KR20230112766A - Method, device and program for providing AI-based car purchase support service - Google Patents

Abstract

The present invention relates to a method for providing a car purchase support service based on artificial intelligence. When vehicle purchase wish information is received from a client terminal, it is analyzed to recommend a vehicle that matches the client's vehicle purchase needs, and a specific vehicle purchase request is received from the client terminal. When a contract for a vehicle is signed, it has the effect of providing a consignment service.

Description

Method, device and program for providing AI-based car purchase support service {Method, device and program for providing AI-based car purchase support service}

The present disclosure relates to a method of providing a car purchase support service.

Recently, technologies in various fields, such as image analysis technology and technology for learning artificial intelligence models, are rapidly developing.

Despite the development of these technologies, most of the tasks such as vehicle maintenance, inspection, and after-sales service are still performed directly by people, and due to this, there are considerable differences in skills and costs among individuals. there is.

Therefore, if this technology is used for vehicle image analysis and AS information derivation using image analysis, it is expected that it can be used in areas such as vehicle maintenance, inspection, and AS, but this technology has not been disclosed at present.

In this way, if AI learns vehicle image analysis technology, it is expected that it can be used in the AI-based vehicle after-sales service as well as customer vehicle purchase support services.

In order to solve the above problems, the present invention is to provide an artificial intelligence-based car purchase support service providing method.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

An artificial intelligence-based car purchase support service providing method according to an embodiment of the present invention for solving the above problems is a method performed by a car purchase support service providing apparatus, and receives desired purchase information on a vehicle from a client terminal. receiving; selecting at least one recommended vehicle that matches the purchase needs of the client by inputting the purchase desire information into an artificial intelligence model; providing information on the selected recommended vehicle to the client terminal; and receiving a selection of one of the recommended vehicles from the client terminal and proceeding with a contract for the selected vehicle.

Further, the purchase wish information includes at least one of vehicle type, price, brand, fuel efficiency, fuel method, driving method, and vehicle image.

Further, the method may further include providing a consignment driver connection service for the vehicle based on the client's desired vehicle pick-up area included in the contract, after the step of proceeding with the contract.

In addition to this, another method for implementing the present invention, another system, and a computer readable recording medium recording a computer program for executing the method may be further provided.

According to the present invention as described above, an AI-based car purchase support service can be provided.

The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a block diagram of an apparatus for deriving analysis information through vehicle image analysis and providing vehicle AS information using the same according to an embodiment of the present invention.
2 is a diagram illustrating an example of generating a learning dataset by analyzing an image of a learning target vehicle and learning an artificial intelligence model using the same.
3 is a diagram illustrating the derivation of AS information by inputting an image of a vehicle to be inspected into an artificial intelligence model.
4 is a diagram illustrating an example of deriving a recommended vehicle for a client by analyzing it by an artificial intelligence model when vehicle purchase wish information is input to a client terminal.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram of an apparatus for deriving analysis information through vehicle image analysis and providing vehicle AS information using the same according to an embodiment of the present invention.

2 is a diagram illustrating an example of generating a learning dataset by analyzing an image of a learning target vehicle and learning an artificial intelligence model using the same.

3 is a diagram illustrating the derivation of AS information by inputting an image of a vehicle to be inspected into an artificial intelligence model.

In order to describe a method of providing artificial intelligence-based car purchase support service according to an embodiment of the present invention, with reference to FIGS. 1 to 3, analysis information is derived through vehicle image analysis and vehicle AS information providing apparatus and method using the same Let me explain first.

Referring to FIG. 1 , an apparatus for deriving analysis information through vehicle image analysis and providing vehicle AS information using the same according to an embodiment of the present invention includes a processor, a communication unit, a database, and an input/output unit.

However, in some embodiments, the device may include fewer or more components than those shown in FIG. 1 .

Depending on the embodiment, it may be divided into an analysis information derivation device through vehicle image analysis and a vehicle AS information providing device, and these specific details are easily selected by the practitioner of the present invention.

In addition, in an embodiment of the present invention, an apparatus for deriving analysis information through vehicle image analysis and an apparatus for providing vehicle AS information may include a computer and information processing means, and may include a server device.

The communication unit communicates with an external device, server, etc., may receive at least one image of the vehicle to be studied or the vehicle to be inspected, and transmit AS information derived about the vehicle to be inspected to the external device or server.

The memory stores various commands, algorithms, and artificial intelligence models for executing a method of deriving analysis information through vehicle image analysis and a method of providing vehicle AS information.

In addition, the memory stores various data sources for vehicle inspection, such as a list of various vehicle types and basic specification information for each vehicle.

The input/output unit has a general input unit and output unit configuration, and can receive various control signals, information, images, etc., and output various generated AS information.

The processor is responsible for controlling the components in the analysis information derivation device and the vehicle AS information providing device through vehicle image analysis, and can execute the method according to the embodiment of the present invention by executing commands and algorithms in the memory and using the artificial intelligence model. there is.

Below, a device for deriving analysis information through vehicle image analysis will be referred to as a first device, and a vehicle AS information providing device will be referred to as a second device.

Referring to FIG. 2 , a method of deriving analysis information through vehicle image analysis according to an embodiment of the present invention will be described.

A first device receives at least one image (video, image) of a learning target vehicle.

The first device analyzes the received image to recognize images of vehicle parts included in the learning target vehicle, locations of the vehicle components in the vehicle, and connection relationships between a plurality of vehicle components.

The first device inputs the result recognized by the artificial intelligence model as learning data together with the vehicle type and basic vehicle specifications.

At this time, it is preferable to receive a plurality of different images for more detailed and accurate analysis.

In an embodiment of the present invention, an image may be a video image composed of a plurality of frames and having a predetermined time, or an image composed of one frame may be applied.

In addition, when individual images of all detachable parts such as the bonnet, headlight, rearview mirror, wheel, body, and trunk are received as well as the image of the exterior of the vehicle, each image may be separated and stored in the memory.

In addition, when an image or coupling information coupled between parts/parts is received, a connection relationship between a plurality of vehicle parts may be recognized based on this, generated as learning data, and stored in a memory.

By recognizing this information, storing it in memory, building big data, and inputting it as learning data to learn an artificial intelligence model, when an image of an accident vehicle or a vehicle in which a breakdown occurs is received, the image is analyzed (big data, ML). Classification by AS can be done only with this, and it is possible to determine the parts that require AS.

As described above, the processor recognizes the location of vehicle parts in the vehicle and the connection relationship between multiple vehicle parts, creates big data with the vehicle type and basic vehicle specifications as a learning data set, and inputs it into an artificial intelligence model for learning will make

In some embodiments, the processor may build and use individual AI models for each vehicle manufacturing brand.

In some embodiments, the processor may build and use individual artificial intelligence models for each type of vehicle.

In this way, if an individual AI model is built and used for each vehicle type, the AI model can start analysis by entering the type of vehicle to be inspected and inputting only the image of the vehicle to be inspected, so efficient, fast and accurate judgment can be made. be able to get off

In some embodiments, the vehicle type may be recognized/identified by matching with the customer's DB.

As an embodiment, the memory may include a vehicle type analysis algorithm, and the processor uses the vehicle type algorithm to determine various types of vehicle body shape, side mirror shape, lighting shape, emblem shape, body size, ratio, color, wheel shape, etc. of the vehicle. It can identify elements accurately and in detail, determine the type of vehicle, and even determine the vehicle's various options.

Referring to FIG. 3 , a method for providing vehicle AS information according to an embodiment of the present invention will be described.

A second device receives at least one image of the vehicle to be inspected.

The second device inspects the vehicle to be inspected by inputting at least one image of the vehicle to be inspected into the artificial intelligence model.

In this case, the second device may also receive input of the type of vehicle to be inspected, basic specifications, and the like.

However, the processor is not limited thereto, and when at least one image of the vehicle to be inspected is received, the artificial intelligence model analyzes the image to determine the type of vehicle, and stores various basic specification information for inspection of the vehicle in memory. can be loaded.

The artificial intelligence model can inspect the vehicle to be inspected using the learned model, and can utilize big data stored in the device/server.

Based on the inspection result, it is determined whether the vehicle to be inspected needs AS.

If the vehicle to be inspected requires AS, at least one AS-required part is derived.

Since the vehicle to be inspected may not necessarily require AS, the processor may determine whether the vehicle to be inspected requires AS by performing the above process.

In addition, when it is determined that the vehicle to be inspected requires AS, the processor may derive which parts require AS.

In addition, the processor may derive an essential AS part in which the AS is essential and a recommended AS part in which the AS is recommended.

In addition, the processor may derive a speculative AS portion that guesses that an AS will be needed in the next few days or months.

At this time, the processor inputs the image of the vehicle to be inspected to the artificial intelligence model, and the artificial intelligence model determines the external shape of the vehicle based on the input image, and determines the part requiring AS through the external shape.

In some embodiments, there may be parts that are difficult to identify only with the external image of the vehicle to be inspected. In this case, at least one survey on the state of the vehicle may be conducted with the user terminal, and based on the survey result, the user may select a vehicle part to be photographed and request image upload.

Based on the learned results, the artificial intelligence model can analyze the video of the vehicle to be inspected to determine various information, as well as perform classification decisions for each AS based on big data. (Example: A car that was brought in with a certain exterior condition had many tire changes, etc.)

AS information for AS of the derived AS-required part is derived.

In some embodiments, the processor may evaluate the reliability of the AS information using an artificial intelligence model, and if a value equal to or less than a preset reliability is calculated, it may request more images around the suspicious part in the vehicle, and the corresponding part A confirmation request signal may be sent to an expert on

In addition, the AS information may include part information required for the AS of the corresponding part, but is not limited thereto since parts are not required and AS is needed in some cases.

Based on the derived AS information, the AS person in charge of the vehicle to be inspected is assigned, and a work movement route is derived.

The memory may store information on career information, fields of expertise, and the like for a plurality of AS personnel.

The processor may match the derived AS information to the contact person list and match the person in charge/expert optimized for the derived AS information.

As an embodiment, in the case of AS for a specific part, an essential responsibility condition that an AS person in charge with a specific qualification must perform may be set.

The processor not only derives the assignment of AS personnel and the work movement path, but also calculates the waiting time for AS and the time required for AS.

Accordingly, the AS information derived by the processor may include various information related to the AS, such as a location where the AS is required, parts required for the AS, an AS schedule, an AS person in charge, and a work movement route for the AS.

In addition, the processor may request various settings for the AS so that the AS person in charge can start the AS after moving to an AS execution location (eg, a workbench, a workbench).

Accordingly, the processor may generate ML-based data through vehicle task allocation and workbay location annotation, and may create a learning/test model.

In addition, the apparatus according to the embodiment of the present invention may use an algorithm for assigning an AS person in charge and a work movement path, and may use a graph algorithm through assigning weights to a person in charge and a workbay based on the analyzed data.

Additionally, the processor may perform labor analysis.

Specifically, the processor may output AS part and labor analysis information through a vehicle image. (Using Hadoop-based big data analysis technology)

4 is a diagram illustrating an example of deriving a recommended vehicle for a client by analyzing it by an artificial intelligence model when vehicle purchase wish information is input to a client terminal.

A method for providing an artificial intelligence-based car purchase support service according to an embodiment of the present invention may be performed through the following processes.

The processor receives purchase wish information on the vehicle from the client terminal through the communication unit.

The processor selects at least one recommended vehicle that matches the purchase needs of the client by inputting purchase desired information into the artificial intelligence model.

Information on the selected recommended vehicle is provided to the client terminal.

One of the recommended vehicles is selected from the client terminal, and a contract for the selected vehicle is executed.

Based on the area where the client wants to pick up the vehicle included in the contract, a consignment driver connection service for the vehicle is provided.

At this time, the processor may use the learned artificial intelligence model described with reference to FIGS. 1 and 2, but is not limited thereto and may also use a separately constructed artificial intelligence model.

An artificial intelligence-based car purchase support service providing device/server according to an embodiment of the present invention may operate in a web or application manner, and may provide a self-manufactured/equipped service application.

After installing the app on the terminal, the client may input vehicle purchase desired information about the vehicle the client wishes to purchase, as shown in FIG. 4 .

At this time, the vehicle purchase desired information may include whether the vehicle is new/used, vehicle type, price, brand, fuel efficiency, fuel type, driving method, and the like, and an image of the vehicle may be received as vehicle purchase desired information from the client terminal.

In this case, the video of the vehicle may be a video directly photographed by the client or a video downloaded from the net.

The client does not have to enter all vehicle purchase desired information, and when the client enters the vehicle purchase desired information, the artificial intelligence model analyzes it and selects at least one vehicle that matches the client's vehicle purchase needs as a recommended vehicle. will do

At this time, the vehicle purchase wish information is not necessarily limited to the vehicle, and personal information of the client, such as the client's taste, personality, travel style, and family information, may be applied.

The processor provides the derived recommended vehicle list to the client terminal, and the client checks it and selects a vehicle to proceed with the contract from the recommended vehicle list.

When the contract information is entered into the client terminal and the desired region for receiving the vehicle of the client is selected, the processor sets the starting point/destination and provides a consignment driver connection service for the vehicle.

As an embodiment, when a vehicle (including a new vehicle and a used vehicle) is shipped, the processor may input an image of the vehicle to the artificial intelligence model to check whether the vehicle has a problem in appearance.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

The aforementioned program is C, C++, JAVA, machine language, etc. It may include a code coded in a computer language of. These codes may include functional codes related to functions defining necessary functions for executing the methods, and include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, these codes may further include memory reference related codes for which location (address address) of the computer's internal or external memory should be referenced for additional information or media required for the computer's processor to execute the functions. there is. In addition, when the processor of the computer needs to communicate with any other remote computer or server in order to execute the functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes for whether to communicate, what kind of information or media to transmit/receive during communication, and the like.

The storage medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and is readable by a device. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers accessible by the computer or various recording media on the user's computer. In addition, the medium may be distributed to computer systems connected through a network, and computer readable codes may be stored in a distributed manner.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (3)

A method performed by a car purchase support service providing device,
Receiving purchase wish information on a vehicle from a client terminal;
selecting at least one recommended vehicle that matches the purchase needs of the client by inputting the purchase wish information into an artificial intelligence model;
providing information on the selected recommended vehicle to the client terminal; and
Receiving a selection of one of the recommended vehicles from the client terminal and proceeding with a contract for the selected vehicle,
A method of providing artificial intelligence-based car purchase support services.
According to claim 1,
The purchase wish information includes at least one of vehicle model, price, brand, fuel efficiency, fuel method, driving method, and vehicle image.
A method of providing artificial intelligence-based car purchase support services.
According to claim 2,
Following the contract process,
Further comprising providing a consignment driver connection service for the vehicle based on the client's vehicle pick-up area included in the contract,
A method of providing artificial intelligence-based car purchase support services.