Step 7:

[0921] The server inputs the acquired data into an artificial intelligence model to calculate a vehicle valuation or food recommendations.

[0922] Inputs: Market price information, past appraisal history, artificial intelligence model.

[0923] Output: Calculated valuation or proposal.

[0924] Step 8:

[0925] Text and voice from the user are sent to the emotion engine, and the server analyzes the user's emotions.

[0926] Input: text and voice data, emotion engine.

[0927] Output: Parsed emotional state.

[0928] Step 9:

[0929] Based on the analysis results of the emotion engine, the server generates appropriate feedback and suggestions and notifies the user via LINE or other means.

[0930] Input: Parsed emotional state, template for feedback and suggestions.

[0931] Output: Feedback and suggestions that are communicated to the user.

[0932] Step 10:

[0933] When the user checks the notified estimated price and proposal content and applies for the service, the server provides a link for easily applying for the service.

[0934] Input: What to notify the user.

[0935] Output: A link to apply for the service.

[0936] 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.

[0937] 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.

[0938] 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.

[0939] [Third embodiment]

[0940] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0941] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0942] 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).

[0943] 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.

[0944] 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.

[0945] 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).

[0946] 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.

[0947] 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.

[0948] 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.

[0949] 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.

[0950] 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.

[0951] 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."

[0952] MODE FOR CARRYING OUT THE INVENTION

[0953] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[0954] System configuration

[0955] The system includes the following main elements:

[0956] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[0957] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[0958] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[0959] 4. Database: A database for storing vehicle market price information and past transaction data.

[0960] 5. Artificial intelligence model: A program for calculating appraisal values.

[0961] Program processing

[0962] User submitted image:

[0963] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., LINE). When the user sends the picture, the image data is stored on the LINE server.

[0964] Image reception and analysis:

[0965] The server receives images sent by users through the LINE API and forwards them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. This information is returned to the user's server as a response.

[0966] User input:

[0967] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[0968] Database lookup and valuation calculation:

[0969] The server retrieves market price information for the vehicle based on the model year, mileage, and damage information received from the user, and inputs the acquired data into an artificial intelligence model to calculate the estimated value.

[0970] Valuation Notification:

[0971] The server generates a message to notify the user of the calculated assessment amount and transmits it via the communication application.

[0972] Store and update appraisal history:

[0973] The server stores the valuation results and user information in a database. It periodically checks market trends and notifies users of the latest valuation amount if there is any price movement.

[0974] Simplified service delivery:

[0975] If the user is satisfied with the valuation amount, they can easily apply for the valuation service by clicking on a link on the server, which will be provided along with the valuation amount notification to the user.

[0976] Specific examples

[0977] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from a database, and an artificial intelligence model calculates an appraisal value. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated regularly, and the user simply clicks a link to complete the appraisal service application.

[0978] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[0979] The processing flow will be explained below.

[0980] Step 1:

[0981] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[0982] Step 2:

[0983] The device sends image data to LINE's server based on the user's operation.

[0984] Step 3:

[0985] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[0986] Step 4:

[0987] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[0988] Step 5:

[0989] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[0990] Step 6:

[0991] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[0992] Step 7:

[0993] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[0994] Step 8:

[0995] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[0996] Step 9:

[0997] The database returns historical data and market price information matching the input information to the server.

[0998] Step 10:

[0999] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[1000] Step 11:

[1001] The server (user-side server) prepares a message to notify the user of the assessed value and sends it via a communication application.

[1002] Step 12:

[1003] The user checks the estimated amount through a message sent from the server and applies for the appraisal service if necessary.

[1004] Step 13:

[1005] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[1006] Step 14:

[1007] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[1008] Example 1

[1009] 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."

[1010] Conventional vehicle appraisal methods require users to physically bring in their vehicles, which takes time and effort. Furthermore, the appraisal process is cumbersome, and obtaining an accurate appraisal value requires the presence of an appraiser with specialized knowledge. This means that the needs of users who want a quick and easy vehicle appraisal cannot be fully met.

[1011] 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.

[1012] In this invention, the server includes means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the type and model of the vehicle using an image recognition program, means for having the user input the year, mileage, and degree of damage after identifying the type and grade, means for referencing an information holder based on the input information and obtaining market valuation information, means for calculating an appraised price based on the market valuation information, and means for notifying the user of the calculated appraised price. This allows a user to easily and quickly check the appraised value of their vehicle using their smartphone.

[1013] "User" refers to the individual or entity that takes and transmits images of a vehicle via a communications application and receives assessment information.

[1014] "Vehicle" refers to any movable vehicle that is subject to appraisal, such as a car, motorcycle, or truck.

[1015] "Communications application" refers to software that runs on a smartphone or other device and allows it to send and receive data or information.

[1016] An "image recognition program" refers to an algorithm or software that identifies and classifies specific objects or features from input image data.

[1017] "Vehicle type" refers to the classification of different models produced by a particular automobile manufacturer.

[1018] "Model" refers to each version or type that is further subdivided within a particular vehicle model.

[1019] An "information holder" refers to a database or storage system that stores information necessary for vehicle appraisal, such as market prices and past transaction data.

[1020] "Market valuation information" refers to vehicle value information calculated based on current market trends and past transaction data.

[1021] "Assessed Value" refers to the current market value of the vehicle as calculated through the appraisal process.

[1022] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[1023] System configuration

[1024] The system includes the following main elements:

[1025] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[1026] 2. Communication applications: Applications for sending and receiving image data (e.g. messaging apps).

[1027] 3. Image recognition program: Software to identify the make and model of a vehicle from images (e.g., TensorFlow, OpenCV).

[1028] 4. Information holder: A database for storing vehicle market price information and past transaction data.

[1029] 5. Generative AI model: A program for calculating the appraisal price (e.g., an artificial intelligence model using TensorFlow).

[1030] Program processing

[1031] User submitted image:

[1032] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., a messaging app). When the user sends the picture, the image data is stored on the application's server.

[1033] Image reception and analysis:

[1034] The server receives the image sent by the user through the messaging app's API and forwards it to an image recognition server, which uses an image recognition program to identify the make and model of the vehicle. This information is returned as a response to the user's server.

[1035] User input:

[1036] Based on the identified make and model, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and extent of damage, and the user replies to the message to enter the year, mileage, and extent of damage information.

[1037] Information holder reference and valuation calculation:

[1038] The server obtains market valuation information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs the obtained data into a generative AI model to calculate the estimated price.

[1039] Estimated price notification:

[1040] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[1041] Store and update appraisal history:

[1042] The server stores the valuation results and user information in an information holder. It periodically checks market trends and notifies users of the latest valuation price if there is a price change.

[1043] Simplified service delivery:

[1044] If the user is satisfied with the estimated price, they can simply apply for the appraisal service by clicking on a link on the server, which will be provided when the user receives the notification of the estimated price.

[1045] Specific examples

[1046] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z model and sends it via a messaging app. The server receives this image and uses an image recognition program to identify the make and model of the vehicle. The server then prompts the user to enter the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor damage). Based on this information, market valuation information is obtained from the information holder, and an appraisal price is calculated using a generative AI model. The calculated appraisal price (e.g., 1 million yen) is notified to the user via the messaging app and saved in the information holder along with previous information. This appraisal price is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[1047] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[1048] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1049] Step 1:

[1050] The user launches the camera app on their smartphone and takes a picture of the vehicle.

[1051] This image is temporarily stored in the device's memory, and the user then opens a communication application (e.g., a messaging app) and prepares this image as a message to send.

[1052] Input: Vehicle image

[1053] Output: Message prepared for sending by the communication application

[1054] Step 2:

[1055] The user takes a picture of the vehicle and sends it to the server via a messaging app.

[1056] The server stores the images received via the API in temporary memory.

[1057] Input: A vehicle image sent by the user

[1058] Output: Vehicle images stored on the server

[1059] Step 3:

[1060] The server transfers the stored vehicle images to an image recognition server.

[1061] The image recognition server launches an image recognition program (e.g., TensorFlow, OpenCV) and identifies the vehicle model and grade from the image.

[1062] Input: Vehicle image

[1063] Output: Identified car model and grade

[1064] Step 4:

[1065] The image recognition server returns the identified vehicle model and grade information to the user's server as a response.

[1066] Based on this information, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage.

[1067] The user replies to the message and enters the year, mileage and extent of damage.

[1068] Input: Identified vehicle model and grade, user input (year, mileage, degree of damage)

[1069] Output: Prompt message sent to the user's smartphone, retrieved vehicle information

[1070] Step 5:

[1071] The server refers to the information holder based on the information received from the user, such as the model year, mileage, and degree of damage.

[1072] The server acquires market evaluation information of the target vehicle from the information holder.

[1073] Input: User input (year, mileage, degree of damage)

[1074] Output: Obtained market valuation information

[1075] Step 6:

[1076] The server inputs the acquired market valuation information into a generative AI model to calculate the appraisal price.

[1077] The generative AI model (e.g., a model using TensorFlow) performs the necessary data processing and calculations based on the input data to calculate the appraisal price.

[1078] Input: Market valuation information

[1079] Output: Calculated valuation price

[1080] Step 7:

[1081] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[1082] Input: Calculated valuation price

[1083] Output: A message to be sent to the user informing them of the quoted price.

[1084] Step 8:

[1085] The server stores the assessment results and the information provided by the user in an information holder.

[1086] The server then periodically checks market trends, updates the valuation price as needed, and notifies the user of the latest valuation price.

[1087] Input: Assessment results, user information, periodic market trend data

[1088] Output: Updated valuation price, notification message to user

[1089] Step 9:

[1090] If the user is satisfied with the assessed price, they can complete the application for the assessment service by clicking on a link on the server.

[1091] This link will be provided within the quote notification message.

[1092] Input: User action (click on link)

[1093] Output: Notification of completion of application for appraisal service

[1094] (Application example 1)

[1095] 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."

[1096] Conventional vehicle appraisal systems require users to complete the required procedures, requiring a great deal of time and effort, making it difficult to quickly obtain an appraisal value. Furthermore, it is not possible to easily obtain the latest appraisal value based on market trends or post a vehicle sales advertisement at a fair price based on the appraisal value. A system that solves these problems and allows users to easily and quickly find out the appraisal value of a vehicle and post a sales advertisement at a fair price is needed.

[1097] 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.

[1098] In this invention, the server

[1099] means for a user to take an image of the vehicle using an image capture device and transmit the image via a communication application;

[1100] means for identifying the make and model of the vehicle using an image recognition algorithm;

[1101] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[1102] a means for referencing a database based on the input information and obtaining market price information;

[1103] A means of calculating the appraisal value based on market price information,

[1104] a means for notifying the user of the calculated valuation;

[1105] A means for automatically displaying an estimated value when a user posts a vehicle for sale advertisement;

[1106] Includes.

[1107] This allows users to easily and quickly find out the estimated value of their vehicle, enabling them to post a vehicle sales ad at a fair price.

[1108] "Photography device" refers to equipment used by a user to take images of a vehicle.

[1109] A "communication application" is software that allows users to send and receive images and data they have taken.

[1110] An "image recognition algorithm" is a program that analyzes vehicle images and identifies the vehicle model and grade.

[1111] "Model year" is information indicating the year the vehicle was manufactured.

[1112] "Distance traveled" is information indicating the total distance traveled by the vehicle so far.

[1113] "Damage level" is information indicating the state of damage related to the appearance and functionality of the vehicle.

[1114] A "database" is a system for storing market price information and data necessary for appraisals and for searching such data.

[1115] "Market Price Information" is data regarding the current market value of a vehicle.

[1116] "Appraised value" is the amount resulting from a monetary evaluation of the vehicle's value.

[1117] "Notification means" is a function for informing the user of the calculated appraisal amount.

[1118] "User" means an individual or corporation that uses the System to appraise vehicles and post sales advertisements.

[1119] "Vehicle sales advertisement" refers to advertisement content posted by a user to sell a vehicle.

[1120] A system embodying this invention includes the following main elements:

[1121] 1. Camera: This refers to the device used by the user to take images of the vehicle. Specifically, this refers to the camera on a smartphone.

[1122] 2. Communication application: Software that allows users to send images they have taken. For example, a general messaging app (e.g., a communication application) can be used as a communication application.

[1123] 3. Image recognition algorithm: A program that analyzes captured images of a vehicle and identifies the vehicle model and grade. An image recognition model using TensorFlow falls into this category.

[1124] 4. Database: A system for storing market price information and data required for appraisal. For example, a MySQL database is used.

[1125] 5. Market Value Information: This is data about the current market value of the vehicle, based on which the valuation is calculated.

[1126] 6. Valuation calculation method: An AI model for calculating valuation based on market price information. An AI model using Keras falls into this category.

[1127] 7. Notification Method: This is the communication method to inform the user of the calculated valuation amount. Notification is sent via the communication application API.

[1128] Next, the specific processing flow of each element will be explained.

[1129] First, the user takes a picture of the vehicle using the smartphone camera and sends it via a communication application. The image is then stored on a server and analyzed by an image recognition algorithm, which identifies the make and model of the vehicle.

[1130] Based on the identified make and model, the server sends a prompt message to the user asking them to enter the year, mileage, and damage level, using the following prompt text:

[1131] “You’ve uploaded a picture of your vehicle, now you need to enter some information.

[1132] Year:

[1133] Mileage (km):

[1134] Damage level (minor / moderate / severe):

[1135] "

[1136] Your estimated price is being calculated...

[1137] The information entered by the user regarding the model year, mileage, and degree of damage is sent to a database by the server, where market price information is referenced, and the AI ​​model then calculates the appraisal value based on that information.

[1138] The calculated valuation value is notified to the user via the communication application's API. For example, a message saying "The valuation value is 1 million yen" is sent. At the same time, a function is provided that automatically displays the valuation value when a user tries to post a vehicle sales ad.

[1139] This system allows users to easily find out the estimated value of their vehicle and post a vehicle sales advertisement at an appropriate price based on the estimated value.

[1140] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1141] Step 1:

[1142] The user takes a picture of the vehicle using the camera on the device (smartphone).

[1143] Input: Image of a vehicle taken using a smartphone camera

[1144] Output: Image file of the photographed vehicle

[1145] Step 2:

[1146] The user uses a communication application on their device (smartphone) to send images of the vehicle they have taken to the server.

[1147] Input: Image file of the vehicle, communication application

[1148] Output: Image files of the vehicle sent to the server through the communication application

[1149] Step 3:

[1150] The server uses the communication application's API to receive vehicle images sent by the user and send them to an image recognition algorithm.

[1151] Input: User-submitted vehicle image file

[1152] Output: Image of the vehicle sent to the image recognition algorithm

[1153] Step 4:

[1154] The server uses an image recognition algorithm (TensorFlow model) to identify the vehicle model and grade.

[1155] Input: Vehicle image received by the server

[1156] Output: Identified vehicle model and grade information

[1157] Step 5:

[1158] Based on the identified information on the vehicle model and grade, the server generates a prompt message prompting the user to input the year, mileage, and degree of damage, and transmits the message to the user via the communication application.

[1159] Input: Identified vehicle model and grade information

[1160] Output: Generated prompt (e.g., "Year: Mileage (km): Damage level (minor / moderate / severe): ")

[1161] Step 6:

[1162] The user follows the prompts on the device (smartphone) to enter the model year, mileage, and degree of damage, and then sends the information to the server.

[1163] Input: Year, mileage, and damage information

[1164] Output: Input information sent to the server through a communication application

[1165] Step 7:

[1166] The server retrieves market price information from a database (MySQL) based on the model year, mileage, and degree of damage information received from the user.

[1167] Input: Year, mileage, and damage information received from the user

[1168] Output: Market price information retrieved from the database

[1169] Step 8:

[1170] The server combines the acquired market price information with the identified vehicle model and grade, and user-entered information, and calculates the appraisal value using an AI model (Keras).

[1171] Input: Market price information, specified vehicle model and grade, user-entered model year, mileage, and damage level

[1172] Output: Calculated valuation amount

[1173] Step 9:

[1174] The server notifies the user of the calculated assessment amount via the communication application.

[1175] Input: Calculated valuation amount

[1176] Output: Valuation notification sent to user (e.g. "The valuation is 1 million yen")

[1177] Step 10:

[1178] When a user posts a vehicle sales advertisement, the server automatically displays the calculated estimated value, allowing the user to post the sales advertisement at a fair price.

[1179] Input: Calculated valuation amount

[1180] Output: Estimated price displayed in vehicle sales advertisement

[1181] 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.

[1182] MODE FOR CARRYING OUT THE INVENTION

[1183] This invention combines a system that allows users to easily check the estimated value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. This system allows users to instantly know the estimated value by taking a picture of the vehicle and sending it via a communication application. It also recognizes the user's emotions and provides feedback and suggestions based on those emotions, making it more friendly and improving the user experience.

[1184] System configuration

[1185] The system includes the following main elements:

[1186] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[1187] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[1188] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[1189] 4. Database: A database for storing vehicle market price information and past transaction data.

[1190] 5. Artificial intelligence model: A program for calculating appraisal values.

[1191] 6. Emotion Engine: A program that recognizes the user's emotions and tailors feedback and suggestions accordingly.

[1192] Program processing

[1193] User submitted image:

[1194] Users take pictures of the vehicle using their smartphone camera. They then send the images using a communication application (e.g., LINE). When users send the images, the image data is stored on the LINE server.

[1195] Image reception and analysis:

[1196] The server receives images sent by users through the LINE API and transfers them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. The identified information is returned to the user's server as a response.

[1197] User input:

[1198] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[1199] Database lookup and valuation calculation:

[1200] The server retrieves market price information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs this data into an artificial intelligence model to calculate the vehicle's estimated value.

[1201] Emotion Recognition and Feedback Regulation:

[1202] The server sends the text messages and voice inputs received from the user to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the emotion and adjusts the next feedback or action accordingly. For example, if the user expresses dissatisfaction, it sends a message offering an explanation or additional support.

[1203] Assessment Notification and History Storage:

[1204] The server prepares a message to notify the user of the calculated valuation amount and sends it via a communication application. It also stores the valuation result and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[1205] Simplified service delivery:

[1206] If the user is satisfied with the estimated price, they can easily apply for the appraisal service by clicking on a link on the server, which will be provided along with the notification of the estimated price.

[1207] Specific examples

[1208] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from the database, and an artificial intelligence model is used to calculate an appraisal value. Furthermore, the system analyzes the user's text and voice input using an emotion engine and sends additional support messages if the user is concerned. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[1209] In this way, the system of the present invention not only allows users to easily and quickly check the estimated value of their vehicle, but also provides appropriate support and feedback from the emotion engine, improving the user experience.

[1210] The processing flow will be explained below.

[1211] Step 1:

[1212] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[1213] Step 2:

[1214] The device sends image data to LINE's server based on the user's operation.

[1215] Step 3:

[1216] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[1217] Step 4:

[1218] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[1219] Step 5:

[1220] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[1221] Step 6:

[1222] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[1223] Step 7:

[1224] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[1225] Step 8:

[1226] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[1227] Step 9:

[1228] The database returns historical data and market price information matching the input information to the server.

[1229] Step 10:

[1230] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[1231] Step 11:

[1232] The server (user-side server) retrieves the user's text message and analyzes it using an emotion engine.

[1233] Step 12:

[1234] The server (emotion engine) identifies the user's emotions from the text message and determines whether there is any anxiety or doubt.

[1235] Step 13:

[1236] The server (user-side server) adjusts the content of the valuation notification based on the emotions identified by the emotion engine, for example adding a detailed explanation or support link if the user is feeling anxious.

[1237] Step 14:

[1238] The server (user-side server) sends the coordinated message to the user via a communication application.

[1239] Step 15:

[1240] The user checks the estimated price via a message sent from the server and provides feedback if necessary.

[1241] Step 16:

[1242] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[1243] Step 17:

[1244] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[1245] Step 18:

[1246] If the user is satisfied with the appraisal amount, they can easily apply for the appraisal service by simply clicking on a link on the server.

[1247] Step 19:

[1248] The server (user-side server) forwards the application information to the assessment service provider, and the assessment process begins.

[1249] Example 2

[1250] 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."

[1251] Conventional vehicle appraisal systems require users to provide images of their vehicle and input the necessary information to calculate the appraisal value, which is a complex and time-consuming process. Furthermore, if the appraisal results do not meet user expectations, dissatisfaction and questions tend to arise, and appropriate feedback is not provided. To address these issues, improvements are needed to enable users to easily and quickly check the appraisal value and increase satisfaction.

[1252] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1253] In this invention, the server includes means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and emotion engine means for recognizing the user's emotions and adjusting or suggesting feedback based on the emotions. This allows the user to not only easily and quickly check the appraisal value of their vehicle, but also receive appropriate feedback based on emotion recognition.

[1254] "User" refers to the person who takes a picture of the vehicle, enters the information, and checks the appraisal value.

[1255] "Vehicle" refers to the means of transportation, such as a car or motorcycle, that is the subject of the appraisal.

[1256] "Image" refers to photographic data taken by a user of a vehicle.

[1257] "Communication application" refers to software that provides messaging services such as LINE and sends and receives data between users and servers.

[1258] "Image recognition algorithm" refers to a program for identifying the vehicle model and grade from a photographed image.

[1259] "Identify" refers to identifying and clarifying the vehicle model and grade from an image of the vehicle.

[1260] "Model year" refers to the year the vehicle was manufactured.

[1261] "Distance traveled" refers to the total distance traveled by the vehicle.

[1262] "Extent of damage" refers to the state of damage to the exterior of the vehicle.

[1263] "Database" refers to a collection of information that stores vehicle market price information and past transaction data.

[1264] "Referring" refers to searching for information in a database and obtaining the required data.

[1265] "Market Price Information" refers to data regarding the value of a vehicle in the current market.

[1266] "Appraised value" refers to the assessed amount calculated based on the market value of the vehicle.

[1267] "Notify" refers to informing the user of information such as the appraisal value.

[1268] An "emotion engine" is a program that recognizes a user's emotions and adjusts feedback and suggestions based on them.

[1269] This invention is a system that allows users to instantly check the estimated value of a vehicle by taking a picture of the vehicle using a smartphone camera and sending it via a communication application (e.g., LINE).The system also incorporates an emotion engine that recognizes the user's emotions, and can improve the user experience by providing feedback and suggestions according to the user's emotions.

[1270] When a user takes a picture of a vehicle with their smartphone and sends it via LINE or other services, the image data is stored on LINE's server. The server receives the image using LINE's API and forwards it to an image recognition server. The image recognition server uses an image recognition algorithm (e.g., YOLO, ResNet) to identify the vehicle's model and grade. This identified information is sent back to the server, which then proceeds to the next step of processing.

[1271] The server then uses the identified vehicle model and grade information to send a message prompting the user to enter the year, mileage, and degree of damage. The user enters this information and replies. Using this information, the server references a database to obtain market price information. This information is then fed into an artificial intelligence model (e.g., XGBoost, TensorFlow-based model) to calculate the vehicle's estimated value.

[1272] The server also analyzes the text and voice messages entered by the user using an emotion engine (e.g., IBM Watson Tone Analyzer) and provides appropriate feedback and suggestions based on the analysis results. For example, if the user is feeling anxious, the server may send an additional support message to pique the user's interest.

[1273] Once this process is complete, the server generates a message informing the user of the calculated valuation amount and sends it via LINE. The system also stores the valuation results and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[1274] Furthermore, the message also includes a link that allows users who are satisfied with the appraisal price to easily apply for the appraisal service. Simply clicking on this link will complete the application process.

[1275] As a concrete example, consider the case where a user takes a picture of a 2018 model vehicle and sends it via LINE. In this case, the server receives the image and uses an image recognition algorithm to identify the model and grade. The server then sends a message prompting the user to enter the year, mileage, and degree of damage. For example, prompts such as "Please tell us the year of your vehicle," "Please enter the mileage," and "Please tell us the degree of damage" are sent. Once the user provides this information, the server references the database and calculates the appraisal value using an artificial intelligence model. If the user feels anxious or dissatisfied, the emotion engine provides appropriate support.

[1276] Such a system not only allows users to easily and quickly check the estimated value of their vehicle, but also improves the user experience by providing feedback based on emotion recognition.

[1277] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1278] Step 1:

[1279] The user takes an image of the vehicle using a smartphone. The user opens a camera app and takes images of the vehicle from multiple angles. The input of this action is the smartphone camera, and the output is the image data of the captured vehicle.

[1280] Step 2:

[1281] The user takes a photo and sends it using a communication application (e.g., LINE). The user opens the LINE app and sends the image to a dedicated contact. The input to this process is the captured image data, and the output is the image data stored on the LINE server.

[1282] Step 3:

[1283] The server receives images sent by users via the LINE API. The server downloads the image data and transfers it to the image recognition server. The input to this process is the image data stored on the LINE server, and the output is the image data sent to the image recognition server.

[1284] Step 4:

[1285] The image recognition server uses an image recognition algorithm to identify the vehicle model and grade. The image recognition algorithm (e.g., YOLO, ResNet) analyzes the image data and identifies the vehicle model (e.g., Toyota Prius) and grade (e.g., Z grade). The input to this process is the image data sent to the image recognition server, and the output is the identified vehicle model and grade information.

[1286] Step 5:

[1287] Based on the identified information on the car model and grade, the server sends a message to the user to prompt them to enter the year, mileage, and degree of damage. For example, it sends messages such as "Please tell us the year of your car," "Please enter the mileage," and "Please tell us the degree of damage." The input of this process is the identified information on the car model and grade, and the output is a prompt message for the user.

[1288] Step 6:

[1289] The user replies to a message from the server and inputs information about the model year, mileage, and degree of damage. The user sends a response in text format. The input of this process is the text data returned by the user, and the output is the model year, mileage, and degree of damage information.

[1290] Step 7:

[1291] The server queries the database based on the information received from the user. The database is searched to obtain market price information. The input to this process is model year, mileage, and damage level information, and the output is market price data.

[1292] Step 8:

[1293] The server inputs the acquired market price data into an AI model to calculate the vehicle's valuation. The AI ​​model (e.g., XGBoost or TensorFlow-based model) analyzes the data and calculates the valuation. The input of this process is market price data, and the output is the valuation.

[1294] Step 9:

[1295] The server sends the text or voice message received from the user to the emotion engine to analyze the user's emotion. The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the text or voice data and identifies the user's emotion. The input of this process is the text or voice message from the user, and the output is the analyzed emotion data.

[1296] Step 10:

[1297] The server adjusts the feedback and suggestions based on the analysis results and sends a feedback message to the user. For example, if the user expresses dissatisfaction, it sends an additional support message. The input of this process is the analyzed emotion data, and the output is a feedback message.

[1298] Step 11:

[1299] The server prepares a message to notify the user of the calculated valuation amount and sends it via LINE. The notification message includes the valuation amount and a link to apply for the valuation service. The input for this process is the valuation amount, and the output is the notification message.

[1300] Step 12:

[1301] We provide a system in which the server saves the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount. The input of this process is the appraisal results and user information, and the output is the data saved in the database.

[1302] Step 13:

[1303] If the user is satisfied with the valuation amount, they can simply click on the link to apply for the valuation service. Once the user clicks on the link, the application for the valuation service is completed. The input of this process is the user's click action, and the output is the completed application for the valuation service.

[1304] (Application example 2)

[1305] 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."

[1306] While existing vehicle appraisal systems allow users to easily and quickly check the appraisal value, they lack feedback and suggestions that take into account the user's emotional state, which hinders the user experience.Furthermore, food delivery services that use food images lack a means to make suggestions that reflect the user's emotions, which limits the comprehensiveness of their services.

[1307] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1308] In this invention, the server includes means for a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and means for recognizing the user's emotions and providing further feedback or suggestions based on the emotions. This not only allows the user to easily and quickly check the appraisal value, but also allows them to receive appropriate feedback or suggestions according to their emotions, significantly improving the user experience.

[1309] "Vehicle image" is visual data showing the exterior of a vehicle, taken by a user using a smartphone camera.

[1310] A "communication application" is a platform for users to send and receive images they have taken, including, for example, messaging apps and social media apps.

[1311] An "image recognition algorithm" is a calculation procedure for automatically extracting specific vehicle information (model and grade) from input image data.

[1312] "Vehicle model and grade" is classification information that indicates the vehicle category and detailed specifications.

[1313] "Model year" refers to the year in which the vehicle was manufactured.

[1314] "Distance traveled" refers to the total distance traveled by the vehicle, as measured by the odometer.

[1315] "Damage level" is information indicating the level of damage to the exterior of the vehicle.

[1316] A "database" is a collection of information for managing data such as vehicle market price information and past appraisal history in a certain format.

[1317] "Market price information" refers to market data relating to vehicle transaction prices, values, etc.

[1318] "Appraisal Value" is the estimated value of the vehicle calculated based on image recognition algorithms and databases.

[1319] "Emotion recognition" refers to analyzing the emotional state from text or voice input by the user.

[1320] "Feedback" refers to providing answers or suggestions based on the user's emotional state.

[1321] "Suggestions" are recommendations based on the user's input and emotional state to provide optimal actions or support.

[1322] The present invention combines a system that allows users to easily check the appraisal value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. The specific system configuration and processing procedure for implementing this invention are described below.

[1323] System configuration

[1324] The invention mainly comprises the following elements:

[1325] 1. Smartphone: A device that allows users to take pictures of vehicles and food and provide information through text or voice input.

[1326] 2. Communication applications: These are applications that allow users to send and receive images and messages using platforms such as LINE.

[1327] 3. Image recognition algorithm: This algorithm is used to identify the make and model of a vehicle or food from a captured image. It uses existing image recognition services such as AWS Rekognition.

[1328] 4. Database: A database such as MongoDB or Firebase for storing and managing vehicle market price information, past appraisal history, and menu information.

[1329] 5. Artificial intelligence model: A program that calculates appropriate proposals and valuations based on vehicle and food information. The model is built using TensorFlow and other tools.

[1330] 6. Emotion Engine: A program that analyzes the user's emotions from text and voice and provides feedback and suggestions based on that state. Emotion analysis is performed using IBM Watson and Google Cloud Natural Language API.

[1331] Explanation of program processing

[1332] User submitted image:

[1333] Users can take pictures of vehicles or food using their smartphone camera and send them to a server via a communication application such as LINE, with the option to enter additional information via text or voice.

[1334] Image reception and analysis:

[1335] The server receives images sent by users through LINE's API and passes them to an image recognition algorithm, which then identifies the vehicle model and grade, or food.

[1336] User input:

[1337] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional information (year, mileage, degree of damage, etc.) The user provides the additional information by replying to this message.

[1338] Database lookup and valuation calculation:

[1339] Based on the information provided, the server references a database to obtain market price information and past appraisal history for the vehicle, and inputs this data into an AI model to calculate the vehicle's appraisal value and food recommendations.

[1340] Emotion Recognition and Feedback Regulation:

[1341] Text and voice messages from users are sent to the emotion engine, which analyzes their emotions and generates appropriate feedback and suggestions based on the analysis results.

[1342] Notifications and History Retention:

[1343] The calculated valuation amount and proposal details are notified to the user via LINE etc., and this information is simultaneously saved in a database. If the user is satisfied with the valuation, they are also provided with a link to easily apply for the service.

[1344] Specific examples

[1345] For example, consider the case where a user opens the "Emotional Food Order" app by typing "I feel a bit tired today." The emotion engine recognizes this "feeling of fatigue," and the AI ​​model suggests a menu with a relaxing effect (chicken salad and herbal tea). Based on this information, the app notifies the user, "You seem tired today. How about a relaxing chicken salad and herbal tea?"

[1346] Example prompt for a generative AI model:

[1347] User: Opens the food delivery app "Emotional Food Order" on his smartphone and types, "I feel kind of tired today."

[1348] Emotion engine: Recognizing "fatigue" from text.

[1349] AI model: Suggests chicken salad and herbal tea as a menu that is good for recovering from fatigue.

[1350] App: "You seem tired today. Would you like a relaxing chicken salad and herbal tea?"

[1351] This allows users to easily receive suggestions that correspond to their emotions, improving their service experience.

[1352] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1353] Step 1:

[1354] Users use their smartphones to take pictures of vehicles or food and send the images to a server via a communication application such as LINE.

[1355] Input: A user-taken image of a vehicle or food.

[1356] Output: Image data transferred to the server via LINE's API.

[1357] Step 2:

[1358] The server receives image data sent by the user through LINE's API and transfers the image to an image recognition algorithm.

[1359] Input: Image data received by the server.

[1360] Output: The image sent to the image recognition algorithm.

[1361] Step 3:

[1362] Using image recognition algorithms, the server identifies the make and model of the vehicle or the food item.

[1363] Input: The image sent to the image recognition algorithm.

[1364] Output: Identified car model and model, or food information.

[1365] Step 4:

[1366] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional required information (such as model year, mileage, and degree of damage).

[1367] Input: Identified car model and model, or food information.

[1368] Output: A message sent to your smartphone prompting you to enter additional information.

[1369] Step 5:

[1370] The user enters additional information (such as model year, mileage, and degree of damage) on their smartphone and sends this information to the server.

[1371] Input: Additional information entered by the user.

[1372] Output: Additional information sent to the server.

[1373] Step 6:

[1374] The server refers to the database based on the additional information sent by the user and obtains the relevant market price information and past appraisal history.

[1375] Input: User additional information and market price information in the database.

[1376] Output: Obtained market price information and past appraisal history.

[1377] Step 7:

[1378] The server inputs the acquired data into an artificial intelligence model to calculate a vehicle valuation or food recommendations.

[1379] Inputs: Market price information, past appraisal history, artificial intelligence model.

[1380] Output: Calculated valuation or proposal.

[1381] Step 8:

[1382] Text and voice from the user are sent to the emotion engine, and the server analyzes the user's emotions.

[1383] Input: text and voice data, emotion engine.

[1384] Output: Parsed emotional state.

[1385] Step 9:

[1386] Based on the analysis results of the emotion engine, the server generates appropriate feedback and suggestions and notifies the user via LINE or other means.

[1387] Input: Parsed emotional state, template for feedback and suggestions.

[1388] Output: Feedback and suggestions that are communicated to the user.

[1389] Step 10:

[1390] When the user checks the notified estimated price and proposal content and applies for the service, the server provides a link for easily applying for the service.

[1391] Input: What to notify the user.

[1392] Output: A link to apply for the service.

[1393] 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.

[1394] 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.

[1395] 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.

[1396] [Fourth embodiment]

[1397] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1398] 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.

[1399] 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).

[1400] 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.

[1401] 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.

[1402] 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).

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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."

[1410] MODE FOR CARRYING OUT THE INVENTION

[1411] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[1412] System configuration

[1413] The system includes the following main elements:

[1414] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[1415] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[1416] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[1417] 4. Database: A database for storing vehicle market price information and past transaction data.

[1418] 5. Artificial intelligence model: A program for calculating appraisal values.

[1419] Program processing

[1420] User submitted image:

[1421] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., LINE). When the user sends the picture, the image data is stored on the LINE server.

[1422] Image reception and analysis:

[1423] The server receives images sent by users through the LINE API and forwards them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. This information is returned to the user's server as a response.

[1424] User input:

[1425] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[1426] Database lookup and valuation calculation:

[1427] The server retrieves market price information for the vehicle based on the model year, mileage, and damage information received from the user, and inputs the acquired data into an artificial intelligence model to calculate the estimated value.

[1428] Valuation Notification:

[1429] The server generates a message to notify the user of the calculated assessment amount and transmits it via the communication application.

[1430] Store and update appraisal history:

[1431] The server stores the valuation results and user information in a database. It periodically checks market trends and notifies users of the latest valuation amount if there is any price movement.

[1432] Simplified service delivery:

[1433] If the user is satisfied with the valuation amount, they can easily apply for the valuation service by clicking on a link on the server, which will be provided along with the valuation amount notification to the user.

[1434] Specific examples

[1435] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from a database, and an artificial intelligence model calculates an appraisal value. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated regularly, and the user simply clicks a link to complete the appraisal service application.

[1436] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[1437] The processing flow will be explained below.

[1438] Step 1:

[1439] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[1440] Step 2:

[1441] The device sends image data to LINE's server based on the user's operation.

[1442] Step 3:

[1443] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[1444] Step 4:

[1445] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[1446] Step 5:

[1447] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[1448] Step 6:

[1449] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[1450] Step 7:

[1451] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[1452] Step 8:

[1453] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[1454] Step 9:

[1455] The database returns historical data and market price information matching the input information to the server.

[1456] Step 10:

[1457] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[1458] Step 11:

[1459] The server (user-side server) prepares a message to notify the user of the assessed value and sends it via a communication application.

[1460] Step 12:

[1461] The user checks the estimated amount through a message sent from the server and applies for the appraisal service if necessary.

[1462] Step 13:

[1463] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[1464] Step 14:

[1465] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[1466] Example 1

[1467] 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."

[1468] Conventional vehicle appraisal methods require users to physically bring in their vehicles, which takes time and effort. Furthermore, the appraisal process is cumbersome, and obtaining an accurate appraisal value requires the presence of an appraiser with specialized knowledge. This means that the needs of users who want a quick and easy vehicle appraisal cannot be fully met.

[1469] 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.

[1470] In this invention, the server includes means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the type and model of the vehicle using an image recognition program, means for having the user input the year, mileage, and degree of damage after identifying the type and grade, means for referencing an information holder based on the input information and obtaining market valuation information, means for calculating an appraised price based on the market valuation information, and means for notifying the user of the calculated appraised price. This allows a user to easily and quickly check the appraised value of their vehicle using their smartphone.

[1471] "User" refers to the individual or entity that takes and transmits images of a vehicle via a communications application and receives assessment information.

[1472] "Vehicle" refers to any movable vehicle that is subject to appraisal, such as a car, motorcycle, or truck.

[1473] "Communications application" refers to software that runs on a smartphone or other device and allows it to send and receive data or information.

[1474] An "image recognition program" refers to an algorithm or software that identifies and classifies specific objects or features from input image data.

[1475] "Vehicle type" refers to the classification of different models produced by a particular automobile manufacturer.

[1476] "Model" refers to each version or type that is further subdivided within a particular vehicle model.

[1477] An "information holder" refers to a database or storage system that stores information necessary for vehicle appraisal, such as market prices and past transaction data.

[1478] "Market valuation information" refers to vehicle value information calculated based on current market trends and past transaction data.

[1479] "Assessed Value" refers to the current market value of the vehicle as calculated through the appraisal process.

[1480] The present invention relates to a system that allows users to easily check the estimated value of a vehicle using a smartphone. This system allows users to instantly know the estimated value of a vehicle by taking a picture of the vehicle and sending the picture via a communication application.

[1481] System configuration

[1482] The system includes the following main elements:

[1483] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[1484] 2. Communication applications: Applications for sending and receiving image data (e.g. messaging apps).

[1485] 3. Image recognition program: Software to identify the make and model of a vehicle from images (e.g., TensorFlow, OpenCV).

[1486] 4. Information holder: A database for storing vehicle market price information and past transaction data.

[1487] 5. Generative AI model: A program for calculating the appraisal price (e.g., an artificial intelligence model using TensorFlow).

[1488] Program processing

[1489] User submitted image:

[1490] The user takes a picture of the vehicle using the camera on their smartphone. Then, they send the picture using a communication application (e.g., a messaging app). When the user sends the picture, the image data is stored on the application's server.

[1491] Image reception and analysis:

[1492] The server receives the image sent by the user through the messaging app's API and forwards it to an image recognition server, which uses an image recognition program to identify the make and model of the vehicle. This information is returned as a response to the user's server.

[1493] User input:

[1494] Based on the identified make and model, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and extent of damage, and the user replies to the message to enter the year, mileage, and extent of damage information.

[1495] Information holder reference and valuation calculation:

[1496] The server obtains market valuation information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs the obtained data into a generative AI model to calculate the estimated price.

[1497] Estimated price notification:

[1498] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[1499] Store and update appraisal history:

[1500] The server stores the valuation results and user information in an information holder. It periodically checks market trends and notifies users of the latest valuation price if there is a price change.

[1501] Simplified service delivery:

[1502] If the user is satisfied with the estimated price, they can simply apply for the appraisal service by clicking on a link on the server, which will be provided when the user receives the notification of the estimated price.

[1503] Specific examples

[1504] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z model and sends it via a messaging app. The server receives this image and uses an image recognition program to identify the make and model of the vehicle. The server then prompts the user to enter the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor damage). Based on this information, market valuation information is obtained from the information holder, and an appraisal price is calculated using a generative AI model. The calculated appraisal price (e.g., 1 million yen) is notified to the user via the messaging app and saved in the information holder along with previous information. This appraisal price is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[1505] In this way, the system of the present invention allows users to easily and quickly find out the appraised value of their vehicle, greatly simplifying the cumbersome procedures that have been used up until now.

[1506] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1507] Step 1:

[1508] The user launches the camera app on their smartphone and takes a picture of the vehicle.

[1509] This image is temporarily stored in the device's memory, and the user then opens a communication application (e.g., a messaging app) and prepares this image as a message to send.

[1510] Input: Vehicle image

[1511] Output: Message prepared for sending by the communication application

[1512] Step 2:

[1513] The user takes a picture of the vehicle and sends it to the server via a messaging app.

[1514] The server stores the images received via the API in temporary memory.

[1515] Input: A vehicle image sent by the user

[1516] Output: Vehicle images stored on the server

[1517] Step 3:

[1518] The server transfers the stored vehicle images to an image recognition server.

[1519] The image recognition server launches an image recognition program (e.g., TensorFlow, OpenCV) and identifies the vehicle model and grade from the image.

[1520] Input: Vehicle image

[1521] Output: Identified car model and grade

[1522] Step 4:

[1523] The image recognition server returns the identified vehicle model and grade information to the user's server as a response.

[1524] Based on this information, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage.

[1525] The user replies to the message and enters the year, mileage and extent of damage.

[1526] Input: Identified vehicle model and grade, user input (year, mileage, degree of damage)

[1527] Output: Prompt message sent to the user's smartphone, retrieved vehicle information

[1528] Step 5:

[1529] The server refers to the information holder based on the information received from the user, such as the model year, mileage, and degree of damage.

[1530] The server acquires market evaluation information of the target vehicle from the information holder.

[1531] Input: User input (year, mileage, degree of damage)

[1532] Output: Obtained market valuation information

[1533] Step 6:

[1534] The server inputs the acquired market valuation information into a generative AI model to calculate the appraisal price.

[1535] The generative AI model (e.g., a model using TensorFlow) performs the necessary data processing and calculations based on the input data to calculate the appraisal price.

[1536] Input: Market valuation information

[1537] Output: Calculated valuation price

[1538] Step 7:

[1539] The server generates a message to notify the user of the calculated valuation price and transmits it via the communication application.

[1540] Input: Calculated valuation price

[1541] Output: A message to be sent to the user informing them of the quoted price.

[1542] Step 8:

[1543] The server stores the assessment results and the information provided by the user in an information holder.

[1544] The server then periodically checks market trends, updates the valuation price as needed, and notifies the user of the latest valuation price.

[1545] Input: Assessment results, user information, periodic market trend data

[1546] Output: Updated valuation price, notification message to user

[1547] Step 9:

[1548] If the user is satisfied with the assessed price, they can complete the application for the assessment service by clicking on a link on the server.

[1549] This link will be provided within the quote notification message.

[1550] Input: User action (click on link)

[1551] Output: Notification of completion of application for appraisal service

[1552] (Application example 1)

[1553] 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."

[1554] Conventional vehicle appraisal systems require users to complete the required procedures, requiring a great deal of time and effort, making it difficult to quickly obtain an appraisal value. Furthermore, it is not possible to easily obtain the latest appraisal value based on market trends or post a vehicle sales advertisement at a fair price based on the appraisal value. A system that solves these problems and allows users to easily and quickly find out the appraisal value of a vehicle and post a sales advertisement at a fair price is needed.

[1555] 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.

[1556] In this invention, the server

[1557] means for a user to take an image of the vehicle using an image capture device and transmit the image via a communication application;

[1558] means for identifying the make and model of the vehicle using an image recognition algorithm;

[1559] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[1560] a means for referencing a database based on the input information and obtaining market price information;

[1561] A means of calculating the appraisal value based on market price information,

[1562] a means for notifying the user of the calculated valuation;

[1563] A means for automatically displaying an estimated value when a user posts a vehicle for sale advertisement;

[1564] Includes.

[1565] This allows users to easily and quickly find out the estimated value of their vehicle, enabling them to post a vehicle sales ad at a fair price.

[1566] "Photography device" refers to equipment used by a user to take images of a vehicle.

[1567] A "communication application" is software that allows users to send and receive images and data they have taken.

[1568] An "image recognition algorithm" is a program that analyzes vehicle images and identifies the vehicle model and grade.

[1569] "Model year" is information indicating the year the vehicle was manufactured.

[1570] "Distance traveled" is information indicating the total distance traveled by the vehicle so far.

[1571] "Damage level" is information indicating the state of damage related to the appearance and functionality of the vehicle.

[1572] A "database" is a system for storing market price information and data necessary for appraisals and for searching such data.

[1573] "Market Price Information" is data regarding the current market value of a vehicle.

[1574] "Appraised value" is the amount resulting from a monetary evaluation of the vehicle's value.

[1575] "Notification means" is a function for informing the user of the calculated appraisal amount.

[1576] "User" means an individual or corporation that uses the System to appraise vehicles and post sales advertisements.

[1577] "Vehicle sales advertisement" refers to advertisement content posted by a user to sell a vehicle.

[1578] A system embodying this invention includes the following main elements:

[1579] 1. Camera: This refers to the device used by the user to take images of the vehicle. Specifically, this refers to the camera on a smartphone.

[1580] 2. Communication application: Software that allows users to send images they have taken. For example, a general messaging app (e.g., a communication application) can be used as a communication application.

[1581] 3. Image recognition algorithm: A program that analyzes captured images of a vehicle and identifies the vehicle model and grade. An image recognition model using TensorFlow falls into this category.

[1582] 4. Database: A system for storing market price information and data required for appraisal. For example, a MySQL database is used.

[1583] 5. Market Value Information: This is data about the current market value of the vehicle, based on which the valuation is calculated.

[1584] 6. Valuation calculation method: An AI model for calculating valuation based on market price information. An AI model using Keras falls into this category.

[1585] 7. Notification Method: This is the communication method to inform the user of the calculated valuation amount. Notification is sent via the communication application API.

[1586] Next, the specific processing flow of each element will be explained.

[1587] First, the user takes a picture of the vehicle using the smartphone camera and sends it via a communication application. The image is then stored on a server and analyzed by an image recognition algorithm, which identifies the make and model of the vehicle.

[1588] Based on the identified make and model, the server sends a prompt message to the user asking them to enter the year, mileage, and damage level, using the following prompt text:

[1589] “You’ve uploaded a picture of your vehicle, now you need to enter some information.

[1590] Year:

[1591] Mileage (km):

[1592] Damage level (minor / moderate / severe):

[1593] "

[1594] Your estimated price is being calculated...

[1595] The information entered by the user regarding the model year, mileage, and degree of damage is sent to a database by the server, where market price information is referenced, and the AI ​​model then calculates the appraisal value based on that information.

[1596] The calculated valuation value is notified to the user via the communication application's API. For example, a message saying "The valuation value is 1 million yen" is sent. At the same time, a function is provided that automatically displays the valuation value when a user tries to post a vehicle sales ad.

[1597] This system allows users to easily find out the estimated value of their vehicle and post a vehicle sales advertisement at an appropriate price based on the estimated value.

[1598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1599] Step 1:

[1600] The user takes a picture of the vehicle using the camera on the device (smartphone).

[1601] Input: Image of a vehicle taken using a smartphone camera

[1602] Output: Image file of the photographed vehicle

[1603] Step 2:

[1604] The user uses a communication application on their device (smartphone) to send images of the vehicle they have taken to the server.

[1605] Input: Image file of the vehicle, communication application

[1606] Output: Image files of the vehicle sent to the server through the communication application

[1607] Step 3:

[1608] The server uses the communication application's API to receive vehicle images sent by the user and send them to an image recognition algorithm.

[1609] Input: User-submitted vehicle image file

[1610] Output: Image of the vehicle sent to the image recognition algorithm

[1611] Step 4:

[1612] The server uses an image recognition algorithm (TensorFlow model) to identify the vehicle model and grade.

[1613] Input: Vehicle image received by the server

[1614] Output: Identified vehicle model and grade information

[1615] Step 5:

[1616] Based on the identified information on the vehicle model and grade, the server generates a prompt message prompting the user to input the year, mileage, and degree of damage, and transmits the message to the user via the communication application.

[1617] Input: Identified vehicle model and grade information

[1618] Output: Generated prompt (e.g., "Year: Mileage (km): Damage level (minor / moderate / severe): ")

[1619] Step 6:

[1620] The user follows the prompts on the device (smartphone) to enter the model year, mileage, and degree of damage, and then sends the information to the server.

[1621] Input: Year, mileage, and damage information

[1622] Output: Input information sent to the server through a communication application

[1623] Step 7:

[1624] The server retrieves market price information from a database (MySQL) based on the model year, mileage, and degree of damage information received from the user.

[1625] Input: Year, mileage, and damage information received from the user

[1626] Output: Market price information retrieved from the database

[1627] Step 8:

[1628] The server combines the acquired market price information with the identified vehicle model and grade, and user-entered information, and calculates the appraisal value using an AI model (Keras).

[1629] Input: Market price information, specified vehicle model and grade, user-entered model year, mileage, and damage level

[1630] Output: Calculated valuation amount

[1631] Step 9:

[1632] The server notifies the user of the calculated assessment amount via the communication application.

[1633] Input: Calculated valuation amount

[1634] Output: Valuation notification sent to user (e.g. "The valuation is 1 million yen")

[1635] Step 10:

[1636] When a user posts a vehicle sales advertisement, the server automatically displays the calculated estimated value, allowing the user to post the sales advertisement at a fair price.

[1637] Input: Calculated valuation amount

[1638] Output: Estimated price displayed in vehicle sales advertisement

[1639] 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.

[1640] MODE FOR CARRYING OUT THE INVENTION

[1641] This invention combines a system that allows users to easily check the estimated value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. This system allows users to instantly know the estimated value by taking a picture of the vehicle and sending it via a communication application. It also recognizes the user's emotions and provides feedback and suggestions based on those emotions, making it more friendly and improving the user experience.

[1642] System configuration

[1643] The system includes the following main elements:

[1644] 1. Smartphone: A device that allows the user to take images of the vehicle and send them using a communication application.

[1645] 2. Communication applications: Applications for sending and receiving image data (e.g., LINE).

[1646] 3. Image recognition algorithm: A program for identifying the vehicle model and grade from an image of the vehicle.

[1647] 4. Database: A database for storing vehicle market price information and past transaction data.

[1648] 5. Artificial intelligence model: A program for calculating appraisal values.

[1649] 6. Emotion Engine: A program that recognizes the user's emotions and tailors feedback and suggestions accordingly.

[1650] Program processing

[1651] User submitted image:

[1652] Users take pictures of the vehicle using their smartphone camera. They then send the images using a communication application (e.g., LINE). When users send the images, the image data is stored on the LINE server.

[1653] Image reception and analysis:

[1654] The server receives images sent by users through the LINE API and transfers them to the image recognition server, which uses an image recognition algorithm to identify the vehicle model and grade. The identified information is returned to the user's server as a response.

[1655] User input:

[1656] Based on the identified car model and grade, the server sends a message to the user's smartphone prompting them to enter the year, mileage, and degree of damage. The user then replies to the message and enters the year, mileage, and degree of damage information.

[1657] Database lookup and valuation calculation:

[1658] The server retrieves market price information for the vehicle based on the model year, mileage, and damage level information received from the user, and inputs this data into an artificial intelligence model to calculate the vehicle's estimated value.

[1659] Emotion Recognition and Feedback Regulation:

[1660] The server sends the text messages and voice inputs received from the user to the emotion engine, which analyzes the user's emotions. The emotion engine identifies the emotion and adjusts the next feedback or action accordingly. For example, if the user expresses dissatisfaction, it sends a message offering an explanation or additional support.

[1661] Assessment Notification and History Storage:

[1662] The server prepares a message to notify the user of the calculated valuation amount and sends it via a communication application. It also stores the valuation result and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[1663] Simplified service delivery:

[1664] If the user is satisfied with the estimated price, they can easily apply for the appraisal service by clicking on a link on the server, which will be provided along with the notification of the estimated price.

[1665] Specific examples

[1666] For example, consider the case where a user takes a photo of a 2018 Toyota Prius Z grade and sends it via LINE. The server receives this image and uses an image recognition algorithm to identify the model and grade. The user then enters the year, mileage (e.g., 30,000 km), and degree of damage (e.g., minor scratches). Based on this information, market price information is retrieved from the database, and an artificial intelligence model is used to calculate an appraisal value. Furthermore, the system analyzes the user's text and voice input using an emotion engine and sends additional support messages if the user is concerned. The calculated appraisal value (e.g., 1 million yen) is notified to the user via LINE and stored in the database along with past information. This appraisal value is updated periodically, and the user simply clicks a link to complete the appraisal service application.

[1667] In this way, the system of the present invention not only allows users to easily and quickly check the estimated value of their vehicle, but also provides appropriate support and feedback from the emotion engine, improving the user experience.

[1668] The processing flow will be explained below.

[1669] Step 1:

[1670] The user takes a picture of the vehicle using the camera app on their smartphone, then uploads the image to a communication application (e.g., LINE) and presses the "send" button.

[1671] Step 2:

[1672] The device sends image data to LINE's server based on the user's operation.

[1673] Step 3:

[1674] The server (LINE's server) receives the image data and either redirects it to the specified URL or uses the API key to transfer the image to the linked image recognition server.

[1675] Step 4:

[1676] The server (image recognition server) preprocesses the received image data and extracts vehicle features from the image.

[1677] Step 5:

[1678] The server (image recognition server) uses an image recognition algorithm to identify the vehicle model and grade, and returns the identified information to the user's server as a response.

[1679] Step 6:

[1680] The server (user-side server) sends a message to the user prompting them to input the year, mileage, and degree of damage based on the identified information on the vehicle model and grade.

[1681] Step 7:

[1682] The user inputs the model year, mileage, and degree of damage according to the message sent from the server, and replies via the communication application.

[1683] Step 8:

[1684] The server (user-side server) queries the database based on the information received from the user, such as the model year, mileage, and degree of damage.

[1685] Step 9:

[1686] The database returns historical data and market price information matching the input information to the server.

[1687] Step 10:

[1688] The server (user's server) uses the acquired data to calculate the vehicle's estimated value using an artificial intelligence model.

[1689] Step 11:

[1690] The server (user-side server) retrieves the user's text message and analyzes it using an emotion engine.

[1691] Step 12:

[1692] The server (emotion engine) identifies the user's emotions from the text message and determines whether there is any anxiety or doubt.

[1693] Step 13:

[1694] The server (user-side server) adjusts the content of the valuation notification based on the emotions identified by the emotion engine, for example adding a detailed explanation or support link if the user is feeling anxious.

[1695] Step 14:

[1696] The server (user-side server) sends the coordinated message to the user via a communication application.

[1697] Step 15:

[1698] The user checks the estimated price via a message sent from the server and provides feedback if necessary.

[1699] Step 16:

[1700] The server (user-side server) stores the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount.

[1701] Step 17:

[1702] Users can receive periodic notifications sent from the server and check the latest valuation of their vehicle.

[1703] Step 18:

[1704] If the user is satisfied with the appraisal amount, they can easily apply for the appraisal service by simply clicking on a link on the server.

[1705] Step 19:

[1706] The server (user-side server) forwards the application information to the assessment service provider, and the assessment process begins.

[1707] Example 2

[1708] 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."

[1709] Conventional vehicle appraisal systems require users to provide images of their vehicle and input the necessary information to calculate the appraisal value, which is a complex and time-consuming process. Furthermore, if the appraisal results do not meet user expectations, dissatisfaction and questions tend to arise, and appropriate feedback is not provided. To address these issues, improvements are needed to enable users to easily and quickly check the appraisal value and increase satisfaction.

[1710] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1711] In this invention, the server includes means for allowing a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and emotion engine means for recognizing the user's emotions and adjusting or suggesting feedback based on the emotions. This allows the user to not only easily and quickly check the appraisal value of their vehicle, but also receive appropriate feedback based on emotion recognition.

[1712] "User" refers to the person who takes a picture of the vehicle, enters the information, and checks the appraisal value.

[1713] "Vehicle" refers to the means of transportation, such as a car or motorcycle, that is the subject of the appraisal.

[1714] "Image" refers to photographic data taken by a user of a vehicle.

[1715] "Communication application" refers to software that provides messaging services such as LINE and sends and receives data between users and servers.

[1716] "Image recognition algorithm" refers to a program for identifying the vehicle model and grade from a photographed image.

[1717] "Identify" refers to identifying and clarifying the vehicle model and grade from an image of the vehicle.

[1718] "Model year" refers to the year the vehicle was manufactured.

[1719] "Distance traveled" refers to the total distance traveled by the vehicle.

[1720] "Extent of damage" refers to the state of damage to the exterior of the vehicle.

[1721] "Database" refers to a collection of information that stores vehicle market price information and past transaction data.

[1722] "Referring" refers to searching for information in a database and obtaining the required data.

[1723] "Market Price Information" refers to data regarding the value of a vehicle in the current market.

[1724] "Appraised value" refers to the assessed amount calculated based on the market value of the vehicle.

[1725] "Notify" refers to informing the user of information such as the appraisal value.

[1726] An "emotion engine" is a program that recognizes a user's emotions and adjusts feedback and suggestions based on them.

[1727] This invention is a system that allows users to instantly check the estimated value of a vehicle by taking a picture of the vehicle using a smartphone camera and sending it via a communication application (e.g., LINE).The system also incorporates an emotion engine that recognizes the user's emotions, and can improve the user experience by providing feedback and suggestions according to the user's emotions.

[1728] When a user takes a picture of a vehicle with their smartphone and sends it via LINE or other services, the image data is stored on LINE's server. The server receives the image using LINE's API and forwards it to an image recognition server. The image recognition server uses an image recognition algorithm (e.g., YOLO, ResNet) to identify the vehicle's model and grade. This identified information is sent back to the server, which then proceeds to the next step of processing.

[1729] The server then uses the identified vehicle model and grade information to send a message prompting the user to enter the year, mileage, and degree of damage. The user enters this information and replies. Using this information, the server references a database to obtain market price information. This information is then fed into an artificial intelligence model (e.g., XGBoost, TensorFlow-based model) to calculate the vehicle's estimated value.

[1730] The server also analyzes the text and voice messages entered by the user using an emotion engine (e.g., IBM Watson Tone Analyzer) and provides appropriate feedback and suggestions based on the analysis results. For example, if the user is feeling anxious, the server may send an additional support message to pique the user's interest.

[1731] Once this process is complete, the server generates a message informing the user of the calculated valuation amount and sends it via LINE. The system also stores the valuation results and user information in a database, periodically checks market trends, and notifies the user of the latest valuation amount.

[1732] Furthermore, the message also includes a link that allows users who are satisfied with the appraisal price to easily apply for the appraisal service. Simply clicking on this link will complete the application process.

[1733] As a concrete example, consider the case where a user takes a picture of a 2018 model vehicle and sends it via LINE. In this case, the server receives the image and uses an image recognition algorithm to identify the model and grade. The server then sends a message prompting the user to enter the year, mileage, and degree of damage. For example, prompts such as "Please tell us the year of your vehicle," "Please enter the mileage," and "Please tell us the degree of damage" are sent. Once the user provides this information, the server references the database and calculates the appraisal value using an artificial intelligence model. If the user feels anxious or dissatisfied, the emotion engine provides appropriate support.

[1734] Such a system not only allows users to easily and quickly check the estimated value of their vehicle, but also improves the user experience by providing feedback based on emotion recognition.

[1735] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1736] Step 1:

[1737] The user takes an image of the vehicle using a smartphone. The user opens a camera app and takes images of the vehicle from multiple angles. The input of this action is the smartphone camera, and the output is the image data of the captured vehicle.

[1738] Step 2:

[1739] The user takes a photo and sends it using a communication application (e.g., LINE). The user opens the LINE app and sends the image to a dedicated contact. The input to this process is the captured image data, and the output is the image data stored on the LINE server.

[1740] Step 3:

[1741] The server receives images sent by users via the LINE API. The server downloads the image data and transfers it to the image recognition server. The input to this process is the image data stored on the LINE server, and the output is the image data sent to the image recognition server.

[1742] Step 4:

[1743] The image recognition server uses an image recognition algorithm to identify the vehicle model and grade. The image recognition algorithm (e.g., YOLO, ResNet) analyzes the image data and identifies the vehicle model (e.g., Toyota Prius) and grade (e.g., Z grade). The input to this process is the image data sent to the image recognition server, and the output is the identified vehicle model and grade information.

[1744] Step 5:

[1745] Based on the identified information on the car model and grade, the server sends a message to the user to prompt them to enter the year, mileage, and degree of damage. For example, it sends messages such as "Please tell us the year of your car," "Please enter the mileage," and "Please tell us the degree of damage." The input of this process is the identified information on the car model and grade, and the output is a prompt message for the user.

[1746] Step 6:

[1747] The user replies to a message from the server and inputs information about the model year, mileage, and degree of damage. The user sends a response in text format. The input of this process is the text data returned by the user, and the output is the model year, mileage, and degree of damage information.

[1748] Step 7:

[1749] The server queries the database based on the information received from the user. The database is searched to obtain market price information. The input to this process is model year, mileage, and damage level information, and the output is market price data.

[1750] Step 8:

[1751] The server inputs the acquired market price data into an AI model to calculate the vehicle's valuation. The AI ​​model (e.g., XGBoost or TensorFlow-based model) analyzes the data and calculates the valuation. The input of this process is market price data, and the output is the valuation.

[1752] Step 9:

[1753] The server sends the text or voice message received from the user to the emotion engine to analyze the user's emotion. The emotion engine (e.g., IBM Watson Tone Analyzer) analyzes the text or voice data and identifies the user's emotion. The input of this process is the text or voice message from the user, and the output is the analyzed emotion data.

[1754] Step 10:

[1755] The server adjusts the feedback and suggestions based on the analysis results and sends a feedback message to the user. For example, if the user expresses dissatisfaction, it sends an additional support message. The input of this process is the analyzed emotion data, and the output is a feedback message.

[1756] Step 11:

[1757] The server prepares a message to notify the user of the calculated valuation amount and sends it via LINE. The notification message includes the valuation amount and a link to apply for the valuation service. The input for this process is the valuation amount, and the output is the notification message.

[1758] Step 12:

[1759] We provide a system in which the server saves the appraisal results and user information in a database, periodically checks market trends, and notifies the user of the latest appraisal amount. The input of this process is the appraisal results and user information, and the output is the data saved in the database.

[1760] Step 13:

[1761] If the user is satisfied with the valuation amount, they can simply click on the link to apply for the valuation service. Once the user clicks on the link, the application for the valuation service is completed. The input of this process is the user's click action, and the output is the completed application for the valuation service.

[1762] (Application example 2)

[1763] 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."

[1764] While existing vehicle appraisal systems allow users to easily and quickly check the appraisal value, they lack feedback and suggestions that take into account the user's emotional state, which hinders the user experience.Furthermore, food delivery services that use food images lack a means to make suggestions that reflect the user's emotions, which limits the comprehensiveness of their services.

[1765] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1766] In this invention, the server includes means for a user to take an image of a vehicle and send the image via a communication application, means for identifying the vehicle model and grade using an image recognition algorithm, means for prompting the user to input the year, mileage, and degree of damage after identifying the model and grade, means for referencing a database based on the input information and obtaining market price information, means for calculating an appraisal value based on the market price information, means for notifying the user of the calculated appraisal value, and means for recognizing the user's emotions and providing further feedback or suggestions based on the emotions. This not only allows the user to easily and quickly check the appraisal value, but also allows them to receive appropriate feedback or suggestions according to their emotions, significantly improving the user experience.

[1767] "Vehicle image" is visual data showing the exterior of a vehicle, taken by a user using a smartphone camera.

[1768] A "communication application" is a platform for users to send and receive images they have taken, including, for example, messaging apps and social media apps.

[1769] An "image recognition algorithm" is a calculation procedure for automatically extracting specific vehicle information (model and grade) from input image data.

[1770] "Vehicle model and grade" is classification information that indicates the vehicle category and detailed specifications.

[1771] "Model year" refers to the year in which the vehicle was manufactured.

[1772] "Distance traveled" refers to the total distance traveled by the vehicle, as measured by the odometer.

[1773] "Damage level" is information indicating the level of damage to the exterior of the vehicle.

[1774] A "database" is a collection of information for managing data such as vehicle market price information and past appraisal history in a certain format.

[1775] "Market price information" refers to market data relating to vehicle transaction prices, values, etc.

[1776] "Appraisal Value" is the estimated value of the vehicle calculated based on image recognition algorithms and databases.

[1777] "Emotion recognition" refers to analyzing the emotional state from text or voice input by the user.

[1778] "Feedback" refers to providing answers or suggestions based on the user's emotional state.

[1779] "Suggestions" are recommendations based on the user's input and emotional state to provide optimal actions or support.

[1780] The present invention combines a system that allows users to easily check the appraisal value of their vehicle using a smartphone with an emotion engine that recognizes the user's emotions. The specific system configuration and processing procedure for implementing this invention are described below.

[1781] System configuration

[1782] The invention mainly comprises the following elements:

[1783] 1. Smartphone: A device that allows users to take pictures of vehicles and food and provide information through text or voice input.

[1784] 2. Communication applications: These are applications that allow users to send and receive images and messages using platforms such as LINE.

[1785] 3. Image recognition algorithm: This algorithm is used to identify the make and model of a vehicle or food from a captured image. It uses existing image recognition services such as AWS Rekognition.

[1786] 4. Database: A database such as MongoDB or Firebase for storing and managing vehicle market price information, past appraisal history, and menu information.

[1787] 5. Artificial intelligence model: A program that calculates appropriate proposals and valuations based on vehicle and food information. The model is built using TensorFlow and other tools.

[1788] 6. Emotion Engine: A program that analyzes the user's emotions from text and voice and provides feedback and suggestions based on that state. Emotion analysis is performed using IBM Watson and Google Cloud Natural Language API.

[1789] Explanation of program processing

[1790] User submitted image:

[1791] Users can take pictures of vehicles or food using their smartphone camera and send them to a server via a communication application such as LINE, with the option to enter additional information via text or voice.

[1792] Image reception and analysis:

[1793] The server receives images sent by users through LINE's API and passes them to an image recognition algorithm, which then identifies the vehicle model and grade, or food.

[1794] User input:

[1795] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional information (year, mileage, degree of damage, etc.) The user provides the additional information by replying to this message.

[1796] Database lookup and valuation calculation:

[1797] Based on the information provided, the server references a database to obtain market price information and past appraisal history for the vehicle, and inputs this data into an AI model to calculate the vehicle's appraisal value and food recommendations.

[1798] Emotion Recognition and Feedback Regulation:

[1799] Text and voice messages from users are sent to the emotion engine, which analyzes their emotions and generates appropriate feedback and suggestions based on the analysis results.

[1800] Notifications and History Retention:

[1801] The calculated valuation amount and proposal details are notified to the user via LINE etc., and this information is simultaneously saved in a database. If the user is satisfied with the valuation, they are also provided with a link to easily apply for the service.

[1802] Specific examples

[1803] For example, consider the case where a user opens the "Emotional Food Order" app by typing "I feel a bit tired today." The emotion engine recognizes this "feeling of fatigue," and the AI ​​model suggests a menu with a relaxing effect (chicken salad and herbal tea). Based on this information, the app notifies the user, "You seem tired today. How about a relaxing chicken salad and herbal tea?"

[1804] Example prompt for a generative AI model:

[1805] User: Opens the food delivery app "Emotional Food Order" on his smartphone and types, "I feel kind of tired today."

[1806] Emotion engine: Recognizing "fatigue" from text.

[1807] AI model: Suggests chicken salad and herbal tea as a menu that is good for recovering from fatigue.

[1808] App: "You seem tired today. Would you like a relaxing chicken salad and herbal tea?"

[1809] This allows users to easily receive suggestions that correspond to their emotions, improving their service experience.

[1810] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1811] Step 1:

[1812] Users use their smartphones to take pictures of vehicles or food and send the images to a server via a communication application such as LINE.

[1813] Input: A user-taken image of a vehicle or food.

[1814] Output: Image data transferred to the server via LINE's API.

[1815] Step 2:

[1816] The server receives image data sent by the user through LINE's API and transfers the image to an image recognition algorithm.

[1817] Input: Image data received by the server.

[1818] Output: The image sent to the image recognition algorithm.

[1819] Step 3:

[1820] Using image recognition algorithms, the server identifies the make and model of the vehicle or the food item.

[1821] Input: The image sent to the image recognition algorithm.

[1822] Output: Identified car model and model, or food information.

[1823] Step 4:

[1824] Based on the identified information, the server sends a message to the smartphone prompting the user to enter additional required information (such as model year, mileage, and degree of damage).

[1825] Input: Identified car model and model, or food information.

[1826] Output: A message sent to your smartphone prompting you to enter additional information.

[1827] Step 5:

[1828] The user enters additional information (such as model year, mileage, and degree of damage) on their smartphone and sends this information to the server.

[1829] Input: Additional information entered by the user.

[1830] Output: Additional information sent to the server.

[1831] Step 6:

[1832] The server refers to the database based on the additional information sent by the user and obtains the relevant market price information and past appraisal history.

[1833] Input: User additional information and market price information in the database.

[1834] Output: Obtained market price information and past appraisal history.

[1835] Step 7:

[1836] The server inputs the acquired data into an artificial intelligence model to calculate a vehicle valuation or food recommendations.

[1837] Inputs: Market price information, past appraisal history, artificial intelligence model.

[1838] Output: Calculated valuation or proposal.

[1839] Step 8:

[1840] Text and voice from the user are sent to the emotion engine, and the server analyzes the user's emotions.

[1841] Input: text and voice data, emotion engine.

[1842] Output: Parsed emotional state.

[1843] Step 9:

[1844] Based on the analysis results of the emotion engine, the server generates appropriate feedback and suggestions and notifies the user via LINE or other means.

[1845] Input: Parsed emotional state, template for feedback and suggestions.

[1846] Output: Feedback and suggestions that are communicated to the user.

[1847] Step 10:

[1848] When the user checks the notified estimated price and proposal content and applies for the service, the server provides a link for easily applying for the service.

[1849] Input: What to notify the user.

[1850] Output: A link to apply for the service.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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.

[1856] 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.

[1857] 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).

[1858] 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.

[1859] 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."

[1860] 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.

[1861] 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).

[1862] 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.

[1863] 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.

[1864] 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.

[1865] 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.

[1866] 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.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] The following is further disclosed regarding the above embodiment.

[1873] (Claim 1)

[1874] means for a user to take an image of the vehicle and transmit the image via a communication application;

[1875] means for identifying the make and model of the vehicle using an image recognition algorithm;

[1876] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[1877] a means for referencing a database based on the input information and obtaining market price information;

[1878] A means of calculating the assessed value based on market price information;

[1879] a means for notifying the user of the calculated valuation amount;

[1880] A system including:

[1881] (Claim 2)

[1882] 10. The system of claim 1, further comprising means for storing past appraisal history, periodically checking market trends, and notifying the user of the latest appraisal amount.

[1883] (Claim 3)

[1884] 10. The system of claim 1, further comprising: means for providing a link for a user to easily apply for appraisal services.

[1885] "Example 1"

[1886] (Claim 1)

[1887] means for a user to take an image of the vehicle and transmit the image via a communications application;

[1888] a means for identifying the make and model of the vehicle using an image recognition program;

[1889] means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[1890] A means for referencing an information holder based on the input information and acquiring market evaluation information;

[1891] A means for calculating the assessed value based on market valuation information;

[1892] a means for notifying the user of the calculated valuation price;

[1893] A system including:

[1894] (Claim 2)

[1895] 10. The system of claim 1, further comprising means for storing past appraisal history, periodically checking market trends, and notifying the user of the latest appraisal price.

[1896] (Claim 3)

[1897] 10. The system of claim 1, further comprising: means for providing a link for a user to easily apply for appraisal services.

[1898] "Application Example 1"

[1899] (Claim 1)

[1900] means for a user to take an image of the vehicle using an image capture device and transmit the image via a communication application;

[1901] means for identifying the make and model of the vehicle using an image recognition algorithm;

[1902] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[1903] a means for referencing a database based on the input information and obtaining market price information;

[1904] A means of calculating the appraisal value based on market price information,

[1905] a means for notifying the user of the calculated valuation;

[1906] A means for automatically displaying an estimated value when a user posts a vehicle for sale advertisement;

[1907] A system including:

[1908] (Claim 2)

[1909] 10. The system of claim 1, further comprising means for storing past valuation history, periodically checking market trends, and notifying the user of updated valuations.

[1910] (Claim 3)

[1911] 10. The system of claim 1, further comprising: means for providing a link for a user to conveniently process an appraisal service.

[1912] "Example 2: Combining Emotion Engines"

[1913] (Claim 1)

[1914] means for a user to take an image of the vehicle and transmit the image via a communication application;

[1915] means for identifying the make and model of the vehicle using an image recognition algorithm;

[1916] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[1917] a means for referencing a database based on the input information and obtaining market price information;

[1918] A means of calculating the assessed value based on market price information;

[1919] a means for notifying the user of the calculated valuation amount;

[1920] emotion engine means for recognizing the user's emotions and adjusting or suggesting feedback based thereon;

[1921] A system including:

[1922] (Claim 2)

[1923] 10. The system of claim 1, further comprising means for storing past appraisal history, periodically checking market trends, and notifying the user of the latest appraisal amount.

[1924] (Claim 3)

[1925] 10. The system of claim 1, further comprising: means for providing a link for a user to easily apply for appraisal services.

[1926] "Application example 2 when combining emotion engines"

[1927] (Claim 1)

[1928] means for a user to take an image of the vehicle and transmit the image via a communication application;

[1929] means for identifying the make and model of the vehicle using an image recognition algorithm;

[1930] A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade;

[1931] a means for referencing a database based on the input information and obtaining market price information;

[1932] A means of calculating the assessed value based on market price information;

[1933] a means for notifying the user of the calculated valuation amount;

[1934] A means to recognize the user's emotions and provide further feedback or suggestions based on those emotions;

[1935] A system including:

[1936] (Claim 2)

[1937] 10. The system of claim 1, further comprising means for storing past appraisal history, periodically checking market trends, and notifying the user of the latest appraisal amount.

[1938] (Claim 3)

[1939] 10. The system of claim 1, further comprising: means for providing a link for a user to easily apply for appraisal services.

[1940] (Claim 4)

[1941] The system according to claim 1, further comprising means for analyzing text or voice input by a user and making product suggestions according to the user's emotions. [Explanation of symbols]

[1942] 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. means for a user to take an image of the vehicle and transmit the image via a communication application; means for identifying the make and model of the vehicle using an image recognition algorithm; A means for allowing a user to input the year, mileage, and degree of damage after identifying the vehicle model and grade; a means for referencing a database based on the input information and obtaining market price information; A means of calculating the assessed value based on market price information; a means for notifying the user of the calculated valuation amount; A system including:

2. 2. The system of claim 1, further comprising means for storing past appraisal history, periodically checking market trends, and notifying the user of the latest appraisal amount.

3. 10. The system of claim 1, further comprising means for providing a link for a user to easily apply for the appraisal service.

Images