KR102815560B1 - Method and system for providing negligence rate - Google Patents

Abstract

The present disclosure provides a method for providing a fault ratio, performed by at least one processor. The method includes the steps of receiving a first user input associated with a vehicle accident, determining a fault type associated with the vehicle accident based on the first user input using a first machine learning model, and determining a fault ratio associated with the vehicle accident based on the fault type.

Description

METHOD AND SYSTEM FOR PROVIDING NEGLIGENCE RATE

The present disclosure relates to a method and system for providing a fruit ratio.

Recently, due to the development of industry, many vehicles are being driven. As the number of vehicles increases, the occurrence of traffic accidents is also increasing. In general, when an accident occurs on the road, etc., a dispute occurs through claims such as the high degree of negligence between the perpetrator and the victim, and the insurance company intervenes in the dispute over negligence for the accident.

In the dispute process, the insurance company staff calculates the fault ratio based on the insurance company's practice. The fault ratio can be calculated by reviewing the accident cases investigated through the insurance company's practice booklet. The conventional technology provides a fault chart by selecting multiple questions for high-frequency accident types. However, this conventional technology has the problem that the insurance company staff has to select multiple questions one by one, and does not provide a fault chart for low-frequency accident types, complex accident types, etc.

The present disclosure provides a method and system (device) for providing a fruit ratio to solve the above problems.

The technical problem of the present disclosure is to provide a fault ratio provision method and system (device) that can easily provide a fault ratio through a machine learning model by allowing an insurance company employee to conveniently input accident details, thereby improving the work efficiency of the insurance company employee.

The technical problem of the present disclosure is to provide a method and system (device) for providing a fault rate by which a machine learning model can more easily select a fault type by creating a classification criterion of a tree structure, and can determine the most appropriate accident type for an accident situation while covering various accident types by using the classification criterion of the tree structure.

The technical problem of the present disclosure is to provide a fault ratio providing method and system (device) that provides a fault ratio for a fault type desired by a user by prompting the user to select one of multiple fault types when the details of an accident are ambiguous, and allows the user to confirm a more accurate fault ratio.

The technical problem of the present disclosure is to provide a fault ratio providing method and system (device) that allows a user to conveniently check fault type, fault ratio, description related to fault type, fault ratio, etc. on a display by entering accident details without having to search for each accident type.

The technical problem of the present disclosure is to provide a fault ratio provision method and system (device) that can provide a differentiated experience to the user by allowing the user to check the type of fault, improve the user's understanding of his/her own fault, and easily check the fault ratio related to a vehicle accident.

The present disclosure can be implemented in various ways, including as a method, an apparatus (system), or a computer program stored in a readable storage medium.

According to one embodiment of the present disclosure, a fault ratio providing method may include the steps of receiving a first user input associated with a vehicle accident, determining a fault type associated with the vehicle accident based on the first user input using a first machine learning model, and determining a fault ratio associated with the vehicle accident based on the fault type.

According to one embodiment of the present disclosure, the first machine learning model can be designed to select a fruit type using a tree-structured classification criterion in response to a first user input.

According to one embodiment of the present disclosure, the first user input may include user inputs associated with a vehicle accident received from different users.

According to one embodiment of the present disclosure, the first user input may include multiple user inputs associated with a vehicle accident received from the same user.

According to one embodiment of the present disclosure, the classification criterion may correspond to a criterion in which the second machine learning model generates a fruit rate calculation criterion in a tree structure.

According to one embodiment of the present disclosure, the step of determining the fault percentage may include the step of receiving a second user input regarding a mitigation factor associated with a vehicle accident, and determining the fault percentage based on the second user input.

According to one embodiment of the present disclosure, the method may further include a step of providing at least one of an accident situation related to a vehicle accident, a fault ratio recognition standard, a case law, or a law, together with a fault ratio.

According to one embodiment of the present disclosure, the first user input may include at least one of text, video, image or voice associated with a vehicle accident.

According to one embodiment of the present disclosure, the step of receiving a first user input may include the step of suggesting a plurality of words for a type of fault associated with a vehicle accident and the step of receiving a user input selecting one of the plurality of words.

According to one embodiment of the present disclosure, the step of determining the fault type may include the step of providing a plurality of fault types associated with the vehicle accident based on a first user input, the step of receiving a third user input selecting one of the plurality of fault types, and the step of determining the fault type associated with the vehicle accident based on the third user input.

According to one embodiment of the present disclosure, the method may further include a step of training a first machine learning model based on the error rate, the error type, and the first user input.

A computer program stored in a computer-readable recording medium may be provided to execute a method according to one embodiment of the present disclosure on a computer.

According to one embodiment of the present disclosure, an information processing device for providing a fault ratio is provided. The information processing device includes a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program may include instructions for receiving a first user input associated with a vehicle accident, determining a type of fault associated with the vehicle accident based on the first user input using a first machine learning model, and determining a fault ratio associated with the vehicle accident based on the type of fault.

According to one embodiment of the present disclosure, an insurance company employee can easily obtain a fault ratio through a machine learning model by conveniently entering accident details. Accordingly, the work efficiency of the insurance company employee can be improved.

According to one embodiment of the present disclosure, by generating a classification criterion of a tree structure, a machine learning model can more easily select a fault type. Accordingly, by using the classification criterion of a tree structure, the most suitable accident type for an accident situation can be determined while covering various accident types.

According to one embodiment of the present disclosure, when the details of an accident are ambiguous, the user may be prompted to select one of multiple fault types, thereby providing the user with a fault ratio for the fault type desired by the user. Accordingly, the user may confirm a more accurate fault ratio.

According to one embodiment of the present disclosure, a user can conveniently check the fault type, fault ratio, description associated with the fault type, fault ratio, etc. on a display by entering the accident details without having to search for the accident types one by one.

According to one embodiment of the present disclosure, a user can identify the type of fault and improve his or her understanding of his or her own fault. In addition, by easily identifying the percentage of fault associated with a vehicle accident, the user can be provided with a differentiated experience.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by a person having ordinary skill in the art to which the present disclosure belongs (hereinafter referred to as “one of ordinary skill in the art”) from the description of the claims.

Embodiments of the present disclosure will be described below with reference to the accompanying drawings, in which like reference numerals represent similar elements, but are not limited thereto.
FIG. 1 illustrates an example of a method for providing a fruit ratio according to one embodiment of the present disclosure.
FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to be able to communicate with a plurality of user terminals to provide a fault ratio according to one embodiment of the present disclosure.
FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure.
FIG. 4 is a diagram illustrating an example of a method for providing a fruit ratio according to one embodiment of the present disclosure.
FIG. 5 is a diagram showing an example of classification criteria according to one embodiment of the present disclosure.
FIG. 6 is a diagram illustrating an example in which multiple types of fruits are provided according to one embodiment of the present disclosure.
FIG. 7 is a diagram illustrating an example of updating a machine learning model and classification criteria according to one embodiment of the present disclosure.
FIG. 8 is a diagram illustrating an example of an interface providing a fruit rate according to one embodiment of the present disclosure.
FIG. 9 is a diagram illustrating an example of an interface providing a fruit rate according to one embodiment of the present disclosure.
FIG. 10 is a flowchart illustrating an example of a method for providing a fruit ratio according to one embodiment of the present disclosure.

Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions of widely known functions or configurations will be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

In the attached drawings, identical or corresponding components are given the same reference numerals. In addition, in the description of the embodiments below, the description of identical or corresponding components may be omitted. However, even if the description of a component is omitted, it is not intended that such a component is not included in any embodiment.

The advantages and features of the disclosed embodiments, and the methods for achieving them, will become apparent with reference to the embodiments described below together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below, but may be implemented in various different forms, and these embodiments are provided only to make the present disclosure complete, and to fully inform those skilled in the art of the scope of the invention.

Hereinafter, terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected from widely used general terms as much as possible while considering the functions in this disclosure, but this may vary depending on the intention of engineers engaged in the relevant field, precedents, the emergence of new technologies, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meanings thereof will be described in detail in the description of the relevant invention. Therefore, the terms used in this disclosure should be defined based on the meanings of the terms and the overall contents of this disclosure, rather than simply the names of the terms.

In this specification, singular expressions include plural expressions unless the context clearly specifies that they are singular. In addition, plural expressions include singular expressions unless the context clearly specifies that they are plural. When it is said that a part includes a certain component throughout the specification, this does not exclude other components unless specifically stated to the contrary, but rather means that other components may be included.

Also, the term 'module' or 'part' used in the specification means a software or hardware component, and the 'module' or 'part' performs certain roles. However, the 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to be on an addressable storage medium and may be configured to execute one or more processors. Thus, as an example, the 'module' or 'part' may include at least one of components such as software components, object-oriented software components, class components, and task components, and processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, or variables. The components and 'modules' or 'parts' may be combined into a smaller number of components and 'modules' or 'parts', or may be further separated into additional components and 'modules' or 'parts'.

According to one embodiment of the present disclosure, a 'module' or 'unit' may be implemented as a processor and a memory. 'Processor' should be broadly construed to include a general purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, and the like. In some circumstances, a 'processor' may also refer to an application specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), and the like. A 'processor' may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in conjunction with a DSP core, or any other such combination of configurations. In addition, 'memory' should be broadly construed to include any electronic component capable of storing electronic information. 'Memory' may also refer to various types of processor-readable media, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. Memory is said to be in electronic communication with the processor if the processor can read information from, and/or write information to, the memory. Memory integrated in a processor is in electronic communication with the processor.

In the present disclosure, a 'system' is an information processing device, which may include at least one of a server device and a cloud device, but is not limited thereto. For example, the system may be composed of one or more server devices. As another example, the system may be composed of one or more cloud devices. As another example, the system may be configured and operated by a server device and a cloud device together.

In the present disclosure, 'display' may refer to any display device associated with a computing device, for example, any display device capable of displaying any information/data controlled by or provided from the computing device.

In the present disclosure, 'each of the plurality of As' or 'each of the plurality of As' may refer to each of all components included in the plurality of As, or may refer to each of some components included in the plurality of As.

In the present disclosure, a 'machine learning model' may include any model used to infer an answer to a given input. According to one embodiment, the machine learning model may include an artificial neural network model including an input layer (layer), a plurality of hidden layers, and an output layer. Here, each layer may include a plurality of nodes. In the present disclosure, each of the plurality of machine learning models is described as a separate machine learning model, but is not limited thereto, and some or all of the plurality of machine learning models may be implemented as one machine learning model. In addition, one machine learning model may include a plurality of machine learning models. In the present disclosure, the terms machine learning model and artificial neural network model may be used interchangeably to indicate the same or similar models.

FIG. 1 illustrates an example of a fault ratio providing method according to one embodiment of the present disclosure. In one embodiment, the fault ratio providing method may be performed by at least one processor (e.g., at least one processor of an information processing system or a user terminal). The processor may receive user input associated with a vehicle accident. In this case, the user input may include user input associated with a vehicle accident received from different users. For example, the processor may receive accident information (112, 122, 132) from each of a customer (110), a dispatch reporter (120), and a compensation employee (or, an insurance employee, 130).

In one embodiment, the user input may include multiple user inputs related to a vehicle accident received from the same user. That is, the customer (110), the dispatch reporter (120), and the compensation staff (130) may input the accident details multiple times. In addition, the user input may include not only text related to the vehicle accident, but also images, videos, voices, etc. related to the vehicle accident. For example, the user input may include images taken at the scene of the vehicle accident, videos recorded on a black box, etc. before the accident, brake sounds, etc.

In one embodiment, the processor can use a machine learning model (140) to determine a fault type (150) associated with a vehicle accident based on user input (i.e., accident information (112, 122, 132)). Here, the machine learning model (140) can be pre-designed and trained to select a fault type using a tree-structured classification criterion in response to the user input. An example of the tree-structured classification criterion is described in detail below with reference to FIG. 5.

In one embodiment, the processor can determine a fault rate (160) based on a fault type (150). Here, the fault rate (160) according to the fault type (150) can be set in advance. Additionally or alternatively, the fault rate (160) can be determined based on additional information, such as a deduction factor.

In one embodiment, the processor can train the machine learning model (140) based on the fault type (150) and the fault rate (160) in addition to the user input. For example, weights can be applied so that the machine learning model (140) selects a fault type and a fault rate that are more suitable for a particular accident situation. Accordingly, the performance of the machine learning model can be improved as it continuously learns. In addition, the processor can update the classification criteria based on the fault type (150) and the fault rate (160) in addition to the user input. Such updated classification criteria can reflect information about new accident types.

With this configuration, insurance company employees can easily obtain the fault ratio through a machine learning model by entering the accident details. Accordingly, the work efficiency of insurance company employees can be improved.

FIG. 2 is a schematic diagram showing a configuration in which an information processing system (230) is connected to a plurality of user terminals (210_1, 210_2, 210_3) so as to be able to communicate with each other in order to provide a fault ratio according to one embodiment of the present disclosure. As illustrated, the plurality of user terminals (210_1, 210_2, 210_3) may be connected to an information processing system (230) that may provide a fault ratio provision service via a network (220). Here, the plurality of user terminals (210_1, 210_2, 210_3) may include terminals of users who receive a fault ratio provision service.

In one embodiment, the information processing system (230) may include one or more server devices and/or databases capable of storing, providing, and executing computer executable programs (e.g., downloadable applications) and data associated with providing a service for providing a percentage of errors, or one or more distributed computing devices and/or distributed databases based on a cloud computing service.

The fault ratio provision service provided by the information processing system (230) can be provided to users through a fault ratio provision application, a web browser, a web browser extension program, etc. installed on each of a plurality of user terminals (210_1, 210_2, 210_3). For example, the information processing system (230) can provide information corresponding to a fault ratio provision request received from a user terminal (210_1, 210_2, 210_3) through a fault ratio provision application, etc., or perform corresponding processing.

A plurality of user terminals (210_1, 210_2, 210_3) can communicate with an information processing system (230) via a network (220). The network (220) can be configured to enable communication between a plurality of user terminals (210_1, 210_2, 210_3) and the information processing system (230). Depending on the installation environment, the network (220) can be configured as, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a wireless network such as a mobile communication network, WLAN (Wireless LAN), Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include not only a communication method utilizing a communication network (e.g., a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.) that the network (220) may include, but also short-range wireless communication between user terminals (210_1, 210_2, 210_3).

In FIG. 2, a mobile phone terminal (210_1), a tablet terminal (210_2), and a PC terminal (210_3) are illustrated as examples of user terminals, but are not limited thereto, and the user terminals (210_1, 210_2, 210_3) may be any computing device capable of wired and/or wireless communication and capable of installing and executing a fault ratio providing application or a web browser, etc. For example, the user terminal may include an AI speaker, a smartphone, a mobile phone, a navigation device, a computer, a laptop, a digital broadcasting terminal, a PDA (Personal Digital Assistants), a PMP (Portable Multimedia Player), a tablet PC, a game console, a wearable device, an IoT (internet of things) device, a VR (virtual reality) device, an AR (augmented reality) device, a set-top box, etc. In addition, although FIG. 2 illustrates three user terminals (210_1, 210_2, 210_3) communicating with the information processing system (230) via the network (220), this is not limited thereto, and a different number of user terminals may be configured to communicate with the information processing system (230) via the network (220).

In FIG. 2, a configuration is exemplarily illustrated in which a user's request (e.g., a request to provide a fault ratio) is transmitted to an information processing system (230) through a user terminal (210_1, 210_2, 210_3), but is not limited thereto. The user's request may be provided to the information processing system (230) through an input device associated with the information processing system (230) without passing through the user terminal (210_1, 210_2, 210_3), and the result of processing the user's request (e.g., a determined fault ratio) may be provided to the user through an output device (e.g., a display, etc.) associated with the information processing system (230).

In Fig. 2, the user terminals (210_1, 210_2, 210_3) are illustrated as receiving the fault ratio provision service from the information processing system (230), but this is not limited thereto. For example, the fault ratio provision service may be provided through the fault ratio provision program/application installed in the user terminals (210_1, 210_2, 210_3) without communication with the information processing system (230). In addition, the information processing system (230) is illustrated as a single device, but this is not limited thereto, and the information processing system (230) may be composed of multiple devices.

FIG. 3 is a block diagram showing the internal configuration of a user terminal (210) and an information processing system (230) according to one embodiment of the present disclosure. The user terminal (210) may refer to any computing device capable of executing an application, a web browser, etc. and capable of wired/wireless communication, and may include, for example, a mobile phone terminal (210_1), a tablet terminal (210_2), a PC terminal (210_3) of FIG. 2. As illustrated, the user terminal (210) may include a memory (312), a processor (314), a communication module (316), and an input/output interface (318). Similarly, the information processing system (230) may include a memory (332), a processor (334), a communication module (336), and an input/output interface (338). As illustrated in FIG. 3, the user terminal (210) and the information processing system (230) may be configured to communicate information and/or data via the network (220) using their respective communication modules (316, 336). In addition, the input/output device (320) may be configured to input information and/or data to the user terminal (210) or output information and/or data generated from the user terminal (210) via the input/output interface (318).

The memory (312, 332) may include any non-transitory computer-readable recording medium. According to one embodiment, the memory (312, 332) may include a permanent mass storage device, such as a read only memory (ROM), a disk drive, a solid state drive (SSD), a flash memory, etc. As another example, a permanent mass storage device, such as a ROM, an SSD, a flash memory, a disk drive, etc., may be included in the user terminal (210) or the information processing system (230) as a separate permanent storage device distinct from the memory. In addition, an operating system and at least one program code may be stored in the memory (312, 332).

These software components may be loaded from a computer-readable recording medium separate from the memory (312, 332). This separate computer-readable recording medium may include a recording medium directly connectable to the user terminal (210) and the information processing system (230), for example, a computer-readable recording medium such as a floppy drive, a disk, a tape, a DVD/CD-ROM drive, a memory card, etc. As another example, the software components may be loaded into the memory (312, 332) via a communication module (316, 336) other than a computer-readable recording medium. For example, at least one program may be loaded into the memory (312, 332) based on a computer program that is installed by files provided by developers or a file distribution system that distributes installation files of applications via a network (220).

The processor (314, 334) may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. The instructions may be provided to the processor (314, 334) by the memory (312, 332) or the communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a storage device such as the memory (312, 332).

The communication module (316, 336) may provide a configuration or function for the user terminal (210) and the information processing system (230) to communicate with each other via the network (220), and may provide a configuration or function for the user terminal (210) and/or the information processing system (230) to communicate with another user terminal or another system (for example, a separate cloud system, etc.). For example, a request or data (for example, a request for providing a fault ratio, etc.) generated by the processor (314) of the user terminal (210) according to a program code stored in a recording device such as a memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the information processing system (230) may be received by the user terminal (210) through the communication module (316) of the user terminal (210) via the communication module (336) and the network (220).

The input/output interface (318) may be a means for interfacing with an input/output device (320). As an example, the input device may include a device such as a camera, a keyboard, a microphone, a mouse, etc., including an audio sensor and/or an image sensor, and the output device may include a device such as a display, a speaker, a haptic feedback device, etc. As another example, the input/output interface (318) may be a means for interfacing with a device that has a configuration or function integrated into one for performing input and output, such as a touch screen. For example, when the processor (314) of the user terminal (210) processes a command of a computer program loaded into the memory (312), a service screen, etc., which is configured using information and/or data provided by the information processing system (230) or another user terminal, may be displayed on the display through the input/output interface (318). In FIG. 3, the input/output device (320) is illustrated as not being included in the user terminal (210), but is not limited thereto, and may be configured as a single device with the user terminal (210). In addition, the input/output interface (338) of the information processing system (230) may be a means for interfacing with a device (not shown) for input or output that may be connected to the information processing system (230) or may be included in the information processing system (230). In FIG. 3, the input/output interfaces (318, 338) are illustrated as elements configured separately from the processor (314, 334), but are not limited thereto, and the input/output interfaces (318, 338) may be configured to be included in the processor (314, 334).

The user terminal (210) and the information processing system (230) may include more components than the components of FIG. 3. In one embodiment, the user terminal (210) may be implemented to include at least some of the input/output devices (320) described above. In addition, the user terminal (210) may further include other components such as a transceiver, a Global Positioning System (GPS) module, a camera, various sensors, a database, etc. For example, when the user terminal (210) is a smart phone, it may include components that a smart phone generally includes, and for example, various components such as an acceleration sensor, a gyro sensor, a microphone module, a camera module, various physical buttons, buttons using a touch panel, input/output ports, and a vibrator for vibration may be implemented to be further included in the user terminal (210).

While a program or application for providing a service such as a fruit rate provision service is in operation, the processor (314) may receive text, images, video, voice and/or motion, etc. input or selected through an input device such as a camera, microphone, including a touch screen, a keyboard, an audio sensor and/or an image sensor connected to an input/output interface (318), and may store the received text, images, video, voice and/or motion, etc. in a memory (312) or provide it to an information processing system (230) through a communication module (316) and a network (220).

The processor (314) of the user terminal (210) may be configured to manage, process, and/or store information and/or data received from an input/output device (320), another user terminal, an information processing system (230), and/or a plurality of external systems. The information and/or data processed by the processor (314) may be provided to the information processing system (230) via the communication module (316) and the network (220). The processor (314) of the user terminal (210) may transmit the information and/or data to the input/output device (320) via the input/output interface (318) and output the information and/or data. For example, the processor (314) may output or display the received information and/or data on a screen associated with the user terminal (210).

The processor (334) of the information processing system (230) may be configured to manage, process, and/or store information and/or data received from multiple user terminals (210) and/or multiple external systems. Information and/or data processed by the processor (334) may be provided to the user terminal (210) via the communication module (336) and the network (220).

In one embodiment, the processor (334) of the information processing system (230) may receive a user input related to a vehicle accident from the user terminal (210) via the network (220). In this case, the processor (334) may determine a fault type related to the vehicle accident based on the user input using a machine learning model. Here, the machine learning model may be designed to select a fault type using a classification criterion of a tree structure in response to the user input. In addition, the processor (334) may determine a fault rate related to the vehicle accident based on the fault type.

In one embodiment, the processor (314) of the user terminal (210) may receive data such as a fault ratio, an accident situation related to a vehicle accident, a fault ratio recognition standard, case law, and laws from the information processing system (230) via the network (220). In this case, the processor (314) may transmit and output data such as an accident situation related to a vehicle accident, a fault ratio recognition standard, case law, and laws, along with a fault ratio, to an input/output device (320) via the input/output interface (318).

FIG. 4 is a diagram illustrating an example of a fault ratio providing method according to one embodiment of the present disclosure. In one embodiment, a processor (e.g., 334 of FIG. 3) may receive accident information (410) associated with a vehicle accident. In addition, the processor may input the accident information (410) into a first machine learning model (420), thereby generating a fault type (460) associated with the vehicle accident. Here, the first machine learning model (420) may be designed or learned to select a fault type (460) using a classification criterion (450) of a tree structure in response to the accident information (410).

In one embodiment, the classification criteria (450) of the tree structure may be generated by the second machine learning model (440). Specifically, the second machine learning model (440) may reconstruct the fault ratio calculation criteria (430) into a tree structure to be optimized for the machine learning model and the insurance company's policy. In this case, the classification criteria (450) of the generated tree structure may include a classification system for various accident types including low-frequency accident types. Accordingly, the first machine learning model (420) may select an item for each level of the classification criteria (450) of the tree structure based on the accident information (410), thereby ultimately selecting the fault type (460) associated with the vehicle accident. The classification criteria (450) of the tree structure may be regularly changed according to the insurance company's internal policy, related laws, precedents, etc.

In one embodiment, the processor can determine the fault percentage (470) based on the fault type (460). Specifically, the processor can receive user input for an addition/minus factor associated with a vehicle accident. In addition, the processor can determine the fault percentage (470) based on the user input for the addition/minus factor. That is, the fault percentage (470) can be determined based on the accident information (410) and the addition/minus factor, but is not limited thereto, and the fault percentage (470) can be determined based on additional information.

In FIG. 4, the first machine learning model (420) and the second machine learning model (440) are illustrated separately, but this is not limited to this, and the error rate (470) may be provided using a machine learning model in which the first machine learning model (420) and the second machine learning model (440) are integrated.

By this configuration, by creating a classification criterion of a tree structure, the machine learning model can more easily select the type of fault. Accordingly, by using the classification criterion of a tree structure, the most appropriate type of fault can be determined for the accident situation while covering various types of faults.

FIG. 5 is a diagram showing an example of a classification criterion according to one embodiment of the present disclosure. In one embodiment, the classification criterion may correspond to a criterion of a tree structure generated by a machine learning model. Here, the classification criterion is formed by a plurality of levels, and each of the plurality of levels may include at least one node. Based on this classification criterion, the final fault type according to the accident situation (510) may be determined.

For example, when an accident status (510) related to a vehicle accident is input, the accident status (510) can be classified into any one of a vehicle-to-pedestrian accident (520), a vehicle-to-vehicle accident (530), and a vehicle-to-bicycle accident (540). When the accident status (510) is classified into a vehicle-to-vehicle accident (530), the accident status (510) can be classified into any one of a general intersection (532), a roundabout (534), a non-intersection road (536), and a parking lot (538) associated with the location of the accident, which are child nodes of the vehicle-to-vehicle (530) node. When this process is repeated and a leaf node is reached, the final fault type according to the accident status (510) can be determined.

FIG. 6 is a diagram illustrating an example in which multiple fault types (622) are provided according to one embodiment of the present disclosure. In one embodiment, an information processing system (620) may receive a first user input (612) associated with a vehicle accident from a user terminal (610). In addition, the information processing system (620) may input the first user input (612) into a machine learning model. In this case, the information processing system (620) may provide multiple fault types (622) associated with a vehicle accident to the user terminal (610) based on the first user input (612).

Specifically, the machine learning model may be designed or trained to select at least one fruit type using the classification criteria of the tree structure in response to the first user input (612). For example, if the first user input (612) includes ambiguous content, the machine learning model may select a plurality of child nodes as subnodes of the node associated with the ambiguous content in the classification criteria of the tree structure. Accordingly, the information processing system (620) may determine a plurality of fruit types (622) according to the plurality of child nodes using the machine learning model, and provide them to the user terminal (610).

In one embodiment, the information processing system (620) may receive a second user input (614) for selecting one of a plurality of fruit types (622) from the user terminal (610). That is, the second user input (614) may be received when the user selects a more suitable fruit type from among the provided plurality of fruit types (622). In this case, the information processing system (620) may provide the user terminal (610) with a fruit rate (624) for the fruit type corresponding to the second user input (614) based on the second user input (614).

By this configuration, when the accident content is ambiguous, the fault ratio for the fault type desired by the user can be provided by prompting the user to select one of multiple fault types. Accordingly, the user can confirm a more accurate fault ratio.

FIG. 7 is a diagram illustrating an example of updating a machine learning model (720) and a classification criterion (730) according to one embodiment of the present disclosure. In one embodiment, a processor (e.g., 334 of FIG. 3) may determine a fault type (740) based on accident information (710) and a classification criterion (730) associated with a received vehicle accident using the machine learning model (720). In addition, the processor may determine a fault rate (750) associated with the vehicle accident based on the fault type (740).

In one embodiment, the processor can update the machine learning model (720). Specifically, the processor can train the machine learning model (720) based on the accident information (710), the fault type (740), and the fault rate (750). For example, when accident information (710) including ambiguous content is received, the processor can use the accident information (710), the finally determined fault type (740) and the fault rate (750) as learning data. When the machine learning model (720) receives accident information including similar accident content, weights can be applied so that the machine learning model (720) weighs the fault type (740) and the fault rate (750) previously learned. Accordingly, the performance of the machine learning model (720) and the accuracy of the fault rate (750) can be improved by the machine learning model (720) learning the accident type of a new case.

In one embodiment, the processor can update the classification criteria (730). Specifically, the processor can update the classification criteria (730) based on the accident information (710), the fault type (740), and the fault ratio (750). For example, when accident information (710) including a new accident type is received, the processor can add a new node to the classification criteria (730) of the tree structure reflecting the accident information (710), the fault type (740), and the fault ratio (750). Accordingly, as the new accident type is added to the classification criteria (730), the fault ratio (750) for the corresponding accident type can be quickly determined.

FIG. 8 is a diagram showing an example of an interface (800) providing a fault ratio according to one embodiment of the present disclosure. In one embodiment, a user may input an accident number related to a vehicle accident into a search window (810). In addition, the user may check contract information (820) and fault investigation information (830) related to the vehicle accident by selecting a search button (812) by clicking or entering, etc. Here, the contract information (820) may include an accident type, accident date and time, accident location, insured person, vehicle information, vehicle value, driver information, license information, etc. investigated by a dispatch reporter, insurance company employee, etc. In addition, the fault investigation information (830) may include an applicable fault diagram, confirmed fault, whether confirmed, etc.

In one embodiment, the user may input accident details into the accident details input field (840). For example, the user may input “the vehicle collided with a pedestrian lying on the road while driving straight” into the accident details input field (840). In FIG. 8, the user is shown inputting natural language related to a vehicle accident into the accident details input field (840), but is not limited thereto, and images, videos, voices, etc. related to a vehicle accident may be input. If the user’s input is insufficient, the processor may request the user to input additional details. In addition, the processor may provide the user with a guide for entering details. For example, if the user inputs “the vehicle collided with a pedestrian lying on the road” into the accident details input field (840), the processor may request the user to describe the circumstances under which the collision occurred. In response, the user may input “the vehicle collided with a pedestrian lying on the road.”

In one embodiment, the user can check the expected fault diagram (850) and the fault diagram outline (860) by selecting the AI analysis button (842) by clicking or inputting, etc. Here, the expected fault diagram (850) may include the fault type and fault ratio determined by the machine learning model in response to the accident content entered in the accident content input field (840). In addition, the expected fault diagram (850) may include multiple fault types. In this case, the user can select one of the multiple fault types by clicking or inputting, etc.

In one embodiment, the fault diagram schematic (860) may include details associated with a fault type selected from the expected fault diagram (850). For example, the fault type may include an image or video, a fault percentage, a fault percentage recognition standard, case law, related laws, etc., associated with the fault type. In addition, the user may select a reduction factor in a reduction factor selection box (862) of the fault diagram schematic (860). Here, the reduction factor may include characteristics of the insured object, a compensation scope, characteristics of the insured, regional and environmental factors, etc. for determining the final fault percentage. In response to the user selecting a specific reduction factor, the fault percentage of the fault diagram schematic (860) may be changed and output according to the selected reduction factor.

With this configuration, users can conveniently check the fault type, fault ratio, description related to the fault type, fault ratio, etc. on the display by entering the accident details without having to search for the accident type one by one.

FIG. 9 is a diagram showing an example of an interface providing a fault ratio according to one embodiment of the present disclosure. The first operation (910) shows an example in which a user inputs accident details (914). The user can input accident details (914) through an input field (912). For example, the user can input "a bicycle driving normally on a bicycle path was collided with by a vehicle changing lanes" in the input field (912).

In one embodiment, when a user inputs an accident content (914) through an input field (912), multiple words for the type of fault associated with the accident content (914) may be suggested on the display. For example, when a user inputs “bicycle road” through an input field (912), multiple words including content that may vary in the type of fault, such as “normal driving” and “abnormal driving,” may be suggested on the display. In this case, the user may select a word that is appropriate for the accident content (914) among the multiple words. Accordingly, the user may be prompted to input content necessary to determine the type of fault.

In one embodiment, a fault type (916) determined by a machine learning model in response to a user-entered thought content (914) may be displayed on the display. In this case, an image or video (918) associated with the fault type (916) may be displayed together.

The second operation (920) shows an example in which a description related to a fault type is output. In one embodiment, the user can check the fault type determined by the machine learning model, the accident situation (922), the fault ratio recognition criteria, the fault ratio output field (924), the fault ratio recognition criteria, related laws (926), case law (928), etc. on the display.

In one embodiment, a user may select an offset factor on the display. In response to the user selecting a particular offset factor, the offset ratio may be changed and output according to the selected offset factor in the offset ratio output field (924).

In one embodiment, a smart link associated with the interfaces illustrated in FIGS. 8 and 9 may be created. In this case, the created smart link may be transmitted to the insured. Accordingly, the insured may access the interface described above through the smart link to check the accident details, fault type, fault ratio, etc.

In Fig. 9, only one type of fruit (916) is shown as being output, but this is not limited to this, and multiple types of fruit may be output. In this case, the user can select one of the multiple types of fruit and check the description, image, etc. of the selected type of fruit.

With this configuration, users can identify the type of fault and improve their understanding of their own fault. In addition, users can receive a differentiated experience by easily checking the fault ratio related to vehicle accidents.

FIG. 10 is a flowchart illustrating an example of a fault ratio providing method (1000) according to one embodiment of the present disclosure. In one embodiment, the fault ratio providing method (1000) may be performed by at least one processor (at least one processor of a user terminal or an information processing system). The method (1000) may be initiated by the processor receiving a first user input related to a vehicle accident (S1010).

Thereafter, the processor may determine the type of fault associated with the vehicle accident based on the first user input using the first machine learning model (S1020). Here, the first machine learning model may be designed to select the type of fault using a classification criterion of a tree structure in response to the first user input. In addition, the classification criterion may correspond to a criterion that the second machine learning model generates as a tree structure the criteria for calculating the fault ratio.

Thereafter, the processor can determine a fault ratio associated with the vehicle accident based on the fault type (S1030). Specifically, the processor can receive a second user input for an addition and subtraction factor associated with the vehicle accident. In addition, the processor can determine a fault ratio based on the second user input. Additionally, the processor can provide at least one of an accident situation associated with the vehicle accident, a fault ratio recognition standard, a case law, or a law along with the fault ratio.

In one embodiment, the first user input may include user inputs associated with a vehicle accident received from different users. Additionally, the first user input may include multiple user inputs associated with a vehicle accident received from the same user. Additionally, the first user input may include at least one of text, video, image, or voice associated with the vehicle accident.

In one embodiment, the processor may suggest a plurality of words for types of negligence associated with a vehicle accident. Additionally, the processor may receive user input selecting any one of the plurality of words.

The processor can provide a plurality of fault types associated with the vehicle accident based on the first user input. Additionally, the processor can receive a third user input selecting any one of the plurality of fault types. Additionally, the processor can determine a fault type associated with the vehicle accident based on the third user input.

In one embodiment, the processor can train the first machine learning model based on the error rate, the error type, and the first user input. Additionally, the processor can update the classification criteria based on the error rate, the error type, and the first user input.

The above-described method may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may be a computer-executable program that is continuously stored or temporarily stored for execution or download. In addition, the medium may be various recording means or storage means in the form of a single or multiple hardware combinations, and is not limited to a medium directly connected to a computer system, and may be distributed on a network. Examples of the medium may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROMs, RAMs, flash memories, etc., configured to store program instructions. In addition, examples of other media may include recording media or storage media managed by app stores that distribute applications or other sites, servers, etc. that supply or distribute various software.

The methods, operations, or techniques of the present disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will appreciate that the various exemplary logical blocks, modules, circuits, and algorithm steps described in connection with the present disclosure may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various exemplary components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software will depend upon the particular application and design requirements imposed on the overall system. Those skilled in the art may implement the described functionality in various ways for each particular application, but such implementations should not be construed as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units utilized to perform the techniques may be implemented within one or more ASICs, DSPs, GPUs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described herein, a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

In a firmware and/or software implementation, the techniques may be implemented as instructions stored on a computer-readable medium, such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, a compact disc (CD), a magnetic or optical data storage device, etc. The instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described herein.

When implemented in software, the techniques may be stored on or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a computer. By way of non-limiting example, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. In addition, any connection is suitably made to a computer-readable medium.

Disk and disc, as used herein, include compact discs, laser discs, optical discs, digital versatile discs (DVDs), floppy disks, and Blu-ray discs, wherein disks usually reproduce data magnetically, while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and the storage medium may reside as discrete components in the user terminal.

While the embodiments described above have been described as utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the present disclosure is not limited thereto, and may be implemented in conjunction with any computing environment, such as a network or distributed computing environment. Furthermore, aspects of the subject matter in the present disclosure may be implemented in multiple processing chips or devices, and storage may be similarly affected across multiple devices. Such devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in connection with one embodiment thereof, it will be understood by those skilled in the art that various modifications and variations may be made therein without departing from the scope of the present disclosure. Furthermore, such modifications and variations should be considered to fall within the scope of the claims appended hereto.

110: Customer
112: Accident Information
120: Dispatch Reporter
122: Accident Information
130: Compensation staff
132: Accident Information
140: Machine Learning Model
150: Type of fruit
160: Fault ratio

Claims (15)

In a method for providing a fruit ratio, performed by at least one processor,
A step of receiving a first user input associated with a vehicle accident;
A step of determining a type of fault associated with the vehicle accident based on the first user input using a first machine learning model; and
A step of determining the percentage of fault associated with the vehicle accident based on the above fault type.
Including,
The first user input above includes text associated with the vehicle accident,
The step of receiving the first user input is:
If the first user input is an input of content related to the vehicle accident, a step of suggesting multiple words for the type of fault related to the vehicle accident; and
A step of receiving a user input for selecting one of the above multiple words.
Including,
A method for providing a fault percentage, wherein the plurality of words include content that the type of fault associated with the vehicle accident may vary.
In the first paragraph,
A method for providing a fruit rate, wherein the first machine learning model is designed to select the fruit type using a tree-structured classification criterion in response to the first user input.
In the first paragraph,
A method for providing a fault ratio, wherein said first user input comprises user inputs associated with said vehicle accident received from different users.
In the first paragraph,
A method for providing a fault ratio, wherein the first user input comprises a plurality of user inputs associated with the vehicle accident received from the same user.
In the second paragraph,
The above classification criteria are a method for providing a fault ratio, which corresponds to the criteria for generating a fault ratio calculation criterion in a tree structure by a second machine learning model.
In the first paragraph,
The steps for determining the above-mentioned percentage of fault are:
A step of receiving a second user input regarding a deduction factor associated with the vehicle accident; and
A step of determining the error rate based on the second user input.
A method of providing a percentage of fault, including:
In the first paragraph,
A step of providing at least one of the accident circumstances, criteria for recognizing the fault ratio, case law or law related to the vehicle accident, along with the above fault ratio.
A method of providing a percentage of fault, which further includes:
In the first paragraph,
A method for providing a fault ratio, wherein the first user input further includes at least one of a video, image or audio associated with the vehicle accident.
delete In the first paragraph,
The steps for determining the above types of errors are:
A step of providing a plurality of types of fault associated with the vehicle accident based on the first user input;
receiving a third user input selecting one of the plurality of fruit types; and
A step of determining the type of fault associated with the vehicle accident based on the third user input.
A method of providing a percentage of fault, including:
In Article 10,
Based on the first user input, the step of providing multiple types of fault associated with the vehicle accident is:
A step of outputting an image associated with at least one of the above multiple types of errors.
A method of providing a percentage of fault, including:
In the first paragraph,
A step of training the first machine learning model based on the above-mentioned error rate, the above-mentioned error type, and the above-mentioned first user input.
A method of providing a percentage of fault, which further includes:
In the second paragraph,
A step of updating the classification criteria based on the above-mentioned fault ratio, the above-mentioned fault type, and the above-mentioned first user input.
A method of providing a percentage of fault, which further includes:
A computer program stored on a computer-readable recording medium for executing a method according to any one of claims 1 to 8 and claims 10 to 13 on a computer.
As an information processing device for providing a fruit ratio,
Communication module;
memory; and
At least one processor coupled to said memory and configured to execute at least one computer-readable program contained in said memory,
At least one of the above programs,
Receive a first user input related to a vehicle accident,
Using the first machine learning model, the type of fault associated with the vehicle accident is determined based on the first user input,
Based on the above fault type, it includes commands for determining the fault ratio associated with the vehicle accident,
The first user input above includes text associated with the vehicle accident,
Receiving the first user input above,
If the first user input is an input related to the vehicle accident, multiple words are suggested for the type of fault related to the vehicle accident,
Receiving user input selecting one of the above multiple words.
Including,
An information processing device wherein the plurality of words include content that may vary in the type of fault associated with the vehicle accident.