system

Abstract

We provide the system. [Solution] A means for providing an information input unit for inputting vehicle information, A means of collecting vehicle information and transmitting real-time data to a server, Using the collected information, a means of integrating historical data and market trends, and analyzing the vehicle's condition using a generative model, A means of predicting the market value and optimal selling time of a vehicle based on analysis results and market information, A means of presenting users with predicted value and timing of sale, and providing them with potential candidates. A means of notifying users when the formula value they have set matches market conditions, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the sale and purchase of vehicles, it is a complex process to appropriately grasp the market value of a privately-owned vehicle and aim for a high-price sale. In particular, for general users lacking knowledge about market trends, it is not easy to determine the optimal selling time or select a reliable buyer. Even if information is collected from multiple information sources, there is a problem that the information is scattered and difficult to manage and compare in a unified manner. As a result, users may miss opportunities to obtain profits above the market price or risk suffering losses by contracting with inappropriate dealers.

Means for Solving the Problems

[0005] This invention provides a system that acquires vehicle images and related information via a user interface and analyzes the vehicle's external condition using a generative model. This system integrates the obtained analysis results with the latest market data and includes means for predicting the vehicle's market value and optimal selling time using a drive algorithm. Furthermore, it visualizes these prediction results for the user, presents potential buyers, and notifies the user when the selling price matches market conditions according to user-defined criteria. This facilitates transaction decisions and enables users to sell at a high price. Additionally, it has a function to calculate and provide a recommendation score to the user based on the user's evaluation of potential buyers.

[0006] A "user interface for entering vehicle information" refers to the screens and functions that users use to input information about their vehicle into the system.

[0007] A "generative model" is an algorithm that includes AI technology to generate new data based on specific data and then analyze or evaluate that data.

[0008] "Means for analyzing the condition of a vehicle" refers to a method or apparatus for processing information regarding the appearance and performance of a vehicle and evaluating its condition.

[0009] "Means of forecasting based on market data" refers to methods or devices that use data on market trends and prices to estimate the current value of a vehicle and the appropriate time to sell it.

[0010] "Means of displaying prediction results to the user" refers to display devices or functions that present the calculated results in a format that is easy for the user to understand.

[0011] "Means of presenting potential buyers" refers to a method or function that allows users to select and propose appropriate trading partners when considering selling their property.

[0012] "Providing notifications" refers to a means or function that informs the user when certain conditions are met.

[0013] "User evaluations of potential buyers" refers to data collected from evaluations and feedback given by users who have previously sold vehicles to those buyers.

[0014] The "function to calculate and display recommendation scores" is a function that calculates the reliability and suitability of potential buyers based on collected evaluation data and presents the results to the user. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0018] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

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

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0032] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for properly evaluating the market value of privately owned vehicles and for buying and selling them at high prices. This system includes a user interface, a generative model, market data collection means, analysis and prediction means, a notification function, and a buyer suggestion function.

[0037] User actions

[0038] Users utilize a dedicated application or web interface provided via their smartphone or computer. They take photos of their vehicle and input basic information such as make, model, year of manufacture, and mileage. This information is transmitted to the system and used as initial data for analysis.

[0039] Server operation

[0040] The server inputs image data of the vehicle received from the user into a generative model and analyzes the vehicle's external condition. This analysis includes the vehicle's color, scratches, dents, etc. The server also identifies the vehicle model and predicts its value by comparing it with past sales information and market trends in the database.

[0041] For example, if the server receives vehicle data such as "sedan type, manufactured in 2015, 50,000 km driven," the server will analyze the vehicle's condition and output an analysis result such as "no visible damage, 50,000 km driven." Based on this, the market value of this vehicle is estimated to be, for example, 1.2 million yen.

[0042] Terminal operation

[0043] The terminal displays analysis results and value prediction information received from the server to the user. The user makes a transaction decision based on this information. A list of reliable buyers as potential buyers is also displayed. Furthermore, if the desired selling price set by the user matches market conditions, the user is notified, and they can obtain the information necessary to decide whether to sell.

[0044] This system allows users to understand the market value of their vehicle in real time and determine the optimal time to sell. Compared to conventional methods, it enables a more efficient and reliable buying and selling process.

[0045] The following describes the processing flow.

[0046] Step 1: The user uploads images and information.

[0047] The user takes multiple photos of their vehicle using a dedicated application and enters basic information such as the vehicle make, model year, and mileage. This data is then transmitted to a server via the internet.

[0048] Step 2: The server receives the image data.

[0049] The server receives image data and vehicle information sent by the user. This data is then prepared to be input into the analysis model.

[0050] Step 3: The server analyzes the image.

[0051] The server's generation model analyzes the vehicle's external condition from received image data. Specifically, it checks for damage, paint condition, and component deterioration, and generates a numerical evaluation based on the results.

[0052] Step 4: The server collects market data.

[0053] The server uses external databases and APIs to collect data to estimate the market value of the analyzed vehicle. This data includes recent sales prices and market trends of similar vehicles.

[0054] Step 5: The server predicts value.

[0055] The server integrates image analysis results with market data and uses machine learning algorithms to predict the vehicle's current market value and optimal selling time. These results are output as a predicted price and recommended selling time.

[0056] Step 6: The device displays information.

[0057] The terminal displays value predictions and recommendations sent from the server to the user. This includes the predicted market value, the optimal timing for selling, and a list of recommended buyers. The user uses this information to make decisions about selling their vehicle in the future.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] For many individual users, accurately assessing a vehicle's market value and determining the optimal time to sell is difficult. Traditional methods are time-consuming and often fail to provide reliable valuations. Finding a trustworthy buyer has also been a challenge.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for using a generated AI model to identify the appearance of the vehicle using the received photographic data, means for calculating the marketability and timing of the transaction of the movable property based on the identification result and market trend data, and means for suggesting potential buyers based on the calculation result. This enables the user to quickly and accurately understand the market value of their vehicle and to conduct an optimal transaction with a recommended buyer.

[0063] "Data receiving means for inputting user information" refers to a system that allows users to directly input information about their own vehicle, and is an interface for collecting vehicle details.

[0064] "A means of identifying the exterior appearance using a generated AI model based on received vehicle image data" refers to a process that uses artificial intelligence to determine the external condition of a vehicle based on images of the vehicle provided by the user.

[0065] "Means for calculating the marketability and timing of transactions of movable property based on identification results and market trend data" refers to a function that analyzes the condition of a vehicle and market data to predict the appropriate time to sell and its market value.

[0066] "Means of displaying calculation results to the user" refers to a method of clearly communicating predictive information obtained through analysis to the user, enabling the user to visually confirm the results.

[0067] "A method for proposing potential buyers based on calculation results" refers to a system that recommends highly reliable buyers to users based on market value predictions and presents them as specific buyers.

[0068] "Notifying the user when the sales price matches the conditions set by the user" means that when the sales conditions set in advance by the user are met, the system immediately informs the user of that information.

[0069] "Accumulating user feedback on potential buyers and calculating and displaying recommendation levels" refers to the process of collecting user evaluation data for each buyer and calculating recommendation levels to reflect in future proposals.

[0070] This invention is a system that accurately assesses the market value of privately owned vehicles and enables efficient sales. The system mainly consists of user terminals and a server, with each component working in coordination.

[0071] Users operate a specialized application using their smartphones or computers. Through the application, they input vehicle image data and provide information such as vehicle make, model year, and mileage. The user's device then formats this information and sends it to the server.

[0072] The server inputs the received image data into an advanced generative AI model to analyze the vehicle's appearance. This generative AI model incorporates image analysis technology and has the ability to automatically identify the vehicle's condition and characteristics. Specifically, it recognizes scratches, dents, and the vehicle's color, and outputs this information as analysis results.

[0073] The analysis results are cross-referenced with a market trend database by the server. This database includes historical vehicle sales information and current market trends, and the server uses this to predict the marketability of movable assets and the optimal timing for transactions. Machine learning algorithms are used in this prediction process to enable more accurate assessments.

[0074] Next, the terminal displays predictive information sent from the server to the user. This allows the user to visualize the market value of their vehicle and the optimal timing for selling, supporting efficient decision-making. Furthermore, the server suggests suitable buyers, providing the user with options. The system also includes a notification function that matches the user's desired selling price with market data.

[0075] As a concrete example, consider a scenario where a user enters the prompt message, "My car is a 2018 SUV with 30,000 km on the odometer. What is its market value?" Based on this information, the server performs a detailed market analysis and presents a market value of 2 million yen along with a list of reliable buyers.

[0076] Thus, the present invention helps users obtain accurate market information about their vehicles and provides an efficient and reliable selling process.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users input photographic data of their vehicles via a dedicated application using a smartphone or computer. This includes basic information such as vehicle make, model year, and mileage. The entered information is formatted and sent to the server. The user's actions are responsible for accurately collecting detailed information about the vehicle.

[0080] Step 2:

[0081] The server inputs the captured image data and vehicle information received from the user into a generating AI model. This model analyzes the vehicle's exterior condition, identifying features such as scratches, dents, and vehicle color. The output of the analysis is detailed information about the vehicle's exterior and serves as the basis for the next processing step. By utilizing the generating AI model, a detailed understanding of the vehicle's condition is achieved.

[0082] Step 3:

[0083] The server compares the analysis results with a market trend database. This database contains historical sales information and market trends, which the server uses to calculate market potential and the optimal trading timing. Based on the analysis data as input, the server outputs market value using statistical models and machine learning algorithms. This calculation process provides accurate market information that should be offered to the user.

[0084] Step 4:

[0085] The terminal displays market value and sale timing information transmitted from the server to the user. The output information is visualized and provided in a format that the user can directly check. This allows the user to check the information as a concrete action to make trading decisions easier.

[0086] Step 5:

[0087] The server sends a notification to the terminal when the user's desired selling price matches the market value. It also suggests reliable potential buyers, and a list of these is presented to the user. Presenting potential buyers is a specific action to provide the user with options and support quick decision-making.

[0088] This series of processes allows users to accurately understand the market value of their vehicles and experience an efficient buying and selling process.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] In recent years, with the increasing adoption of autonomous vehicles, there has been a growing need for systems that efficiently manage the market value of vehicles and propose the optimal time for sale. However, conventional technologies have not adequately provided value assessments based on real-time vehicle data or suggested maintenance tailored to usage conditions. This invention aims to solve these problems and provide a more accurate and timely assessment of market value.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for collecting vehicle information and transmitting real-time data, means for integrating the collected information and analyzing the vehicle's condition using a generative model, and means for predicting market value and providing maintenance recommendations based on the analysis results. This enables advanced market value assessment and maintenance recommendations for autonomous vehicles.

[0094] "Vehicle information" refers to basic information related to a vehicle, specifically data such as make, model, year of manufacture, mileage, and exterior images.

[0095] The "information input section" is a function that provides an interface for users to input vehicle information, and can be implemented on a smartphone app or computer.

[0096] "Real-time data" refers to data that indicates the current state of a vehicle while it is in operation, and is constantly updated through sensors.

[0097] A "generative model" is a computational model used to analyze the state of a vehicle, and may include machine learning or deep learning techniques.

[0098] "Analysis results" refer to evaluation data on the vehicle's condition obtained using a generative model, indicating the presence or absence of damage and the degree of vehicle deterioration.

[0099] "Market value" refers to the assessed value of a vehicle when it is traded in the market, and is influenced by past sales data and the balance of supply and demand in the market.

[0100] An "information display unit" is a device that has the function of displaying analysis results and other related information to the user, and is installed on the vehicle's console, smartphone, etc.

[0101] A "maintenance recommendation" refers to a proposal to the user of necessary measures for maintaining the vehicle, and specific actions are indicated based on the vehicle's deterioration status.

[0102] This invention is a system that efficiently evaluates the market value of a privately owned vehicle and proposes the optimal time to sell it. The system acquires vehicle information and analyzes real-time data to understand the vehicle's condition. This allows it to accurately predict the vehicle's market value and propose the best time to sell or maintenance to the user.

[0103] The server processes vehicle information received from users and analyzes the vehicle's condition using a generated AI model. Specifically, it evaluates the external condition and degree of deterioration from input data such as vehicle images and mileage. This analysis utilizes machine learning and deep learning technologies, and the results are combined with market information in a database to calculate market value. The software used includes deep learning frameworks such as TENSORFLOW® and Keras.

[0104] The terminal visualizes the analysis results received from the server for the user, clearly indicating the vehicle's market value, recommended timing for sale, and maintenance recommendations. This information is displayed on a smartphone or the main console of the autonomous vehicle, allowing the user to make decisions regarding sale or maintenance based on it.

[0105] Users can monitor market fluctuations in real time through the system and seek the optimal timing for transactions that suit their intentions and conditions. For example, when returning home from a family trip on a holiday, users can update the newly acquired mileage and vehicle condition and understand the subsequent changes in market value.

[0106] An example of a prompt for the generating AI model is, "Use the image and mileage of this 2016 mass-market vehicle to predict its market value." In this way, users can accurately and efficiently manage the market value of their own vehicles.

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] Users use their smartphones or computers to input images of their vehicle and related information (vehicle type, year of manufacture, mileage) into the information input section. This input data is collected as initial data for analyzing the vehicle's condition.

[0110] Step 2:

[0111] The server inputs images of vehicles received from users into a deep learning model (generative AI model) to analyze the vehicle's external condition. Using the input image data, it performs data calculations on external information, including scratches and color changes, and generates analysis results that evaluate the degree of vehicle deterioration.

[0112] Step 3:

[0113] Based on the analysis results, the server searches the market database for past transaction data and current market trends for similar vehicles, and combines them to predict the vehicle's market value and optimal selling time. This allows it to output an appropriate market value and other relevant information for the input data.

[0114] Step 4:

[0115] The server notifies the user's device of the predicted market value and the best time to sell. Furthermore, it includes maintenance recommendations based on the vehicle's usage, providing the user with specific actions they need to take.

[0116] Step 5:

[0117] The terminal displays the received analysis results and recommendations to the user in an easy-to-understand interface. Based on this information, the user can decide when to sell or maintain the vehicle and determine the next course of action.

[0118] The data is processed as a prompt for the generating AI model, in the form of, "Use the image and mileage of this 2016 mass-market vehicle to predict its market value."

[0119] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0120] The present invention is a system for facilitating and optimizing the buying and selling of vehicles, and in particular, in addition to evaluating the market value of vehicles, it has the function of recognizing user emotions and dynamically adjusting the interface and information provision based on them. Specific embodiments for carrying out this invention are shown below.

[0121] User actions

[0122] Users launch a dedicated application on their smartphone or computer and enter information to sell their vehicle. This includes taking photos of the vehicle and entering the make, model, year of manufacture, and mileage. Once the user has finished entering the information, the application sends this data to the server.

[0123] Server operation

[0124] The server performs multiple functions based on data received from the user. Image data is analyzed by a generative model to evaluate the vehicle's condition (presence or absence of damage, degree of cosmetic deterioration, etc.). This evaluation result is integrated with market data to predict the current market value and optimal selling time.

[0125] Furthermore, the server utilizes an emotion engine to recognize emotions from user input and interactions. This emotion data is used to dynamically adjust the user interface, providing information best suited to the user's psychological state. For example, if the user is feeling anxious, more detailed information and support options will be presented.

[0126] Terminal operation

[0127] The device displays market value predictions, recommended selling times, and sentiment-based interface adjustments to the user, all derived from the server. When the user is ready to sell, suitable buyer candidates are presented, along with other users' ratings for those buyers. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0128] Specific example

[0129] For example, suppose a user enters vehicle information such as "compact car, 2018 model, 30,000km mileage." The server analyzes this information, predicts a market value of 1.5 million yen, and notifies the user that the optimal time to sell is three months from now. At the same time, the emotion engine recognizes the user's "sense of security" from their input and provides information through a simple interface. Conversely, if the user's emotion is "anxiety," an interface is generated that offers more options and allows for immediate responses to questions.

[0130] In this way, this system supports the optimization of buying and selling based on market trends while taking into account the user's psychological state.

[0131] The following describes the processing flow.

[0132] Step 1: The user enters the information.

[0133] The user launches an application on their device, takes a photo of their vehicle, and then inputs the vehicle make, model year, and mileage. The user's emotional state is analyzed in real time by an emotion engine while they are using the input interface.

[0134] Step 2: The device sends the data.

[0135] The device sends image data and input information collected from the user to the server. This data includes photographs taken, vehicle information, and emotional states detected by the emotion engine.

[0136] Step 3: The server analyzes the image data.

[0137] The server inputs the received image data into a generative model and performs image analysis. This allows it to evaluate the detailed condition of the vehicle's exterior, including whether or not there is damage and whether or not the exterior has deteriorated.

[0138] Step 4: The server collects market data.

[0139] The server collects market prices and historical sales data for similar vehicles from external databases and APIs. The collected data is integrated with the results of image analysis to predict the vehicle's current market value and the optimal time to sell it.

[0140] Step 5: The server makes emotion-based adjustments.

[0141] The server's emotion engine dynamically adjusts the interface presentation and level of detail based on the user's emotional state. For example, if a sense of security is detected, the display is simplified; if anxiety is detected, more detailed information and guidance are provided.

[0142] Step 6: The device displays the prediction results.

[0143] Based on predictions from the server and interface adjustments, the terminal displays market value assessments and recommended selling times to the user. It also presents potential buyers and other users' ratings for them.

[0144] Step 7: The user makes a decision.

[0145] Based on the information provided, the user decides whether or not to sell the vehicle. If the sale conditions match market conditions, the user receives a notification and can begin the actual sale process.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] Conventional mobile vehicle trading systems have made it difficult to accurately assess the market value of mobile vehicles and predict the optimal selling timing. Furthermore, they have not adequately improved the user experience due to a lack of information provision that takes into account the user's psychological state. This has resulted in a challenge in ensuring that users can conduct transactions with confidence.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes means for inputting mobile object information via an input device, means for analyzing the state of the mobile object using a generating AI model based on the input image information of the mobile object, and means for predicting the market value and timing of sale of the mobile object based on the analysis results and market information. This enables accurate market value assessment of the mobile object and the provision of optimal information tailored to the user's psychological state.

[0151] An "input device" refers to a device used by users to input mobile information, and generally includes hardware such as smartphones and computers.

[0152] "Mobile vehicles" primarily refer to land vehicles used to transport people and goods, such as automobiles, and are a type of item that is bought and sold.

[0153] "Image information" refers to visual data captured to specifically show the state of a moving object, and is digital data acquired by cameras, smartphones, etc.

[0154] A "generative AI model" refers to a machine learning algorithm or system used to analyze data, specifically a technique for extracting features from images and evaluating their state.

[0155] "Analyzing the condition" refers to the process of determining the current state, deterioration, and presence or absence of damage of a moving object based on image information and other data.

[0156] "Market information" refers to data about the current market situation, including information that shows the balance of supply and demand and price trends.

[0157] "Market value" refers to the price at which a moving object can be traded in the market at the present time, and is the benchmark price for buying and selling that is estimated from the information provided.

[0158] "Selling timing" refers to the optimal time to sell a mobile vehicle on the market, and is determined based on factors such as the season and market demand.

[0159] "Psychological state" refers to the user's current emotions and mental condition, and is a factor considered when adjusting the information the system dynamically provides.

[0160] "Information provision" refers to the act of displaying necessary information to users in a timely manner based on analyzed data and prediction results.

[0161] This invention is a system for efficiently buying and selling mobile objects, and in particular, it accurately evaluates the market value of mobile objects and provides information that reflects the psychological state of users during the buying and selling process. Specific embodiments for implementing this invention are shown below.

[0162] User actions

[0163] Users use a smartphone or computer application to input vehicle information such as make, model, year of manufacture, and mileage. This application also allows for image data input, enabling users to upload images that accurately capture the vehicle's current condition. An example of a prompt message might be, "Analyze the vehicle image to assess damage and its market value."

[0164] Server operation

[0165] After receiving data from the user, the server uses a generative AI model to analyze the image data. Specifically, the process involves evaluating the presence or absence of damage and the degree of deterioration of the mobile object based on the image data provided by the user. This evaluation is performed by a generative AI model, which is an AI algorithm, and the market value of the mobile object is calculated based on the information obtained, in conjunction with current market information.

[0166] The server also analyzes user input and operation patterns and uses an emotion engine to recognize the user's psychological state. Based on this recognized emotional information, it dynamically adjusts the user interface to provide the user with the most relevant information. For example, if the user is experiencing anxiety, a detailed explanation of their state and additional support options will be displayed.

[0167] Terminal operation

[0168] The device displays to the user market value predictions obtained from the server, recommended selling times, and sentiment-based information. When the user decides to sell, it presents suitable buyer candidates and displays ratings from other users. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0169] This system not only streamlines the buying and selling process for mobile vehicles but also enhances user satisfaction. This invention, through the use of new technology, provides a novel method for supporting complex buying and selling decision-making.

[0170] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0171] Step 1:

[0172] Users launch the application on their smartphone or computer and enter information about their vehicle. This information includes the vehicle make, model year, mileage, and any captured image data. The purpose of this input is to provide an accurate market value assessment and predict the best time to sell the vehicle. The entered data is then sent to a server.

[0173] Step 2:

[0174] The server analyzes the received vehicle data. The generating AI model then performs a detailed evaluation of the vehicle's appearance based on the image data provided by the user. This evaluation process utilizes the prompt message "Analyze the vehicle's appearance and assess whether it is damaged" to run the generating AI model. The output provides information on the presence or absence of damage and the degree of deterioration.

[0175] Step 3:

[0176] The server combines acquired damage and deterioration information with the latest market data. Referencing market data, it calculates the vehicle's market value and the optimal time to sell. Data processing includes market price trend analysis and comparison with other similar vehicles. The output results are specific market value and recommended selling timing.

[0177] Step 4:

[0178] The server uses an emotion engine to recognize the user's psychological state through user input data. The input consists of user actions and text data, which the emotion engine processes. The output is emotional data such as reassurance, interest, or anxiety. Based on this output, the user interface is dynamically adjusted.

[0179] Step 5:

[0180] The terminal displays market value information, recommended selling times, and sentiment-based interface adjustments sent from the server to the user. Specifically, the terminal presents recommendations in an easy-to-understand visual format, encouraging the user to prepare for the sale.

[0181] Step 6:

[0182] Users make their selling decisions based on potential buyers and other user reviews displayed on their device. Once a user sets their selling conditions, the device immediately sends a notification if the conditions match those of the market. This allows users to start the selling process at the optimal time.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] Traditional vehicle trading systems, while offering methods to assess the market value of vehicles and support sales, lacked systems that considered user emotions and the unique operational information of autonomous vehicles. Furthermore, they struggled to dynamically optimize vehicle condition and maintenance, leading to missed opportunities for improved usability and sales.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes means for providing a user interface for inputting vehicle information, means for analyzing the vehicle's condition using a generated model based on image data of the vehicle received from the user, means for predicting the vehicle's market value and timing of sale based on the analysis results and market information, means for dynamically adjusting the interface and information provision according to the user's psychological state using emotion recognition, and means for analyzing vehicle operation information related to autonomous driving technology to improve vehicle maintenance and environmental adjustments. This enables the provision of appropriate information and the creation of a comfortable environment in accordance with the user's emotions, as well as efficient operation management of autonomous vehicles and maximization of their market value.

[0188] "Vehicle information" refers to specific data about a vehicle that a user enters when selling it, including the make and model, year of manufacture, mileage, and image data of the vehicle.

[0189] "User interface" refers to the system's operation screens and input devices used by users to input vehicle information and check results.

[0190] A "generated model" refers to an algorithm or program created using machine learning or artificial intelligence techniques to analyze received data and provide insights and recommendations.

[0191] "Emotion recognition" is a technology in which a computer system analyzes a user's emotions from data such as facial expressions and voice, and determines those emotions.

[0192] "Autonomous driving technology" refers to a set of sensors, algorithms, and control systems that enable a vehicle to operate safely without human intervention, and includes the collection and analysis of data related to its operation.

[0193] "Environmental adjustment" refers to the dynamic control of the vehicle's interior lighting, air conditioning, seat arrangement, and even the entertainment system, in accordance with the user's psychological state and external environment.

[0194] The system for implementing this invention streamlines the vehicle buying and selling process and improves user comfort. The system consists of three main components: a user interface, a server, and a terminal.

[0195] The server runs on devices such as NVIDIA Jetson Xavier and receives vehicle image data, running software to analyze its condition using a generative AI model. Specifically, the software used includes an AI model based on TensorFlow and OpenCV for image processing. This makes it possible to automatically evaluate whether a vehicle is damaged and the degree of deterioration of its appearance.

[0196] By integrating analysis results with market information, the server predicts the market value of a vehicle and the optimal time to sell it. Furthermore, it incorporates an emotion recognition engine that analyzes the user's psychological state from their input and interactions, allowing for dynamic adjustments to the interface and information provided. For example, if a user is feeling anxious, detailed information and support options are provided.

[0197] Based on data obtained from the server, the terminal displays market value predictions, recommended selling times, and potential best buyers to the user. It also displays sentiment-based interface adjustments, enabling users to make quick decisions based on this information.

[0198] As a concrete example, consider a case where a user inputs information such as "SUV, 2020 model, 15,000 km driven." The server analyzes the image data to check the vehicle's condition, integrates it with market data to predict a market value of 3 million yen, and suggests the optimal time to sell it as two months from now. At the same time, if the emotion engine recognizes "peace of mind," a simple and intuitive interface is provided. Conversely, if the user indicates "anxiety," a wealth of information and options are presented.

[0199] The generative AI model performs various analyses based on prompt messages. Prompt messages used include "Provide data samples to predict the market value of the vehicle" and "Provide suggestions for designing an interface that instills a sense of security."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user launches a dedicated application and enters vehicle information. This information includes a vehicle image, make and model, year of manufacture, and mileage. This data is then sent to the server. It is important that the input data is organized, and high-resolution image data is desirable.

[0203] Step 2:

[0204] The server inputs the received vehicle image data into a generating AI model to analyze the vehicle's condition. Specifically, it uses OpenCV to process the images and analyze the presence or absence of damage and deterioration of the exterior. As a result of the analysis, the vehicle's condition is quantified, providing basic information that can be integrated with other market data.

[0205] Step 3:

[0206] The server integrates analysis results and market data, and uses a generated AI model to predict the market value and optimal selling time for a vehicle. Market data includes historical sales prices, demand forecasts, and economic indicators. The prediction results are output in the form of the vehicle's market value and the optimal selling time.

[0207] Step 4:

[0208] The server uses an emotion recognition engine to analyze the user's emotions from the input data and subsequent interactions. This involves using natural language processing and image analysis techniques to read emotions from the user's facial expressions and text. The analysis results are output as tags such as "feeling secure" and "feeling anxious."

[0209] Step 5:

[0210] The terminal dynamically adjusts its interface based on market value predictions, selling timing, and sentiment analysis results received from the server. For example, if "security" is detected, it provides information in a simple layout; if "anxiety" is detected, it creates an interface that provides detailed information and additional options.

[0211] Step 6:

[0212] Users make selling decisions based on predictive information and interfaces presented on their devices. The system displays optimal buyer candidates, notification settings for when conditions are met, and other users' ratings of potential buyers, providing information to support the decision-making process. This allows users to proceed with the selling process with confidence.

[0213] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0220] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0224] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0226] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0229] This invention is a system for properly evaluating the market value of privately owned vehicles and for buying and selling them at high prices. This system includes a user interface, a generative model, market data collection means, analysis and prediction means, a notification function, and a buyer suggestion function.

[0230] User actions

[0231] Users utilize a dedicated application or web interface provided via their smartphone or computer. They take photos of their vehicle and input basic information such as make, model, year of manufacture, and mileage. This information is transmitted to the system and used as initial data for analysis.

[0232] Server operation

[0233] The server inputs image data of the vehicle received from the user into a generative model and analyzes the vehicle's external condition. This analysis includes the vehicle's color, scratches, dents, etc. The server also identifies the vehicle model and predicts its value by comparing it with past sales information and market trends in the database.

[0234] For example, if the server receives vehicle data such as "sedan type, manufactured in 2015, 50,000 km driven," the server will analyze the vehicle's condition and output an analysis result such as "no visible damage, 50,000 km driven." Based on this, the market value of this vehicle is estimated to be, for example, 1.2 million yen.

[0235] Terminal operation

[0236] The terminal displays analysis results and value prediction information received from the server to the user. The user makes a transaction decision based on this information. A list of reliable buyers as potential buyers is also displayed. Furthermore, if the desired selling price set by the user matches market conditions, the user is notified, and they can obtain the information necessary to decide whether to sell.

[0237] This system allows users to understand the market value of their vehicle in real time and determine the optimal time to sell. Compared to conventional methods, it enables a more efficient and reliable buying and selling process.

[0238] The following describes the processing flow.

[0239] Step 1: The user uploads images and information.

[0240] The user takes multiple photos of their vehicle using a dedicated application and enters basic information such as the vehicle make, model year, and mileage. This data is then transmitted to a server via the internet.

[0241] Step 2: The server receives the image data.

[0242] The server receives image data and vehicle information sent by the user. This data is then prepared to be input into the analysis model.

[0243] Step 3: The server analyzes the image.

[0244] The server's generation model analyzes the vehicle's external condition from received image data. Specifically, it checks for damage, paint condition, and component deterioration, and generates a numerical evaluation based on the results.

[0245] Step 4: The server collects market data.

[0246] The server uses external databases and APIs to collect data to estimate the market value of the analyzed vehicle. This data includes recent sales prices and market trends of similar vehicles.

[0247] Step 5: The server predicts value.

[0248] The server integrates image analysis results with market data and uses machine learning algorithms to predict the vehicle's current market value and optimal selling time. These results are output as a predicted price and recommended selling time.

[0249] Step 6: The device displays information.

[0250] The terminal displays value predictions and recommendations sent from the server to the user. This includes the predicted market value, the optimal timing for selling, and a list of recommended buyers. The user uses this information to make decisions about selling their vehicle in the future.

[0251] (Example 1)

[0252] Next, we will describe Example 1. 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."

[0253] For many individual users, accurately assessing a vehicle's market value and determining the optimal time to sell is difficult. Traditional methods are time-consuming and often fail to provide reliable valuations. Finding a trustworthy buyer has also been a challenge.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes means for using a generated AI model to identify the appearance of the vehicle using the received photographic data, means for calculating the marketability and timing of the transaction of the movable property based on the identification result and market trend data, and means for suggesting potential buyers based on the calculation result. This enables the user to quickly and accurately understand the market value of their vehicle and to conduct an optimal transaction with a recommended buyer.

[0256] "Data receiving means for inputting user information" refers to a system that allows users to directly input information about their own vehicle, and is an interface for collecting vehicle details.

[0257] "A means of identifying the exterior appearance using a generated AI model based on received vehicle image data" refers to a process that uses artificial intelligence to determine the external condition of a vehicle based on images of the vehicle provided by the user.

[0258] "Means for calculating the marketability and timing of transactions of movable property based on identification results and market trend data" refers to a function that analyzes the condition of a vehicle and market data to predict the appropriate time to sell and its market value.

[0259] "Means of displaying calculation results to the user" refers to a method of clearly communicating predictive information obtained through analysis to the user, enabling the user to visually confirm the results.

[0260] "A method for proposing potential buyers based on calculation results" refers to a system that recommends highly reliable buyers to users based on market value predictions and presents them as specific buyers.

[0261] "Notifying the user when the sales price matches the conditions set by the user" means that when the sales conditions set in advance by the user are met, the system immediately informs the user of that information.

[0262] "Accumulating user feedback on potential buyers and calculating and displaying recommendation levels" refers to the process of collecting user evaluation data for each buyer and calculating recommendation levels to reflect in future proposals.

[0263] This invention is a system that accurately assesses the market value of privately owned vehicles and enables efficient sales. The system mainly consists of user terminals and a server, with each component working in coordination.

[0264] Users operate a specialized application using their smartphones or computers. Through the application, they input vehicle image data and provide information such as vehicle make, model year, and mileage. The user's device then formats this information and sends it to the server.

[0265] The server inputs the received image data into an advanced generative AI model to analyze the vehicle's appearance. This generative AI model incorporates image analysis technology and has the ability to automatically identify the vehicle's condition and characteristics. Specifically, it recognizes scratches, dents, and the vehicle's color, and outputs this information as analysis results.

[0266] The analysis results are cross-referenced with a market trend database by the server. This database includes historical vehicle sales information and current market trends, and the server uses this to predict the marketability of movable assets and the optimal timing for transactions. Machine learning algorithms are used in this prediction process to enable more accurate assessments.

[0267] Next, the terminal displays predictive information sent from the server to the user. This allows the user to visualize the market value of their vehicle and the optimal timing for selling, supporting efficient decision-making. Furthermore, the server suggests suitable buyers, providing the user with options. The system also includes a notification function that matches the user's desired selling price with market data.

[0268] As a concrete example, consider a scenario where a user enters the prompt message, "My car is a 2018 SUV with 30,000 km on the odometer. What is its market value?" Based on this information, the server performs a detailed market analysis and presents a market value of 2 million yen along with a list of reliable buyers.

[0269] Thus, the present invention helps users obtain accurate market information about their vehicles and provides an efficient and reliable selling process.

[0270] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0271] Step 1:

[0272] Users input photographic data of their vehicles via a dedicated application using a smartphone or computer. This includes basic information such as vehicle make, model year, and mileage. The entered information is formatted and sent to the server. The user's actions are responsible for accurately collecting detailed information about the vehicle.

[0273] Step 2:

[0274] The server inputs the captured image data and vehicle information received from the user into a generating AI model. This model analyzes the vehicle's exterior condition, identifying features such as scratches, dents, and vehicle color. The output of the analysis is detailed information about the vehicle's exterior and serves as the basis for the next processing step. By utilizing the generating AI model, a detailed understanding of the vehicle's condition is achieved.

[0275] Step 3:

[0276] The server compares the analysis results with a market trend database. This database contains historical sales information and market trends, which the server uses to calculate market potential and the optimal trading timing. Based on the analysis data as input, the server outputs market value using statistical models and machine learning algorithms. This calculation process provides accurate market information that should be offered to the user.

[0277] Step 4:

[0278] The terminal displays market value and sale timing information transmitted from the server to the user. The output information is visualized and provided in a format that the user can directly check. This allows the user to check the information as a concrete action to make trading decisions easier.

[0279] Step 5:

[0280] When the user's desired selling price matches the market value, the server sends a notification to the terminal. In addition, it proposes reliable purchase candidates, and the list is presented to the user. Presenting purchase candidates is a specific action to provide the user with options and support quick decision-making.

[0281] Through this series of processes, the user can accurately grasp the market value of the vehicle and experience an efficient trading process.

[0282] (Application Example 1)

[0283] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] In recent years, as the popularity of autonomous vehicles has been increasing, the need for a system that efficiently manages the market value of vehicles and proposes the optimal selling time has been growing. However, in the conventional technology, value evaluation based on real-time vehicle data and proposal of maintenance according to usage conditions have not been sufficiently carried out. The purpose of this invention is to solve these problems and provide a more accurate and timely evaluation of market value.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0286] In this invention, the server includes means for collecting vehicle information and transmitting real-time data, means for integrating the collected information and analyzing the vehicle state by a generation model, and means for predicting the market value based on the analysis result and providing maintenance advice. Thereby, it becomes possible to enhance the market value evaluation and maintenance proposal of autonomous vehicles.

[0287] "Vehicle information" is basic information related to the vehicle, specifically data including vehicle type, manufacturing year, mileage, exterior image, etc.

[0288] The "information input section" is a function that provides an interface for users to input vehicle information, and can be implemented on a smartphone app or computer.

[0289] "Real-time data" refers to data that indicates the current state of a vehicle while it is in operation, and is constantly updated through sensors.

[0290] A "generative model" is a computational model used to analyze the state of a vehicle, and may include machine learning or deep learning techniques.

[0291] "Analysis results" refer to evaluation data on the vehicle's condition obtained using a generative model, indicating the presence or absence of damage and the degree of vehicle deterioration.

[0292] "Market value" refers to the assessed value of a vehicle when it is traded in the market, and is influenced by past sales data and the balance of supply and demand in the market.

[0293] An "information display unit" is a device that has the function of displaying analysis results and other related information to the user, and is installed on the vehicle's console, smartphone, etc.

[0294] A "maintenance recommendation" refers to a proposal to the user of necessary measures for maintaining the vehicle, and specific actions are indicated based on the vehicle's deterioration status.

[0295] This invention is a system that efficiently evaluates the market value of a privately owned vehicle and proposes the optimal time to sell it. The system acquires vehicle information and analyzes real-time data to understand the vehicle's condition. This allows it to accurately predict the vehicle's market value and propose the best time to sell or maintenance to the user.

[0296] The server processes vehicle information received from users and analyzes the vehicle's condition using a generated AI model. Specifically, it evaluates the external condition and degree of deterioration from input data such as vehicle images and mileage. This analysis utilizes machine learning and deep learning technologies, and the results are combined with market information in a database to calculate market value. The software used includes deep learning frameworks such as TensorFlow and Keras.

[0297] The terminal visualizes the analysis results received from the server for the user, clearly indicating the vehicle's market value, recommended timing for sale, and maintenance recommendations. This information is displayed on a smartphone or the main console of the autonomous vehicle, allowing the user to make decisions regarding sale or maintenance based on it.

[0298] Users can monitor market fluctuations in real time through the system and seek the optimal timing for transactions that suit their intentions and conditions. For example, when returning home from a family trip on a holiday, users can update the newly acquired mileage and vehicle condition and understand the subsequent changes in market value.

[0299] An example of a prompt for the generating AI model is, "Use the image and mileage of this 2016 mass-market vehicle to predict its market value." In this way, users can accurately and efficiently manage the market value of their own vehicles.

[0300] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0301] Step 1:

[0302] Users use their smartphones or computers to input images of their vehicle and related information (vehicle type, year of manufacture, mileage) into the information input section. This input data is collected as initial data for analyzing the vehicle's condition.

[0303] Step 2:

[0304] The server inputs the image of the vehicle received from the user into a deep learning model (generative AI model) to analyze the appearance state of the vehicle. Using the input image data, it performs data calculations on appearance information including scratches and color changes of the vehicle, and generates an analysis result evaluating the degree of deterioration of the vehicle.

[0305] Step 3:

[0306] Based on the analysis result, the server searches the market database for past transaction data and current market trends related to similar vehicles, and combines them to predict the market value and selling time of the vehicle. Thereby, an appropriate market value etc. is output for the input data.

[0307] Step 4:

[0308] The server notifies the user's terminal of the predicted market value and selling time. Furthermore, it includes maintenance advice according to the usage status of the vehicle, and presents specific actions necessary for the user.

[0309] Step 5:

[0310] The terminal displays the received analysis result and advice information to the user in an easy-to-understand interface. Based on this information, the user determines the timing of selling or maintaining the vehicle and decides the next action.

[0311] Data is processed in the form of "Please predict the market value using the image and mileage of this mass-produced vehicle manufactured in 2016." as a prompt for the generative AI model.

[0312] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0313] The present invention is a system for facilitating and optimizing the buying and selling of vehicles, and in particular, in addition to evaluating the market value of vehicles, it has the function of recognizing user emotions and dynamically adjusting the interface and information provision based on them. Specific embodiments for carrying out this invention are shown below.

[0314] User actions

[0315] Users launch a dedicated application on their smartphone or computer and enter information to sell their vehicle. This includes taking photos of the vehicle and entering the make, model, year of manufacture, and mileage. Once the user has finished entering the information, the application sends this data to the server.

[0316] Server operation

[0317] The server performs multiple functions based on data received from the user. Image data is analyzed by a generative model to evaluate the vehicle's condition (presence or absence of damage, degree of cosmetic deterioration, etc.). This evaluation result is integrated with market data to predict the current market value and optimal selling time.

[0318] Furthermore, the server utilizes an emotion engine to recognize emotions from user input and interactions. This emotion data is used to dynamically adjust the user interface, providing information best suited to the user's psychological state. For example, if the user is feeling anxious, more detailed information and support options will be presented.

[0319] Terminal operation

[0320] The device displays market value predictions, recommended selling times, and sentiment-based interface adjustments to the user, all derived from the server. When the user is ready to sell, suitable buyer candidates are presented, along with other users' ratings for those buyers. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0321] Specific example

[0322] For example, suppose a user enters vehicle information such as "compact car, 2018 model, 30,000km mileage." The server analyzes this information, predicts a market value of 1.5 million yen, and notifies the user that the optimal time to sell is three months from now. At the same time, the emotion engine recognizes the user's "sense of security" from their input and provides information through a simple interface. Conversely, if the user's emotion is "anxiety," an interface is generated that offers more options and allows for immediate responses to questions.

[0323] In this way, this system supports the optimization of buying and selling based on market trends while taking into account the user's psychological state.

[0324] The following describes the processing flow.

[0325] Step 1: The user enters the information.

[0326] The user launches an application on their device, takes a photo of their vehicle, and then inputs the vehicle make, model year, and mileage. The user's emotional state is analyzed in real time by an emotion engine while they are using the input interface.

[0327] Step 2: The device sends the data.

[0328] The device sends image data and input information collected from the user to the server. This data includes photographs taken, vehicle information, and emotional states detected by the emotion engine.

[0329] Step 3: The server analyzes the image data.

[0330] The server inputs the received image data into a generative model and performs image analysis. This allows it to evaluate the detailed condition of the vehicle's exterior, including whether or not there is damage and whether or not the exterior has deteriorated.

[0331] Step 4: The server collects market data.

[0332] The server collects market prices and historical sales data for similar vehicles from external databases and APIs. The collected data is integrated with the results of image analysis to predict the vehicle's current market value and the optimal time to sell it.

[0333] Step 5: The server makes emotion-based adjustments.

[0334] The server's emotion engine dynamically adjusts the interface presentation and level of detail based on the user's emotional state. For example, if a sense of security is detected, the display is simplified; if anxiety is detected, more detailed information and guidance are provided.

[0335] Step 6: The device displays the prediction results.

[0336] Based on predictions from the server and interface adjustments, the terminal displays market value assessments and recommended selling times to the user. It also presents potential buyers and other users' ratings for them.

[0337] Step 7: The user makes a decision.

[0338] Based on the information provided, the user decides whether or not to sell the vehicle. If the sale conditions match market conditions, the user receives a notification and can begin the actual sale process.

[0339] (Example 2)

[0340] Next, we will describe Example 2. 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".

[0341] Conventional mobile vehicle trading systems have made it difficult to accurately assess the market value of mobile vehicles and predict the optimal selling timing. Furthermore, they have not adequately improved the user experience due to a lack of information provision that takes into account the user's psychological state. This has resulted in a challenge in ensuring that users can conduct transactions with confidence.

[0342] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0343] In this invention, the server includes means for inputting mobile object information via an input device, means for analyzing the state of the mobile object using a generating AI model based on the input image information of the mobile object, and means for predicting the market value and timing of sale of the mobile object based on the analysis results and market information. This enables accurate market value assessment of the mobile object and the provision of optimal information tailored to the user's psychological state.

[0344] An "input device" refers to a device used by users to input mobile information, and generally includes hardware such as smartphones and computers.

[0345] "Mobile vehicles" primarily refer to land vehicles used to transport people and goods, such as automobiles, and are a type of item that is bought and sold.

[0346] "Image information" refers to visual data captured to specifically show the state of a moving object, and is digital data acquired by cameras, smartphones, etc.

[0347] A "generative AI model" refers to a machine learning algorithm or system used to analyze data, specifically a technique for extracting features from images and evaluating their state.

[0348] "Analyzing the condition" refers to the process of determining the current state, deterioration, and presence or absence of damage of a moving object based on image information and other data.

[0349] "Market information" refers to data about the current market situation, including information that shows the balance of supply and demand and price trends.

[0350] "Market value" refers to the price at which a moving object can be traded in the market at the present time, and is the benchmark price for buying and selling that is estimated from the information provided.

[0351] "Selling timing" refers to the optimal time to sell a mobile vehicle on the market, and is determined based on factors such as the season and market demand.

[0352] "Psychological state" refers to the user's current emotions and mental condition, and is a factor considered when adjusting the information the system dynamically provides.

[0353] "Information provision" refers to the act of displaying necessary information to users in a timely manner based on analyzed data and prediction results.

[0354] This invention is a system for efficiently buying and selling mobile objects, and in particular, it accurately evaluates the market value of mobile objects and provides information that reflects the psychological state of users during the buying and selling process. Specific embodiments for implementing this invention are shown below.

[0355] User actions

[0356] Users use a smartphone or computer application to input vehicle information such as make, model, year of manufacture, and mileage. This application also allows for image data input, enabling users to upload images that accurately capture the vehicle's current condition. An example of a prompt message might be, "Analyze the vehicle image to assess damage and its market value."

[0357] Server operation

[0358] After receiving data from the user, the server uses a generative AI model to analyze the image data. Specifically, the process involves evaluating the presence or absence of damage and the degree of deterioration of the mobile object based on the image data provided by the user. This evaluation is performed by a generative AI model, which is an AI algorithm, and the market value of the mobile object is calculated based on the information obtained, in conjunction with current market information.

[0359] The server also analyzes user input and operation patterns and uses an emotion engine to recognize the user's psychological state. Based on this recognized emotional information, it dynamically adjusts the user interface to provide the user with the most relevant information. For example, if the user is experiencing anxiety, a detailed explanation of their state and additional support options will be displayed.

[0360] Terminal operation

[0361] The device displays to the user market value predictions obtained from the server, recommended selling times, and sentiment-based information. When the user decides to sell, it presents suitable buyer candidates and displays ratings from other users. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0362] This system not only streamlines the buying and selling process for mobile vehicles but also enhances user satisfaction. This invention, through the use of new technology, provides a novel method for supporting complex buying and selling decision-making.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] Users launch the application on their smartphone or computer and enter information about their vehicle. This information includes the vehicle make, model year, mileage, and any captured image data. The purpose of this input is to provide an accurate market value assessment and predict the best time to sell the vehicle. The entered data is then sent to a server.

[0366] Step 2:

[0367] The server analyzes the received vehicle data. The generating AI model then performs a detailed evaluation of the vehicle's appearance based on the image data provided by the user. This evaluation process utilizes the prompt message "Analyze the vehicle's appearance and assess whether it is damaged" to run the generating AI model. The output provides information on the presence or absence of damage and the degree of deterioration.

[0368] Step 3:

[0369] The server combines acquired damage and deterioration information with the latest market data. Referencing market data, it calculates the vehicle's market value and the optimal time to sell. Data processing includes market price trend analysis and comparison with other similar vehicles. The output results are specific market value and recommended selling timing.

[0370] Step 4:

[0371] The server uses an emotion engine to recognize the user's psychological state through user input data. The input consists of user actions and text data, which the emotion engine processes. The output is emotional data such as reassurance, interest, or anxiety. Based on this output, the user interface is dynamically adjusted.

[0372] Step 5:

[0373] The terminal displays market value information, recommended selling times, and sentiment-based interface adjustments sent from the server to the user. Specifically, the terminal presents recommendations in an easy-to-understand visual format, encouraging the user to prepare for the sale.

[0374] Step 6:

[0375] Users make their selling decisions based on potential buyers and other user reviews displayed on their device. Once a user sets their selling conditions, the device immediately sends a notification if the conditions match those of the market. This allows users to start the selling process at the optimal time.

[0376] (Application Example 2)

[0377] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0378] Traditional vehicle trading systems, while offering methods to assess the market value of vehicles and support sales, lacked systems that considered user emotions and the unique operational information of autonomous vehicles. Furthermore, they struggled to dynamically optimize vehicle condition and maintenance, leading to missed opportunities for improved usability and sales.

[0379] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0380] In this invention, the server includes means for providing a user interface for inputting vehicle information, means for analyzing the vehicle's condition using a generated model based on image data of the vehicle received from the user, means for predicting the vehicle's market value and timing of sale based on the analysis results and market information, means for dynamically adjusting the interface and information provision according to the user's psychological state using emotion recognition, and means for analyzing vehicle operation information related to autonomous driving technology to improve vehicle maintenance and environmental adjustments. This enables the provision of appropriate information and the creation of a comfortable environment in accordance with the user's emotions, as well as efficient operation management of autonomous vehicles and maximization of their market value.

[0381] "Vehicle information" refers to specific data about a vehicle that a user enters when selling it, including the make and model, year of manufacture, mileage, and image data of the vehicle.

[0382] "User interface" refers to the system's operation screens and input devices used by users to input vehicle information and check results.

[0383] A "generated model" refers to an algorithm or program created using machine learning or artificial intelligence techniques to analyze received data and provide insights and recommendations.

[0384] "Emotion recognition" is a technology in which a computer system analyzes a user's emotions from data such as facial expressions and voice, and determines those emotions.

[0385] "Autonomous driving technology" refers to a set of sensors, algorithms, and control systems that enable a vehicle to operate safely without human intervention, and includes the collection and analysis of data related to its operation.

[0386] "Environmental adjustment" refers to the dynamic control of the vehicle's interior lighting, air conditioning, seat arrangement, and even the entertainment system, in accordance with the user's psychological state and external environment.

[0387] The system for implementing this invention streamlines the vehicle buying and selling process and improves user comfort. The system consists of three main components: a user interface, a server, and a terminal.

[0388] The server runs on devices such as NVIDIA Jetson Xavier and receives vehicle image data, running software to analyze its condition using a generative AI model. Specifically, the software used includes an AI model based on TensorFlow and OpenCV for image processing. This makes it possible to automatically evaluate whether a vehicle is damaged and the degree of deterioration of its appearance.

[0389] By integrating analysis results with market information, the server predicts the market value of a vehicle and the optimal time to sell it. Furthermore, it incorporates an emotion recognition engine that analyzes the user's psychological state from their input and interactions, allowing for dynamic adjustments to the interface and information provided. For example, if a user is feeling anxious, detailed information and support options are provided.

[0390] Based on data obtained from the server, the terminal displays market value predictions, recommended selling times, and potential best buyers to the user. It also displays sentiment-based interface adjustments, enabling users to make quick decisions based on this information.

[0391] As a concrete example, consider a case where a user inputs information such as "SUV, 2020 model, 15,000 km driven." The server analyzes the image data to check the vehicle's condition, integrates it with market data to predict a market value of 3 million yen, and suggests the optimal time to sell it as two months from now. At the same time, if the emotion engine recognizes "peace of mind," a simple and intuitive interface is provided. Conversely, if the user indicates "anxiety," a wealth of information and options are presented.

[0392] The generative AI model performs various analyses based on prompt messages. Prompt messages used include "Provide data samples to predict the market value of the vehicle" and "Provide suggestions for designing an interface that instills a sense of security."

[0393] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0394] Step 1:

[0395] The user launches a dedicated application and enters vehicle information. This information includes a vehicle image, make and model, year of manufacture, and mileage. This data is then sent to the server. It is important that the input data is organized, and high-resolution image data is desirable.

[0396] Step 2:

[0397] The server inputs the received vehicle image data into a generating AI model to analyze the vehicle's condition. Specifically, it uses OpenCV to process the images and analyze the presence or absence of damage and deterioration of the exterior. As a result of the analysis, the vehicle's condition is quantified, providing basic information that can be integrated with other market data.

[0398] Step 3:

[0399] The server integrates analysis results and market data, and uses a generated AI model to predict the market value and optimal selling time for a vehicle. Market data includes historical sales prices, demand forecasts, and economic indicators. The prediction results are output in the form of the vehicle's market value and the optimal selling time.

[0400] Step 4:

[0401] The server uses an emotion recognition engine to analyze the user's emotions from the input data and subsequent interactions. This involves using natural language processing and image analysis techniques to read emotions from the user's facial expressions and text. The analysis results are output as tags such as "feeling secure" and "feeling anxious."

[0402] Step 5:

[0403] The terminal dynamically adjusts its interface based on market value predictions, selling timing, and sentiment analysis results received from the server. For example, if "security" is detected, it provides information in a simple layout; if "anxiety" is detected, it creates an interface that provides detailed information and additional options.

[0404] Step 6:

[0405] Users make selling decisions based on predictive information and interfaces presented on their devices. The system displays optimal buyer candidates, notification settings for when conditions are met, and other users' ratings of potential buyers, providing information to support the decision-making process. This allows users to proceed with the selling process with confidence.

[0406] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0407] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0408] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0409] [Third Embodiment]

[0410] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0411] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0412] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0413] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0414] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0415] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0416] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0417] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0418] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0419] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0420] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0421] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0422] This invention is a system for properly evaluating the market value of privately owned vehicles and for buying and selling them at high prices. This system includes a user interface, a generative model, market data collection means, analysis and prediction means, a notification function, and a buyer suggestion function.

[0423] User actions

[0424] Users utilize a dedicated application or web interface provided via their smartphone or computer. They take photos of their vehicle and input basic information such as make, model, year of manufacture, and mileage. This information is transmitted to the system and used as initial data for analysis.

[0425] Server operation

[0426] The server inputs image data of the vehicle received from the user into a generative model and analyzes the vehicle's external condition. This analysis includes the vehicle's color, scratches, dents, etc. The server also identifies the vehicle model and predicts its value by comparing it with past sales information and market trends in the database.

[0427] For example, if the server receives vehicle data such as "sedan type, manufactured in 2015, 50,000 km driven," the server will analyze the vehicle's condition and output an analysis result such as "no visible damage, 50,000 km driven." Based on this, the market value of this vehicle is estimated to be, for example, 1.2 million yen.

[0428] Terminal operation

[0429] The terminal displays analysis results and value prediction information received from the server to the user. The user makes a transaction decision based on this information. A list of reliable buyers as potential buyers is also displayed. Furthermore, if the desired selling price set by the user matches market conditions, the user is notified, and they can obtain the information necessary to decide whether to sell.

[0430] This system allows users to understand the market value of their vehicle in real time and determine the optimal time to sell. Compared to conventional methods, it enables a more efficient and reliable buying and selling process.

[0431] The following describes the processing flow.

[0432] Step 1: The user uploads images and information.

[0433] The user takes multiple photos of their vehicle using a dedicated application and enters basic information such as the vehicle make, model year, and mileage. This data is then transmitted to a server via the internet.

[0434] Step 2: The server receives the image data.

[0435] The server receives image data and vehicle information sent by the user. This data is then prepared to be input into the analysis model.

[0436] Step 3: The server analyzes the image.

[0437] The server's generation model analyzes the vehicle's external condition from received image data. Specifically, it checks for damage, paint condition, and component deterioration, and generates a numerical evaluation based on the results.

[0438] Step 4: The server collects market data.

[0439] The server uses external databases and APIs to collect data to estimate the market value of the analyzed vehicle. This data includes recent sales prices and market trends of similar vehicles.

[0440] Step 5: The server predicts value.

[0441] The server integrates image analysis results with market data and uses machine learning algorithms to predict the vehicle's current market value and optimal selling time. These results are output as a predicted price and recommended selling time.

[0442] Step 6: The device displays information.

[0443] The terminal displays value predictions and recommendations sent from the server to the user. This includes the predicted market value, the optimal timing for selling, and a list of recommended buyers. The user uses this information to make decisions about selling their vehicle in the future.

[0444] (Example 1)

[0445] Next, we will describe Example 1. 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."

[0446] For many individual users, accurately assessing a vehicle's market value and determining the optimal time to sell is difficult. Traditional methods are time-consuming and often fail to provide reliable valuations. Finding a trustworthy buyer has also been a challenge.

[0447] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0448] In this invention, the server includes means for using a generated AI model to identify the appearance of the vehicle using the received photographic data, means for calculating the marketability and timing of the transaction of the movable property based on the identification result and market trend data, and means for suggesting potential buyers based on the calculation result. This enables the user to quickly and accurately understand the market value of their vehicle and to conduct an optimal transaction with a recommended buyer.

[0449] "Data receiving means for inputting user information" refers to a system that allows users to directly input information about their own vehicle, and is an interface for collecting vehicle details.

[0450] "A means of identifying the exterior appearance using a generated AI model based on received vehicle image data" refers to a process that uses artificial intelligence to determine the external condition of a vehicle based on images of the vehicle provided by the user.

[0451] "Means for calculating the marketability and timing of transactions of movable property based on identification results and market trend data" refers to a function that analyzes the condition of a vehicle and market data to predict the appropriate time to sell and its market value.

[0452] "Means of displaying calculation results to the user" refers to a method of clearly communicating predictive information obtained through analysis to the user, enabling the user to visually confirm the results.

[0453] "A method for proposing potential buyers based on calculation results" refers to a system that recommends highly reliable buyers to users based on market value predictions and presents them as specific buyers.

[0454] "Notifying the user when the sales price matches the conditions set by the user" means that when the sales conditions set in advance by the user are met, the system immediately informs the user of that information.

[0455] "Accumulating user feedback on potential buyers and calculating and displaying recommendation levels" refers to the process of collecting user evaluation data for each buyer and calculating recommendation levels to reflect in future proposals.

[0456] This invention is a system that accurately assesses the market value of privately owned vehicles and enables efficient sales. The system mainly consists of user terminals and a server, with each component working in coordination.

[0457] Users operate a specialized application using their smartphones or computers. Through the application, they input vehicle image data and provide information such as vehicle make, model year, and mileage. The user's device then formats this information and sends it to the server.

[0458] The server inputs the received image data into an advanced generative AI model to analyze the vehicle's appearance. This generative AI model incorporates image analysis technology and has the ability to automatically identify the vehicle's condition and characteristics. Specifically, it recognizes scratches, dents, and the vehicle's color, and outputs this information as analysis results.

[0459] The analysis results are cross-referenced with a market trend database by the server. This database includes historical vehicle sales information and current market trends, and the server uses this to predict the marketability of movable assets and the optimal timing for transactions. Machine learning algorithms are used in this prediction process to enable more accurate assessments.

[0460] Next, the terminal displays predictive information sent from the server to the user. This allows the user to visualize the market value of their vehicle and the optimal timing for selling, supporting efficient decision-making. Furthermore, the server suggests suitable buyers, providing the user with options. The system also includes a notification function that matches the user's desired selling price with market data.

[0461] As a concrete example, consider a scenario where a user enters the prompt message, "My car is a 2018 SUV with 30,000 km on the odometer. What is its market value?" Based on this information, the server performs a detailed market analysis and presents a market value of 2 million yen along with a list of reliable buyers.

[0462] Thus, the present invention helps users obtain accurate market information about their vehicles and provides an efficient and reliable selling process.

[0463] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0464] Step 1:

[0465] Users input photographic data of their vehicles via a dedicated application using a smartphone or computer. This includes basic information such as vehicle make, model year, and mileage. The entered information is formatted and sent to the server. The user's actions are responsible for accurately collecting detailed information about the vehicle.

[0466] Step 2:

[0467] The server inputs the captured image data and vehicle information received from the user into a generating AI model. This model analyzes the vehicle's exterior condition, identifying features such as scratches, dents, and vehicle color. The output of the analysis is detailed information about the vehicle's exterior and serves as the basis for the next processing step. By utilizing the generating AI model, a detailed understanding of the vehicle's condition is achieved.

[0468] Step 3:

[0469] The server compares the analysis results with a market trend database. This database contains historical sales information and market trends, which the server uses to calculate market potential and the optimal trading timing. Based on the analysis data as input, the server outputs market value using statistical models and machine learning algorithms. This calculation process provides accurate market information that should be offered to the user.

[0470] Step 4:

[0471] The terminal displays market value and sale timing information transmitted from the server to the user. The output information is visualized and provided in a format that the user can directly check. This allows the user to check the information as a concrete action to make trading decisions easier.

[0472] Step 5:

[0473] The server sends a notification to the terminal when the user's desired selling price matches the market value. It also suggests reliable potential buyers, and a list of these is presented to the user. Presenting potential buyers is a specific action to provide the user with options and support quick decision-making.

[0474] This series of processes allows users to accurately understand the market value of their vehicles and experience an efficient buying and selling process.

[0475] (Application Example 1)

[0476] Next, we will explain Application Example 1. In the following explanation, 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."

[0477] In recent years, with the increasing adoption of autonomous vehicles, there has been a growing need for systems that efficiently manage the market value of vehicles and propose the optimal time for sale. However, conventional technologies have not adequately provided value assessments based on real-time vehicle data or suggested maintenance tailored to usage conditions. This invention aims to solve these problems and provide a more accurate and timely assessment of market value.

[0478] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0479] In this invention, the server includes means for collecting vehicle information and transmitting real-time data, means for integrating the collected information and analyzing the vehicle's condition using a generative model, and means for predicting market value and providing maintenance recommendations based on the analysis results. This enables advanced market value assessment and maintenance recommendations for autonomous vehicles.

[0480] "Vehicle information" refers to basic information related to a vehicle, specifically data such as make, model, year of manufacture, mileage, and exterior images.

[0481] The "information input section" is a function that provides an interface for users to input vehicle information, and can be implemented on a smartphone app or computer.

[0482] "Real-time data" refers to data that indicates the current state of a vehicle while it is in operation, and is constantly updated through sensors.

[0483] A "generative model" is a computational model used to analyze the state of a vehicle, and may include machine learning or deep learning techniques.

[0484] "Analysis results" refer to evaluation data on the vehicle's condition obtained using a generative model, indicating the presence or absence of damage and the degree of vehicle deterioration.

[0485] "Market value" refers to the assessed value of a vehicle when it is traded in the market, and is influenced by past sales data and the balance of supply and demand in the market.

[0486] An "information display unit" is a device that has the function of displaying analysis results and other related information to the user, and is installed on the vehicle's console, smartphone, etc.

[0487] A "maintenance recommendation" refers to a proposal to the user of necessary measures for maintaining the vehicle, and specific actions are indicated based on the vehicle's deterioration status.

[0488] This invention is a system that efficiently evaluates the market value of a privately owned vehicle and proposes the optimal time to sell it. The system acquires vehicle information and analyzes real-time data to understand the vehicle's condition. This allows it to accurately predict the vehicle's market value and propose the best time to sell or maintenance to the user.

[0489] The server processes vehicle information received from users and analyzes the vehicle's condition using a generated AI model. Specifically, it evaluates the external condition and degree of deterioration from input data such as vehicle images and mileage. This analysis utilizes machine learning and deep learning technologies, and the results are combined with market information in a database to calculate market value. The software used includes deep learning frameworks such as TensorFlow and Keras.

[0490] The terminal visualizes the analysis results received from the server for the user, clearly indicating the vehicle's market value, recommended timing for sale, and maintenance recommendations. This information is displayed on a smartphone or the main console of the autonomous vehicle, allowing the user to make decisions regarding sale or maintenance based on it.

[0491] Users can monitor market fluctuations in real time through the system and seek the optimal timing for transactions that suit their intentions and conditions. For example, when returning home from a family trip on a holiday, users can update the newly acquired mileage and vehicle condition and understand the subsequent changes in market value.

[0492] An example of a prompt for the generating AI model is, "Use the image and mileage of this 2016 mass-market vehicle to predict its market value." In this way, users can accurately and efficiently manage the market value of their own vehicles.

[0493] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0494] Step 1:

[0495] Users use their smartphones or computers to input images of their vehicle and related information (vehicle type, year of manufacture, mileage) into the information input section. This input data is collected as initial data for analyzing the vehicle's condition.

[0496] Step 2:

[0497] The server inputs images of vehicles received from users into a deep learning model (generative AI model) to analyze the vehicle's external condition. Using the input image data, it performs data calculations on external information, including scratches and color changes, and generates analysis results that evaluate the degree of vehicle deterioration.

[0498] Step 3:

[0499] Based on the analysis results, the server searches the market database for past transaction data and current market trends for similar vehicles, and combines them to predict the vehicle's market value and optimal selling time. This allows it to output an appropriate market value and other relevant information for the input data.

[0500] Step 4:

[0501] The server notifies the user's device of the predicted market value and the best time to sell. Furthermore, it includes maintenance recommendations based on the vehicle's usage, providing the user with specific actions they need to take.

[0502] Step 5:

[0503] The terminal displays the received analysis results and recommendations to the user in an easy-to-understand interface. Based on this information, the user can decide when to sell or maintain the vehicle and determine the next course of action.

[0504] The data is processed as a prompt for the generating AI model, in the form of, "Use the image and mileage of this 2016 mass-market vehicle to predict its market value."

[0505] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0506] The present invention is a system for facilitating and optimizing the buying and selling of vehicles, and in particular, in addition to evaluating the market value of vehicles, it has the function of recognizing user emotions and dynamically adjusting the interface and information provision based on them. Specific embodiments for carrying out this invention are shown below.

[0507] User actions

[0508] Users launch a dedicated application on their smartphone or computer and enter information to sell their vehicle. This includes taking photos of the vehicle and entering the make, model, year of manufacture, and mileage. Once the user has finished entering the information, the application sends this data to the server.

[0509] Server operation

[0510] The server performs multiple functions based on data received from the user. Image data is analyzed by a generative model to evaluate the vehicle's condition (presence or absence of damage, degree of cosmetic deterioration, etc.). This evaluation result is integrated with market data to predict the current market value and optimal selling time.

[0511] Furthermore, the server utilizes an emotion engine to recognize emotions from user input and interactions. This emotion data is used to dynamically adjust the user interface, providing information best suited to the user's psychological state. For example, if the user is feeling anxious, more detailed information and support options will be presented.

[0512] Terminal operation

[0513] The device displays market value predictions, recommended selling times, and sentiment-based interface adjustments to the user, all derived from the server. When the user is ready to sell, suitable buyer candidates are presented, along with other users' ratings for those buyers. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0514] Specific example

[0515] For example, suppose a user enters vehicle information such as "compact car, 2018 model, 30,000km mileage." The server analyzes this information, predicts a market value of 1.5 million yen, and notifies the user that the optimal time to sell is three months from now. At the same time, the emotion engine recognizes the user's "sense of security" from their input and provides information through a simple interface. Conversely, if the user's emotion is "anxiety," an interface is generated that offers more options and allows for immediate responses to questions.

[0516] In this way, this system supports the optimization of buying and selling based on market trends while taking into account the user's psychological state.

[0517] The following describes the processing flow.

[0518] Step 1: The user enters the information.

[0519] The user launches an application on their device, takes a photo of their vehicle, and then inputs the vehicle make, model year, and mileage. The user's emotional state is analyzed in real time by an emotion engine while they are using the input interface.

[0520] Step 2: The device sends the data.

[0521] The device sends image data and input information collected from the user to the server. This data includes photographs taken, vehicle information, and emotional states detected by the emotion engine.

[0522] Step 3: The server analyzes the image data.

[0523] The server inputs the received image data into a generative model and performs image analysis. This allows it to evaluate the detailed condition of the vehicle's exterior, including whether or not there is damage and whether or not the exterior has deteriorated.

[0524] Step 4: The server collects market data.

[0525] The server collects market prices and historical sales data for similar vehicles from external databases and APIs. The collected data is integrated with the results of image analysis to predict the vehicle's current market value and the optimal time to sell it.

[0526] Step 5: The server makes emotion-based adjustments.

[0527] The server's emotion engine dynamically adjusts the interface presentation and level of detail based on the user's emotional state. For example, if a sense of security is detected, the display is simplified; if anxiety is detected, more detailed information and guidance are provided.

[0528] Step 6: The device displays the prediction results.

[0529] Based on predictions from the server and interface adjustments, the terminal displays market value assessments and recommended selling times to the user. It also presents potential buyers and other users' ratings for them.

[0530] Step 7: The user makes a decision.

[0531] Based on the information provided, the user decides whether or not to sell the vehicle. If the sale conditions match market conditions, the user receives a notification and can begin the actual sale process.

[0532] (Example 2)

[0533] Next, we will describe Example 2. 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."

[0534] Conventional mobile vehicle trading systems have made it difficult to accurately assess the market value of mobile vehicles and predict the optimal selling timing. Furthermore, they have not adequately improved the user experience due to a lack of information provision that takes into account the user's psychological state. This has resulted in a challenge in ensuring that users can conduct transactions with confidence.

[0535] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0536] In this invention, the server includes means for inputting mobile object information via an input device, means for analyzing the state of the mobile object using a generating AI model based on the input image information of the mobile object, and means for predicting the market value and timing of sale of the mobile object based on the analysis results and market information. This enables accurate market value assessment of the mobile object and the provision of optimal information tailored to the user's psychological state.

[0537] An "input device" refers to a device used by users to input mobile information, and generally includes hardware such as smartphones and computers.

[0538] "Mobile vehicles" primarily refer to land vehicles used to transport people and goods, such as automobiles, and are a type of item that is bought and sold.

[0539] "Image information" refers to visual data captured to specifically show the state of a moving object, and is digital data acquired by cameras, smartphones, etc.

[0540] A "generative AI model" refers to a machine learning algorithm or system used to analyze data, specifically a technique for extracting features from images and evaluating their state.

[0541] "Analyzing the condition" refers to the process of determining the current state, deterioration, and presence or absence of damage of a moving object based on image information and other data.

[0542] "Market information" refers to data about the current market situation, including information that shows the balance of supply and demand and price trends.

[0543] "Market value" refers to the price at which a moving object can be traded in the market at the present time, and is the benchmark price for buying and selling that is estimated from the information provided.

[0544] "Selling timing" refers to the optimal time to sell a mobile vehicle on the market, and is determined based on factors such as the season and market demand.

[0545] "Psychological state" refers to the user's current emotions and mental condition, and is a factor considered when adjusting the information the system dynamically provides.

[0546] "Information provision" refers to the act of displaying necessary information to users in a timely manner based on analyzed data and prediction results.

[0547] This invention is a system for efficiently buying and selling mobile objects, and in particular, it accurately evaluates the market value of mobile objects and provides information that reflects the psychological state of users during the buying and selling process. Specific embodiments for implementing this invention are shown below.

[0548] User actions

[0549] Users use a smartphone or computer application to input vehicle information such as make, model, year of manufacture, and mileage. This application also allows for image data input, enabling users to upload images that accurately capture the vehicle's current condition. An example of a prompt message might be, "Analyze the vehicle image to assess damage and its market value."

[0550] Server operation

[0551] After receiving data from the user, the server uses a generative AI model to analyze the image data. Specifically, the process involves evaluating the presence or absence of damage and the degree of deterioration of the mobile object based on the image data provided by the user. This evaluation is performed by a generative AI model, which is an AI algorithm, and the market value of the mobile object is calculated based on the information obtained, in conjunction with current market information.

[0552] The server also analyzes user input and operation patterns and uses an emotion engine to recognize the user's psychological state. Based on this recognized emotional information, it dynamically adjusts the user interface to provide the user with the most relevant information. For example, if the user is experiencing anxiety, a detailed explanation of their state and additional support options will be displayed.

[0553] Terminal operation

[0554] The device displays to the user market value predictions obtained from the server, recommended selling times, and sentiment-based information. When the user decides to sell, it presents suitable buyer candidates and displays ratings from other users. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0555] This system not only streamlines the buying and selling process for mobile vehicles but also enhances user satisfaction. This invention, through the use of new technology, provides a novel method for supporting complex buying and selling decision-making.

[0556] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0557] Step 1:

[0558] Users launch the application on their smartphone or computer and enter information about their vehicle. This information includes the vehicle make, model year, mileage, and any captured image data. The purpose of this input is to provide an accurate market value assessment and predict the best time to sell the vehicle. The entered data is then sent to a server.

[0559] Step 2:

[0560] The server analyzes the received vehicle data. The generating AI model then performs a detailed evaluation of the vehicle's appearance based on the image data provided by the user. This evaluation process utilizes the prompt message "Analyze the vehicle's appearance and assess whether it is damaged" to run the generating AI model. The output provides information on the presence or absence of damage and the degree of deterioration.

[0561] Step 3:

[0562] The server combines acquired damage and deterioration information with the latest market data. Referencing market data, it calculates the vehicle's market value and the optimal time to sell. Data processing includes market price trend analysis and comparison with other similar vehicles. The output results are specific market value and recommended selling timing.

[0563] Step 4:

[0564] The server uses an emotion engine to recognize the user's psychological state through user input data. The input consists of user actions and text data, which the emotion engine processes. The output is emotional data such as reassurance, interest, or anxiety. Based on this output, the user interface is dynamically adjusted.

[0565] Step 5:

[0566] The terminal displays market value information, recommended selling times, and sentiment-based interface adjustments sent from the server to the user. Specifically, the terminal presents recommendations in an easy-to-understand visual format, encouraging the user to prepare for the sale.

[0567] Step 6:

[0568] Users make their selling decisions based on potential buyers and other user reviews displayed on their device. Once a user sets their selling conditions, the device immediately sends a notification if the conditions match those of the market. This allows users to start the selling process at the optimal time.

[0569] (Application Example 2)

[0570] Next, we will explain application example 2. In the following explanation, 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."

[0571] Traditional vehicle trading systems, while offering methods to assess the market value of vehicles and support sales, lacked systems that considered user emotions and the unique operational information of autonomous vehicles. Furthermore, they struggled to dynamically optimize vehicle condition and maintenance, leading to missed opportunities for improved usability and sales.

[0572] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0573] In this invention, the server includes means for providing a user interface for inputting vehicle information, means for analyzing the vehicle's condition using a generated model based on image data of the vehicle received from the user, means for predicting the vehicle's market value and timing of sale based on the analysis results and market information, means for dynamically adjusting the interface and information provision according to the user's psychological state using emotion recognition, and means for analyzing vehicle operation information related to autonomous driving technology to improve vehicle maintenance and environmental adjustments. This enables the provision of appropriate information and the creation of a comfortable environment in accordance with the user's emotions, as well as efficient operation management of autonomous vehicles and maximization of their market value.

[0574] "Vehicle information" refers to specific data about a vehicle that a user enters when selling it, including the make and model, year of manufacture, mileage, and image data of the vehicle.

[0575] "User interface" refers to the system's operation screens and input devices used by users to input vehicle information and check results.

[0576] A "generated model" refers to an algorithm or program created using machine learning or artificial intelligence techniques to analyze received data and provide insights and recommendations.

[0577] "Emotion recognition" is a technology in which a computer system analyzes a user's emotions from data such as facial expressions and voice, and determines those emotions.

[0578] "Autonomous driving technology" refers to a set of sensors, algorithms, and control systems that enable a vehicle to operate safely without human intervention, and includes the collection and analysis of data related to its operation.

[0579] "Environmental adjustment" refers to the dynamic control of the vehicle's interior lighting, air conditioning, seat arrangement, and even the entertainment system, in accordance with the user's psychological state and external environment.

[0580] The system for implementing this invention streamlines the vehicle buying and selling process and improves user comfort. The system consists of three main components: a user interface, a server, and a terminal.

[0581] The server runs on devices such as NVIDIA Jetson Xavier and receives vehicle image data, running software to analyze its condition using a generative AI model. Specifically, the software used includes an AI model based on TensorFlow and OpenCV for image processing. This makes it possible to automatically evaluate whether a vehicle is damaged and the degree of deterioration of its appearance.

[0582] By integrating analysis results with market information, the server predicts the market value of a vehicle and the optimal time to sell it. Furthermore, it incorporates an emotion recognition engine that analyzes the user's psychological state from their input and interactions, allowing for dynamic adjustments to the interface and information provided. For example, if a user is feeling anxious, detailed information and support options are provided.

[0583] Based on data obtained from the server, the terminal displays market value predictions, recommended selling times, and potential best buyers to the user. It also displays sentiment-based interface adjustments, enabling users to make quick decisions based on this information.

[0584] As a concrete example, consider a case where a user inputs information such as "SUV, 2020 model, 15,000 km driven." The server analyzes the image data to check the vehicle's condition, integrates it with market data to predict a market value of 3 million yen, and suggests the optimal time to sell it as two months from now. At the same time, if the emotion engine recognizes "peace of mind," a simple and intuitive interface is provided. Conversely, if the user indicates "anxiety," a wealth of information and options are presented.

[0585] The generative AI model performs various analyses based on prompt messages. Prompt messages used include "Provide data samples to predict the market value of the vehicle" and "Provide suggestions for designing an interface that instills a sense of security."

[0586] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0587] Step 1:

[0588] The user launches a dedicated application and enters vehicle information. This information includes a vehicle image, make and model, year of manufacture, and mileage. This data is then sent to the server. It is important that the input data is organized, and high-resolution image data is desirable.

[0589] Step 2:

[0590] The server inputs the received vehicle image data into a generating AI model to analyze the vehicle's condition. Specifically, it uses OpenCV to process the images and analyze the presence or absence of damage and deterioration of the exterior. As a result of the analysis, the vehicle's condition is quantified, providing basic information that can be integrated with other market data.

[0591] Step 3:

[0592] The server integrates analysis results and market data, and uses a generated AI model to predict the market value and optimal selling time for a vehicle. Market data includes historical sales prices, demand forecasts, and economic indicators. The prediction results are output in the form of the vehicle's market value and the optimal selling time.

[0593] Step 4:

[0594] The server uses an emotion recognition engine to analyze the user's emotions from the input data and subsequent interactions. This involves using natural language processing and image analysis techniques to read emotions from the user's facial expressions and text. The analysis results are output as tags such as "feeling secure" and "feeling anxious."

[0595] Step 5:

[0596] The terminal dynamically adjusts its interface based on market value predictions, selling timing, and sentiment analysis results received from the server. For example, if "security" is detected, it provides information in a simple layout; if "anxiety" is detected, it creates an interface that provides detailed information and additional options.

[0597] Step 6:

[0598] Users make selling decisions based on predictive information and interfaces presented on their devices. The system displays optimal buyer candidates, notification settings for when conditions are met, and other users' ratings of potential buyers, providing information to support the decision-making process. This allows users to proceed with the selling process with confidence.

[0599] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0600] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0601] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0602] [Fourth Embodiment]

[0603] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0604] As shown in Figure 7, the 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.

[0605] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0606] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0607] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0608] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0609] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0610] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0611] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0612] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0613] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0614] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0615] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0616] This invention is a system for properly evaluating the market value of privately owned vehicles and for buying and selling them at high prices. This system includes a user interface, a generative model, market data collection means, analysis and prediction means, a notification function, and a buyer suggestion function.

[0617] User actions

[0618] Users utilize a dedicated application or web interface provided via their smartphone or computer. They take photos of their vehicle and input basic information such as make, model, year of manufacture, and mileage. This information is transmitted to the system and used as initial data for analysis.

[0619] Server operation

[0620] The server inputs image data of the vehicle received from the user into a generative model and analyzes the vehicle's external condition. This analysis includes the vehicle's color, scratches, dents, etc. The server also identifies the vehicle model and predicts its value by comparing it with past sales information and market trends in the database.

[0621] For example, if the server receives vehicle data such as "sedan type, manufactured in 2015, 50,000 km driven," the server will analyze the vehicle's condition and output an analysis result such as "no visible damage, 50,000 km driven." Based on this, the market value of this vehicle is estimated to be, for example, 1.2 million yen.

[0622] Terminal operation

[0623] The terminal displays analysis results and value prediction information received from the server to the user. The user makes a transaction decision based on this information. A list of reliable buyers as potential buyers is also displayed. Furthermore, if the desired selling price set by the user matches market conditions, the user is notified, and they can obtain the information necessary to decide whether to sell.

[0624] This system allows users to understand the market value of their vehicle in real time and determine the optimal time to sell. Compared to conventional methods, it enables a more efficient and reliable buying and selling process.

[0625] The following describes the processing flow.

[0626] Step 1: The user uploads images and information.

[0627] The user takes multiple photos of their vehicle using a dedicated application and enters basic information such as the vehicle make, model year, and mileage. This data is then transmitted to a server via the internet.

[0628] Step 2: The server receives the image data.

[0629] The server receives image data and vehicle information sent by the user. This data is then prepared to be input into the analysis model.

[0630] Step 3: The server analyzes the image.

[0631] The server's generation model analyzes the vehicle's external condition from received image data. Specifically, it checks for damage, paint condition, and component deterioration, and generates a numerical evaluation based on the results.

[0632] Step 4: The server collects market data.

[0633] The server uses external databases and APIs to collect data to estimate the market value of the analyzed vehicle. This data includes recent sales prices and market trends of similar vehicles.

[0634] Step 5: The server predicts value.

[0635] The server integrates image analysis results with market data and uses machine learning algorithms to predict the vehicle's current market value and optimal selling time. These results are output as a predicted price and recommended selling time.

[0636] Step 6: The device displays information.

[0637] The terminal displays value predictions and recommendations sent from the server to the user. This includes the predicted market value, the optimal timing for selling, and a list of recommended buyers. The user uses this information to make decisions about selling their vehicle in the future.

[0638] (Example 1)

[0639] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0640] For many individual users, accurately assessing a vehicle's market value and determining the optimal time to sell is difficult. Traditional methods are time-consuming and often fail to provide reliable valuations. Finding a trustworthy buyer has also been a challenge.

[0641] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0642] In this invention, the server includes means for using a generated AI model to identify the appearance of the vehicle using the received photographic data, means for calculating the marketability and timing of the transaction of the movable property based on the identification result and market trend data, and means for suggesting potential buyers based on the calculation result. This enables the user to quickly and accurately understand the market value of their vehicle and to conduct an optimal transaction with a recommended buyer.

[0643] "Data receiving means for inputting user information" refers to a system that allows users to directly input information about their own vehicle, and is an interface for collecting vehicle details.

[0644] "A means of identifying the exterior appearance using a generated AI model based on received vehicle image data" refers to a process that uses artificial intelligence to determine the external condition of a vehicle based on images of the vehicle provided by the user.

[0645] "Means for calculating the marketability and timing of transactions of movable property based on identification results and market trend data" refers to a function that analyzes the condition of a vehicle and market data to predict the appropriate time to sell and its market value.

[0646] "Means of displaying calculation results to the user" refers to a method of clearly communicating predictive information obtained through analysis to the user, enabling the user to visually confirm the results.

[0647] "A method for proposing potential buyers based on calculation results" refers to a system that recommends highly reliable buyers to users based on market value predictions and presents them as specific buyers.

[0648] "Notifying the user when the sales price matches the conditions set by the user" means that when the sales conditions set in advance by the user are met, the system immediately informs the user of that information.

[0649] "Accumulating user feedback on potential buyers and calculating and displaying recommendation levels" refers to the process of collecting user evaluation data for each buyer and calculating recommendation levels to reflect in future proposals.

[0650] This invention is a system that accurately assesses the market value of privately owned vehicles and enables efficient sales. The system mainly consists of user terminals and a server, with each component working in coordination.

[0651] Users operate a specialized application using their smartphones or computers. Through the application, they input vehicle image data and provide information such as vehicle make, model year, and mileage. The user's device then formats this information and sends it to the server.

[0652] The server inputs the received image data into an advanced generative AI model to analyze the vehicle's appearance. This generative AI model incorporates image analysis technology and has the ability to automatically identify the vehicle's condition and characteristics. Specifically, it recognizes scratches, dents, and the vehicle's color, and outputs this information as analysis results.

[0653] The analysis results are cross-referenced with a market trend database by the server. This database includes historical vehicle sales information and current market trends, and the server uses this to predict the marketability of movable assets and the optimal timing for transactions. Machine learning algorithms are used in this prediction process to enable more accurate assessments.

[0654] Next, the terminal displays predictive information sent from the server to the user. This allows the user to visualize the market value of their vehicle and the optimal timing for selling, supporting efficient decision-making. Furthermore, the server suggests suitable buyers, providing the user with options. The system also includes a notification function that matches the user's desired selling price with market data.

[0655] As a concrete example, consider a scenario where a user enters the prompt message, "My car is a 2018 SUV with 30,000 km on the odometer. What is its market value?" Based on this information, the server performs a detailed market analysis and presents a market value of 2 million yen along with a list of reliable buyers.

[0656] Thus, the present invention helps users obtain accurate market information about their vehicles and provides an efficient and reliable selling process.

[0657] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0658] Step 1:

[0659] Users input photographic data of their vehicles via a dedicated application using a smartphone or computer. This includes basic information such as vehicle make, model year, and mileage. The entered information is formatted and sent to the server. The user's actions are responsible for accurately collecting detailed information about the vehicle.

[0660] Step 2:

[0661] The server inputs the captured image data and vehicle information received from the user into a generating AI model. This model analyzes the vehicle's exterior condition, identifying features such as scratches, dents, and vehicle color. The output of the analysis is detailed information about the vehicle's exterior and serves as the basis for the next processing step. By utilizing the generating AI model, a detailed understanding of the vehicle's condition is achieved.

[0662] Step 3:

[0663] The server compares the analysis results with a market trend database. This database contains historical sales information and market trends, which the server uses to calculate market potential and the optimal trading timing. Based on the analysis data as input, the server outputs market value using statistical models and machine learning algorithms. This calculation process provides accurate market information that should be offered to the user.

[0664] Step 4:

[0665] The terminal displays market value and sale timing information transmitted from the server to the user. The output information is visualized and provided in a format that the user can directly check. This allows the user to check the information as a concrete action to make trading decisions easier.

[0666] Step 5:

[0667] The server sends a notification to the terminal when the user's desired selling price matches the market value. It also suggests reliable potential buyers, and a list of these is presented to the user. Presenting potential buyers is a specific action to provide the user with options and support quick decision-making.

[0668] This series of processes allows users to accurately understand the market value of their vehicles and experience an efficient buying and selling process.

[0669] (Application Example 1)

[0670] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0671] In recent years, with the increasing adoption of autonomous vehicles, there has been a growing need for systems that efficiently manage the market value of vehicles and propose the optimal time for sale. However, conventional technologies have not adequately provided value assessments based on real-time vehicle data or suggested maintenance tailored to usage conditions. This invention aims to solve these problems and provide a more accurate and timely assessment of market value.

[0672] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0673] In this invention, the server includes means for collecting vehicle information and transmitting real-time data, means for integrating the collected information and analyzing the vehicle's condition using a generative model, and means for predicting market value and providing maintenance recommendations based on the analysis results. This enables advanced market value assessment and maintenance recommendations for autonomous vehicles.

[0674] "Vehicle information" refers to basic information related to a vehicle, specifically data such as make, model, year of manufacture, mileage, and exterior images.

[0675] The "information input section" is a function that provides an interface for users to input vehicle information, and can be implemented on a smartphone app or computer.

[0676] "Real-time data" refers to data that indicates the current state of a vehicle while it is in operation, and is constantly updated through sensors.

[0677] A "generative model" is a computational model used to analyze the state of a vehicle, and may include machine learning or deep learning techniques.

[0678] "Analysis results" refer to evaluation data on the vehicle's condition obtained using a generative model, indicating the presence or absence of damage and the degree of vehicle deterioration.

[0679] "Market value" refers to the assessed value of a vehicle when it is traded in the market, and is influenced by past sales data and the balance of supply and demand in the market.

[0680] An "information display unit" is a device that has the function of displaying analysis results and other related information to the user, and is installed on the vehicle's console, smartphone, etc.

[0681] A "maintenance recommendation" refers to a proposal to the user of necessary measures for maintaining the vehicle, and specific actions are indicated based on the vehicle's deterioration status.

[0682] This invention is a system that efficiently evaluates the market value of a privately owned vehicle and proposes the optimal time to sell it. The system acquires vehicle information and analyzes real-time data to understand the vehicle's condition. This allows it to accurately predict the vehicle's market value and propose the best time to sell or maintenance to the user.

[0683] The server processes vehicle information received from users and analyzes the vehicle's condition using a generated AI model. Specifically, it evaluates the external condition and degree of deterioration from input data such as vehicle images and mileage. This analysis utilizes machine learning and deep learning technologies, and the results are combined with market information in a database to calculate market value. The software used includes deep learning frameworks such as TensorFlow and Keras.

[0684] The terminal visualizes the analysis results received from the server for the user, clearly indicating the vehicle's market value, recommended timing for sale, and maintenance recommendations. This information is displayed on a smartphone or the main console of the autonomous vehicle, allowing the user to make decisions regarding sale or maintenance based on it.

[0685] Users can monitor market fluctuations in real time through the system and seek the optimal timing for transactions that suit their intentions and conditions. For example, when returning home from a family trip on a holiday, users can update the newly acquired mileage and vehicle condition and understand the subsequent changes in market value.

[0686] An example of a prompt for the generating AI model is, "Use the image and mileage of this 2016 mass-market vehicle to predict its market value." In this way, users can accurately and efficiently manage the market value of their own vehicles.

[0687] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0688] Step 1:

[0689] Users use their smartphones or computers to input images of their vehicle and related information (vehicle type, year of manufacture, mileage) into the information input section. This input data is collected as initial data for analyzing the vehicle's condition.

[0690] Step 2:

[0691] The server inputs images of vehicles received from users into a deep learning model (generative AI model) to analyze the vehicle's external condition. Using the input image data, it performs data calculations on external information, including scratches and color changes, and generates analysis results that evaluate the degree of vehicle deterioration.

[0692] Step 3:

[0693] Based on the analysis results, the server searches the market database for past transaction data and current market trends for similar vehicles, and combines them to predict the vehicle's market value and optimal selling time. This allows it to output an appropriate market value and other relevant information for the input data.

[0694] Step 4:

[0695] The server notifies the user's device of the predicted market value and the best time to sell. Furthermore, it includes maintenance recommendations based on the vehicle's usage, providing the user with specific actions they need to take.

[0696] Step 5:

[0697] The terminal displays the received analysis results and recommendations to the user in an easy-to-understand interface. Based on this information, the user can decide when to sell or maintain the vehicle and determine the next course of action.

[0698] The data is processed as a prompt for the generating AI model, in the form of, "Use the image and mileage of this 2016 mass-market vehicle to predict its market value."

[0699] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0700] The present invention is a system for facilitating and optimizing the buying and selling of vehicles, and in particular, in addition to evaluating the market value of vehicles, it has the function of recognizing user emotions and dynamically adjusting the interface and information provision based on them. Specific embodiments for carrying out this invention are shown below.

[0701] User actions

[0702] Users launch a dedicated application on their smartphone or computer and enter information to sell their vehicle. This includes taking photos of the vehicle and entering the make, model, year of manufacture, and mileage. Once the user has finished entering the information, the application sends this data to the server.

[0703] Server operation

[0704] The server performs multiple functions based on data received from the user. Image data is analyzed by a generative model to evaluate the vehicle's condition (presence or absence of damage, degree of cosmetic deterioration, etc.). This evaluation result is integrated with market data to predict the current market value and optimal selling time.

[0705] Furthermore, the server utilizes an emotion engine to recognize emotions from user input and interactions. This emotion data is used to dynamically adjust the user interface, providing information best suited to the user's psychological state. For example, if the user is feeling anxious, more detailed information and support options will be presented.

[0706] Terminal operation

[0707] The device displays market value predictions, recommended selling times, and sentiment-based interface adjustments to the user, all derived from the server. When the user is ready to sell, suitable buyer candidates are presented, along with other users' ratings for those buyers. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0708] Specific example

[0709] For example, suppose a user enters vehicle information such as "compact car, 2018 model, 30,000km mileage." The server analyzes this information, predicts a market value of 1.5 million yen, and notifies the user that the optimal time to sell is three months from now. At the same time, the emotion engine recognizes the user's "sense of security" from their input and provides information through a simple interface. Conversely, if the user's emotion is "anxiety," an interface is generated that offers more options and allows for immediate responses to questions.

[0710] In this way, this system supports the optimization of buying and selling based on market trends while taking into account the user's psychological state.

[0711] The following describes the processing flow.

[0712] Step 1: The user enters the information.

[0713] The user launches an application on their device, takes a photo of their vehicle, and then inputs the vehicle make, model year, and mileage. The user's emotional state is analyzed in real time by an emotion engine while they are using the input interface.

[0714] Step 2: The device sends the data.

[0715] The device sends image data and input information collected from the user to the server. This data includes photographs taken, vehicle information, and emotional states detected by the emotion engine.

[0716] Step 3: The server analyzes the image data.

[0717] The server inputs the received image data into a generative model and performs image analysis. This allows it to evaluate the detailed condition of the vehicle's exterior, including whether or not there is damage and whether or not the exterior has deteriorated.

[0718] Step 4: The server collects market data.

[0719] The server collects market prices and historical sales data for similar vehicles from external databases and APIs. The collected data is integrated with the results of image analysis to predict the vehicle's current market value and the optimal time to sell it.

[0720] Step 5: The server makes emotion-based adjustments.

[0721] The server's emotion engine dynamically adjusts the interface presentation and level of detail based on the user's emotional state. For example, if a sense of security is detected, the display is simplified; if anxiety is detected, more detailed information and guidance are provided.

[0722] Step 6: The device displays the prediction results.

[0723] Based on predictions from the server and interface adjustments, the terminal displays market value assessments and recommended selling times to the user. It also presents potential buyers and other users' ratings for them.

[0724] Step 7: The user makes a decision.

[0725] Based on the information provided, the user decides whether or not to sell the vehicle. If the sale conditions match market conditions, the user receives a notification and can begin the actual sale process.

[0726] (Example 2)

[0727] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] Conventional mobile vehicle trading systems have made it difficult to accurately assess the market value of mobile vehicles and predict the optimal selling timing. Furthermore, they have not adequately improved the user experience due to a lack of information provision that takes into account the user's psychological state. This has resulted in a challenge in ensuring that users can conduct transactions with confidence.

[0729] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0730] In this invention, the server includes means for inputting mobile object information via an input device, means for analyzing the state of the mobile object using a generating AI model based on the input image information of the mobile object, and means for predicting the market value and timing of sale of the mobile object based on the analysis results and market information. This enables accurate market value assessment of the mobile object and the provision of optimal information tailored to the user's psychological state.

[0731] An "input device" refers to a device used by users to input mobile information, and generally includes hardware such as smartphones and computers.

[0732] "Mobile vehicles" primarily refer to land vehicles used to transport people and goods, such as automobiles, and are a type of item that is bought and sold.

[0733] "Image information" refers to visual data captured to specifically show the state of a moving object, and is digital data acquired by cameras, smartphones, etc.

[0734] A "generative AI model" refers to a machine learning algorithm or system used to analyze data, specifically a technique for extracting features from images and evaluating their state.

[0735] "Analyzing the condition" refers to the process of determining the current state, deterioration, and presence or absence of damage of a moving object based on image information and other data.

[0736] "Market information" refers to data about the current market situation, including information that shows the balance of supply and demand and price trends.

[0737] "Market value" refers to the price at which a moving object can be traded in the market at the present time, and is the benchmark price for buying and selling that is estimated from the information provided.

[0738] "Selling timing" refers to the optimal time to sell a mobile vehicle on the market, and is determined based on factors such as the season and market demand.

[0739] "Psychological state" refers to the user's current emotions and mental condition, and is a factor considered when adjusting the information the system dynamically provides.

[0740] "Information provision" refers to the act of displaying necessary information to users in a timely manner based on analyzed data and prediction results.

[0741] This invention is a system for efficiently buying and selling mobile objects, and in particular, it accurately evaluates the market value of mobile objects and provides information that reflects the psychological state of users during the buying and selling process. Specific embodiments for implementing this invention are shown below.

[0742] User actions

[0743] Users use a smartphone or computer application to input vehicle information such as make, model, year of manufacture, and mileage. This application also allows for image data input, enabling users to upload images that accurately capture the vehicle's current condition. An example of a prompt message might be, "Analyze the vehicle image to assess damage and its market value."

[0744] Server operation

[0745] After receiving data from the user, the server uses a generative AI model to analyze the image data. Specifically, the process involves evaluating the presence or absence of damage and the degree of deterioration of the mobile object based on the image data provided by the user. This evaluation is performed by a generative AI model, which is an AI algorithm, and the market value of the mobile object is calculated based on the information obtained, in conjunction with current market information.

[0746] The server also analyzes user input and operation patterns and uses an emotion engine to recognize the user's psychological state. Based on this recognized emotional information, it dynamically adjusts the user interface to provide the user with the most relevant information. For example, if the user is experiencing anxiety, a detailed explanation of their state and additional support options will be displayed.

[0747] Terminal operation

[0748] The device displays to the user market value predictions obtained from the server, recommended selling times, and sentiment-based information. When the user decides to sell, it presents suitable buyer candidates and displays ratings from other users. If the selling conditions set by the user match market trends, the device immediately notifies the user and prompts them to make a selling decision.

[0749] This system not only streamlines the buying and selling process for mobile vehicles but also enhances user satisfaction. This invention, through the use of new technology, provides a novel method for supporting complex buying and selling decision-making.

[0750] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0751] Step 1:

[0752] Users launch the application on their smartphone or computer and enter information about their vehicle. This information includes the vehicle make, model year, mileage, and any captured image data. The purpose of this input is to provide an accurate market value assessment and predict the best time to sell the vehicle. The entered data is then sent to a server.

[0753] Step 2:

[0754] The server analyzes the received vehicle data. The generating AI model then performs a detailed evaluation of the vehicle's appearance based on the image data provided by the user. This evaluation process utilizes the prompt message "Analyze the vehicle's appearance and assess whether it is damaged" to run the generating AI model. The output provides information on the presence or absence of damage and the degree of deterioration.

[0755] Step 3:

[0756] The server combines acquired damage and deterioration information with the latest market data. Referencing market data, it calculates the vehicle's market value and the optimal time to sell. Data processing includes market price trend analysis and comparison with other similar vehicles. The output results are specific market value and recommended selling timing.

[0757] Step 4:

[0758] The server uses an emotion engine to recognize the user's psychological state through user input data. The input consists of user actions and text data, which the emotion engine processes. The output is emotional data such as reassurance, interest, or anxiety. Based on this output, the user interface is dynamically adjusted.

[0759] Step 5:

[0760] The terminal displays market value information, recommended selling times, and sentiment-based interface adjustments sent from the server to the user. Specifically, the terminal presents recommendations in an easy-to-understand visual format, encouraging the user to prepare for the sale.

[0761] Step 6:

[0762] Users make their selling decisions based on potential buyers and other user reviews displayed on their device. Once a user sets their selling conditions, the device immediately sends a notification if the conditions match those of the market. This allows users to start the selling process at the optimal time.

[0763] (Application Example 2)

[0764] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0765] Traditional vehicle trading systems, while offering methods to assess the market value of vehicles and support sales, lacked systems that considered user emotions and the unique operational information of autonomous vehicles. Furthermore, they struggled to dynamically optimize vehicle condition and maintenance, leading to missed opportunities for improved usability and sales.

[0766] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0767] In this invention, the server includes means for providing a user interface for inputting vehicle information, means for analyzing the vehicle's condition using a generated model based on image data of the vehicle received from the user, means for predicting the vehicle's market value and timing of sale based on the analysis results and market information, means for dynamically adjusting the interface and information provision according to the user's psychological state using emotion recognition, and means for analyzing vehicle operation information related to autonomous driving technology to improve vehicle maintenance and environmental adjustments. This enables the provision of appropriate information and the creation of a comfortable environment in accordance with the user's emotions, as well as efficient operation management of autonomous vehicles and maximization of their market value.

[0768] "Vehicle information" refers to specific data about a vehicle that a user enters when selling it, including the make and model, year of manufacture, mileage, and image data of the vehicle.

[0769] "User interface" refers to the system's operation screens and input devices used by users to input vehicle information and check results.

[0770] A "generated model" refers to an algorithm or program created using machine learning or artificial intelligence techniques to analyze received data and provide insights and recommendations.

[0771] "Emotion recognition" is a technology in which a computer system analyzes a user's emotions from data such as facial expressions and voice, and determines those emotions.

[0772] "Autonomous driving technology" refers to a set of sensors, algorithms, and control systems that enable a vehicle to operate safely without human intervention, and includes the collection and analysis of data related to its operation.

[0773] "Environmental adjustment" refers to the dynamic control of the vehicle's interior lighting, air conditioning, seat arrangement, and even the entertainment system, in accordance with the user's psychological state and external environment.

[0774] The system for implementing this invention streamlines the vehicle buying and selling process and improves user comfort. The system consists of three main components: a user interface, a server, and a terminal.

[0775] The server runs on devices such as NVIDIA Jetson Xavier and receives vehicle image data, running software to analyze its condition using a generative AI model. Specifically, the software used includes an AI model based on TensorFlow and OpenCV for image processing. This makes it possible to automatically evaluate whether a vehicle is damaged and the degree of deterioration of its appearance.

[0776] By integrating analysis results with market information, the server predicts the market value of a vehicle and the optimal time to sell it. Furthermore, it incorporates an emotion recognition engine that analyzes the user's psychological state from their input and interactions, allowing for dynamic adjustments to the interface and information provided. For example, if a user is feeling anxious, detailed information and support options are provided.

[0777] Based on data obtained from the server, the terminal displays market value predictions, recommended selling times, and potential best buyers to the user. It also displays sentiment-based interface adjustments, enabling users to make quick decisions based on this information.

[0778] As a concrete example, consider a case where a user inputs information such as "SUV, 2020 model, 15,000 km driven." The server analyzes the image data to check the vehicle's condition, integrates it with market data to predict a market value of 3 million yen, and suggests the optimal time to sell it as two months from now. At the same time, if the emotion engine recognizes "peace of mind," a simple and intuitive interface is provided. Conversely, if the user indicates "anxiety," a wealth of information and options are presented.

[0779] The generative AI model performs various analyses based on prompt messages. Prompt messages used include "Provide data samples to predict the market value of the vehicle" and "Provide suggestions for designing an interface that instills a sense of security."

[0780] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0781] Step 1:

[0782] The user launches a dedicated application and enters vehicle information. This information includes a vehicle image, make and model, year of manufacture, and mileage. This data is then sent to the server. It is important that the input data is organized, and high-resolution image data is desirable.

[0783] Step 2:

[0784] The server inputs the received vehicle image data into a generating AI model to analyze the vehicle's condition. Specifically, it uses OpenCV to process the images and analyze the presence or absence of damage and deterioration of the exterior. As a result of the analysis, the vehicle's condition is quantified, providing basic information that can be integrated with other market data.

[0785] Step 3:

[0786] The server integrates analysis results and market data, and uses a generated AI model to predict the market value and optimal selling time for a vehicle. Market data includes historical sales prices, demand forecasts, and economic indicators. The prediction results are output in the form of the vehicle's market value and the optimal selling time.

[0787] Step 4:

[0788] The server uses an emotion recognition engine to analyze the user's emotions from the input data and subsequent interactions. This involves using natural language processing and image analysis techniques to read emotions from the user's facial expressions and text. The analysis results are output as tags such as "feeling secure" and "feeling anxious."

[0789] Step 5:

[0790] The terminal dynamically adjusts its interface based on market value predictions, selling timing, and sentiment analysis results received from the server. For example, if "security" is detected, it provides information in a simple layout; if "anxiety" is detected, it creates an interface that provides detailed information and additional options.

[0791] Step 6:

[0792] Users make selling decisions based on predictive information and interfaces presented on their devices. The system displays optimal buyer candidates, notification settings for when conditions are met, and other users' ratings of potential buyers, providing information to support the decision-making process. This allows users to proceed with the selling process with confidence.

[0793] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0794] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0795] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0796] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0797] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0798] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0799] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0800] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0801] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0802] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0803] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0804] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0805] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0806] 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.

[0807] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0808] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0809] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0810] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0811] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0812] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0813] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0814] The following is further disclosed regarding the embodiments described above.

[0815] (Claim 1)

[0816] A means of providing a user interface for inputting vehicle information,

[0817] A means of analyzing the vehicle's condition using a generative model based on image data of the vehicle received from the user,

[0818] A means of predicting the market value and timing of sale of a vehicle based on analysis results and market data,

[0819] A means of displaying the prediction results to the user,

[0820] A means of presenting potential buyers based on the prediction results,

[0821] A system that includes this.

[0822] (Claim 2)

[0823] The system according to claim 1, which notifies the user when the selling price meets the conditions set by the user.

[0824] (Claim 3)

[0825] The system according to claim 1, which collects user evaluations of potential buyers, calculates and displays the recommendation level.

[0826] "Example 1"

[0827] (Claim 1)

[0828] A data receiving means for inputting user information,

[0829] A means of identifying the exterior of a vehicle using a generated AI model based on the received photographic data,

[0830] A means for calculating the marketability and trading timing of movable property based on identification results and market trend data,

[0831] A means of displaying the calculation results to the user,

[0832] Based on the calculation results, a means of proposing potential purchase candidates,

[0833] A system that includes this.

[0834] (Claim 2)

[0835] The system according to claim 1, which notifies the user when the sales amount matches the conditions set by the user.

[0836] (Claim 3)

[0837] The system according to claim 1, which accumulates user responses to potential purchase candidates and calculates and displays the recommendation level.

[0838] "Application Example 1"

[0839] (Claim 1)

[0840] A means for providing an information input unit for inputting vehicle information,

[0841] A means of collecting vehicle information and transmitting real-time data to a server,

[0842] Using the collected information, a means of integrating historical data and market trends, and analyzing the vehicle's condition using a generative model,

[0843] A means of predicting the market value and optimal selling time of a vehicle based on analysis results and market information,

[0844] A means of presenting users with predicted value and timing of sale, and providing them with potential candidates.

[0845] A means of notifying users when the formula value they have set matches market conditions,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, further comprising means for periodically updating the market value trend according to the vehicle's usage history and whether or not any new damage has been detected.

[0849] (Claim 3)

[0850] The system according to claim 1, further comprising an information display unit for providing maintenance recommendations according to the vehicle's usage status.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] A means for inputting mobile object information via an input device,

[0854] A means for analyzing the state of a moving object using a generated AI model based on input image information of the moving object,

[0855] A means for predicting the market value and timing of sale of a mobile object based on analysis results and market information,

[0856] A means of recognizing the user's emotions using an emotion engine along with prediction results, dynamically adjusting the user interface, and displaying information;

[0857] A means of presenting potential buyers based on prediction results and displaying other users' evaluations of those potential buyers,

[0858] A system that includes this.

[0859] (Claim 2)

[0860] The system according to claim 1, which notifies the user when the selling price meets the conditions set by the user and the perceived emotions.

[0861] (Claim 3)

[0862] The system according to claim 1, which collects evaluation information on potential buyers, calculates a recommendation level, and dynamically displays it.

[0863] "Application example 2 when combining with an emotional engine"

[0864] (Claim 1)

[0865] A means for providing a user interface for inputting vehicle information,

[0866] A means of analyzing the vehicle's condition using a model generated from vehicle image data received from the user,

[0867] A means of predicting the market value and timing of sale of a vehicle based on analysis results and market information,

[0868] A means of displaying the prediction results to the user,

[0869] A means of presenting potential buyers based on the prediction results,

[0870] A means of dynamically adjusting the interface and information provision according to the user's psychological state using emotion recognition,

[0871] A means of analyzing vehicle operation information related to autonomous driving technology and improving vehicle maintenance and environmental adjustments,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, which notifies the user when the selling price meets the conditions set by the user.

[0875] (Claim 3)

[0876] The system according to claim 1, which collects user evaluations of potential buyers, calculates and displays the recommendation level. [Explanation of Symbols]

[0877] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for providing an information input unit for inputting vehicle information, A means of collecting vehicle information and transmitting real-time data to a server, Using the collected information, a means of integrating historical data and market trends, and analyzing the vehicle's condition using a generative model, A means of predicting the market value and optimal selling time of a vehicle based on analysis results and market information, A means of presenting users with predicted value and timing of sale, and providing them with potential candidates. A means of notifying users when the formula value they have set matches market conditions, A system that includes this.

2. The system according to claim 1, further comprising means for periodically updating the market value trend according to the vehicle's usage history and whether or not any new damage has been detected.

3. The system according to claim 1, further comprising an information display unit for providing maintenance recommendations according to the vehicle's usage status.

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