KR20210049321A - Computer program for providing a method to analysis insurance documents - Google Patents

Abstract

Disclosed is a computer program stored in a computer-readable recording medium executed by one or more processors to efficiently provide methods related to insurance subscription. According to one embodiment of the present invention, the computer program is configured to execute the following operations for analyzing documents related to insurance subscription while being executed by one or more processors: determining whether to initiate an insurance-related procedure on the basis of a user's chatting record received through a chatbot interface of a user terminal; receiving an insurance subscription document input through the chatbot interface from the user terminal; inputting the insurance subscription document into a document analysis model to analyze the insurance subscription document by using a document analysis model; and determining the insurance subscription on the basis of an analysis result of the insurance subscription document.

Description

Computer program for providing methods of analyzing insurance documents {COMPUTER PROGRAM FOR PROVIDING A METHOD TO ANALYSIS INSURANCE DOCUMENTS}

The present disclosure relates to a computer program for providing information processing using a computer, and more specifically, to a computer program for providing a method for analyzing a document.

In recent years, as the demand for insurance increases, automation of insurance guidance and subscription processes using a chatbot of a terminal is in progress in order to efficiently provide insurance. However, human intervention is still essential to the procedures and screening for insurance. In particular, it can be said that it is a process of determining the degree of possible risk or loss of an individual, and contract review (underwriting), a process that evaluates the expected mortality rate or expected disease incidence rate, is an expert in the contract review task (underwriter, Underwriter). Insurance companies decide whether to accept the insurance subscription of the policyholder (subscriber) through the contract review result, classify the risk of the insured, and use it to adjust the premium or the limit of the insurance amount according to the degree of risk. I leave it.

However, the contract review conducted by the person in charge of contract review or the mandatory review performed by other experts is postponed to the business day following the review and approval of insurance subscriptions received on the same day as the holiday.

Accordingly, there may be a demand in the art for a method to proceed with subscription without time constraints through automation of the insurance subscription process including insurance guidance and contract review through a chatbot.

Korean Patent Publication KR10-2019-0074356

The present disclosure is conceived in response to the above-described background technology, and is to provide a computer program for providing a document analysis method related to insurance subscription.

In one embodiment of the present disclosure for solving the above-described problem, a computer program stored in a computer-readable storage medium executable by one or more processors is disclosed. A computer program stored in the computer-readable storage medium, wherein the computer program, when executed by one or more processors, performs the following operations for analyzing a document for insurance subscription, and the operations include: Chatbot interface of a user terminal Determining whether to start insurance-related procedures based on the user's chat record received through the user's chat record; Receiving an insurance subscription document input through the chatbot interface from the user terminal; Inputting the insurance subscription document into a document analysis model and analyzing the insurance subscription document using the document analysis model; And determining insurance subscription based on the analysis result of the insurance subscription document.

Alternatively, the insurance subscription document may include an image of a document related to insurance subscription.

Alternatively, the operation of receiving the insurance subscription document from the user terminal through the chatbot interface includes: transmitting insurance guide information to the user terminal through a chatbot interface provided to the user terminal; Receiving a user confirmation signal for the insurance guidance information of a user through a chatbot interface provided to the user terminal; And receiving the insurance subscription document based on an upload input for the insurance subscription document by a user through a chatbot interface provided to the user terminal.

Alternatively, the operation of analyzing the insurance subscription document using the document analysis model by inputting the insurance subscription document into a document analysis model includes one or more characters included in the insurance subscription document using the document analysis model and Identifying a signature; And reading the structure of the insurance subscription document by performing natural language processing on the one or more characters using the document analysis model.

Alternatively, the operation of reading the structure of the insurance insured document by performing natural language processing on the one or more characters using the document analysis model is based on the natural language processing result of the insured document. Identifying one or more items included in; And reading the structure of the insurance subscription document based on a result of a context analysis of the one or more items.

Alternatively, the operation of reading the structure of the insurance policy document based on the context analysis result for the one or more items includes comparing the context analysis result with an existing insurance policy document structure to read the structure of the insurance policy document. action; Or inputting the context analysis result to a structure analysis submodel to read the structure of the insurance subscription document; At least one operation may be included.

Alternatively, the structural analysis sub-model is learned based on the analysis result of the imaged insurance policy document, and the result of comparing the structure information included in the selected essential document and the structure information of the imaged insurance policy using a policy network It determines whether to select other essential documents by processing them, calculates the reward based on the structure information included in the other essential documents and the structure information of the imaged insured document, and calculates other essential documents that can maximize the reward. Can be learned to choose.

Alternatively, the determining of insurance subscription based on the analysis result of the insurance subscription document may include: recognizing at least one of the types or contents of the insurance subscription document based on the analysis result of the insurance subscription document; And determining insurance subscription based on at least one of the type or content of the insurance subscription document.

Alternatively, the operation of determining insurance subscription based on at least one of the types or contents of the insurance subscription document is an operation of generating an examination result for insurance subscription based on at least one of the types or contents of the insurance subscription document. It may include.

Alternatively, the operation of generating the examination result for insurance subscription based on at least one of the type or content of the insurance subscription document is an operation of generating underwriting supplementary information based on the recognition result for each item included in the insurance subscription document. It may include.

Alternatively, the operation of generating the examination result for insurance subscription based on at least one of the type or content of the insurance subscription document is an operation of generating conditional approval information based on the recognition result for each item included in the insurance subscription document. It may include.

Alternatively, the determining of insurance subscription based on the analysis result of the insurance subscription document may include generating document supplemental information for insurance subscription based on the analysis result of the insurance subscription document; And transmitting the supplemental information to the user terminal.

Alternatively, the operation of generating document supplemental information for insurance subscription based on the analysis result of the insurance subscription document may include determining whether or not an omission item exists in the insurance subscription document; And determining whether an anomaly exists in the item included in the insurance subscription document.

Disclosed is a method for providing a document analysis method related to insurance subscription according to another embodiment of the present disclosure. The method includes the steps of determining whether to start an insurance-related procedure based on a chat record of a user received through a chatbot interface of a user terminal; Receiving an insurance subscription document input through the chatbot interface from a user terminal; Inputting the insurance subscription document into a document analysis model and analyzing the insurance subscription document using the document analysis model; And determining insurance subscription based on the analysis result of the insurance subscription document.

A server for providing a document analysis method related to insurance subscription according to another embodiment of the present disclosure is disclosed. The server includes a processor including one or more cores; Memory; And a network unit that transmits and receives data to and from the user terminal; wherein the processor determines whether to start insurance-related procedures based on the user's chat record received through the chatbot interface of the user terminal, and the chatbot interface from the user terminal. Receives an insurance subscription document input through, inputs the insurance subscription document into a document analysis model to analyze the insurance subscription document using the document analysis model, and based on the analysis result of the insurance subscription document You can decide to buy insurance.

The present disclosure may provide a computer program for providing a method related to insurance subscription more efficiently using a document analysis model.

Various aspects are now described with reference to the drawings, wherein like reference numbers are used collectively to refer to like elements. In the examples that follow, for illustrative purposes, a number of specific details are presented to provide a comprehensive understanding of one or more aspects. However, it will be apparent that such aspect(s) may be practiced without these specific details.
1 shows a conceptual diagram showing a system in which various aspects of a server for providing a document analysis method related to insurance subscription of the present disclosure may be implemented.
2 is a block diagram of a server for providing a document analysis method related to insurance subscription according to an embodiment of the present disclosure.
3 is a schematic diagram illustrating a network function that is a basis of a structure analysis model for determining the structure of a document according to an embodiment of the present disclosure.
4 is a diagram illustrating a method of analyzing documents related to insurance subscription according to an embodiment of the present disclosure.
5 is a flowchart illustrating a method for analyzing documents related to insurance subscription according to an embodiment of the present disclosure.
6 illustrates logic for implementing a document analysis method related to insurance subscription according to an embodiment of the present disclosure.
7 shows a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is clear that these embodiments may be implemented without this specific description.

As used herein, the terms "component", "model", "system" and the like refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or execution of software. For example, a component may be, but is not limited to, a process executed on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a computing device and a computing device may be components. One or more components may reside within a processor and/or thread of execution. A component can be localized within a single computer. A component can be distributed between two or more computers. In addition, these components can execute from a variety of computer readable media having various data structures stored therein. Components can be, for example, via a signal with one or more data packets (e.g., data from one component interacting with another component in a local system, a distributed system and/or a signal through another system and a network such as the Internet. Depending on the data being transmitted), it may communicate via local and/or remote processes.

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or”. That is, unless specified otherwise or is not clear from the context, "X employs A or B" is intended to mean one of the natural inclusive substitutions. That is, X uses A; X uses B; Or when X uses both A and B, "X uses A or B" can be applied to either of these cases. In addition, the term “and/or” as used herein should be understood to refer to and include all possible combinations of one or more of the listed related items.

In addition, the terms "comprises" and/or "comprising" should be understood to mean that the corresponding features and/or components are present. However, it is to be understood that the terms "comprising" and/or "comprising" do not exclude the presence or addition of one or more other features, elements, and/or groups thereof. Further, unless otherwise specified or when the context is not clear as indicating a singular form, the singular in the specification and claims should be interpreted as meaning "one or more" in general.

Those of skill in the art would further describe the various illustrative logical blocks, configurations, models, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein, including electronic hardware, computer software, or a combination of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, models, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the specific application and design limitations imposed on the overall system. Skilled technicians can implement the described functionality in various ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The scope of rights to the actions in the claims of the present disclosure is generated by the functions and features described in each action, and unless the precedence relationship of the order in each action is specified, in the claims It is not affected by the order of description of each operation. For example, in the claims described as operations including operations A and B, even if operation A is described before operation B, the scope of rights is not limited to that operation A must precede operation B.

Network functions and neural networks in this disclosure can often be used interchangeably.

1 shows a conceptual diagram showing a system in which various aspects of a server for providing a document analysis method related to insurance subscription of the present disclosure may be implemented.

A system according to embodiments of the present disclosure may include a document analysis server 100, a user terminal 10, an external server 20, and a network. The document analysis server 100, the user terminal 10, and the external server 20 according to the embodiments of the present disclosure may mutually transmit and receive data for the system according to the exemplary embodiments of the present disclosure through a network. .

2 is a block diagram of a server for providing a document analysis method related to insurance subscription according to an embodiment of the present disclosure.

The server 100, which provides an insurance subscription method using a chatbot according to an embodiment of the present disclosure, responds to customer inquiries in a one-on-one conversation with a user for one of counseling, inquiries, or customer service related to personal financial products. Can be used to Here, personal financial products may refer to general products related to finance such as insurance, loans, pensions, and funds.

The document analysis server 100 using a chatbot according to an embodiment of the present disclosure may include a network unit 110, a processor 120, and a memory 130.

According to an embodiment of the present disclosure, the network unit 110 may transmit and receive information with the user terminal 10 and the external server 20. Specifically, the network unit 110 may receive a chat record of a user generated through a chatbot interface provided to the user terminal 10. The network unit 110 may transmit the response of the chatbot generated based on the input signal of the user terminal 10 by the processor 120 to the user terminal 10. Accordingly, the user may interactively interact with the chatbot through the user terminal 10, and a chat record of the user may be generated based on this. In addition, the network unit 110 may receive a user's confirmation signal from the user terminal 10 and may receive the user's personal information from the external server 20. In addition, the network unit 110 may receive the insurance subscription document based on an upload input of the user's insurance subscription document through a chatbot interface provided to the user terminal 10. The insurance subscription document may include one or more documents and documents related to insurance subscription predetermined based on insurance products and types, and may include an image of a document related to insurance subscription applied by the user.

The document analysis server 100 according to an embodiment of the present disclosure may generate an answer of a chatbot provided to the user terminal 10. Using the chatbot interface by the user may include chatting with the chatbot through the user terminal 10.

The processor 120 may determine whether to start an insurance-related procedure based on the user's chat record received through the chatbot interface of the user terminal. The processor 120 may determine whether the user wants a simple chatbot service or a procedure related to insurance based on the user's chat record. When it is determined that the user wants the insurance-related procedure, the processor 120 may initiate the insurance-related procedure. The processor 120 may determine which of the insurance-related procedures is desired to be initiated based on the user's chat record. The processor 120 processes the chat record in natural language to determine the context of the chat record, and the processor 120 detects, for example, a change in the user's personal information in the context of the chat record, It is possible to determine which insurance procedure should be initiated for the user through relevance search or the like. Insurance-related procedures may include a user's insurance subscription procedure, a claim procedure, an inquiry procedure, and the like. For example, the processor 120 may cause the insurance-related procedure to be initiated on the chatbot interface when the user inputs a chat of meaningful content related to insurance. For example, the processor 120 may initiate an insurance claim procedure, an insurance subscription procedure, an insurance subscription induction procedure, and the like for the user based on a chat record indicating that the user has gone to the orthopedic clinic.

The processor 120 may inquire personal information of a user using the chatbot interface from the database. The processor 120 may generate a message (eg, providing a choice as to whether or not to consent) requesting that the user be allowed to inquire about personal information. A message delivered to the user terminal 10 through the network unit 110 may be displayed through a chatbot interface. When a signal allowing the user's personal information to be inquired is received from the user terminal 10, the processor 120 may query the user's personal information.

The user's personal information may include information about the current user's status, such as the user's marital status, the presence of children, and job status, user identification information and insurance subscription history. User personal information may be information previously stored in a database. When the user identification information is not searched, the processor 120 may generate a message requesting input of the user identification information. The generated request message may be transmitted to the user terminal 10, and user identification information may be generated based on an input signal of the user terminal 10 receiving the request message.

The processor 120 according to an embodiment of the present disclosure may generate a response from a chatbot that recommends an insurance product based on a user's personal information and a chat record. Specifically, the processor 120 may receive personal information of the user inquired from the database through the network unit 110 and a chat using the user's chatbot interface.

In addition, the processor 120 may recognize changes in personal information of the user based on the received chat record. For example, when it is determined that content related to a user's marriage is determined among the chat records, the processor 120 may recognize that there is a change in the user's personal information. Accordingly, the processor 120 may generate an answer of a chatbot that asks a user a question regarding a change in personal information. The generated answer may be transmitted to the user terminal 10 through the network unit 110, and may be displayed as a question related to changes in personal information through a chatbot interface. The user's answer to the question related to the change in personal information is based on an input signal of the user terminal 10 and may be received by the network unit 110. Accordingly, the processor 120 may modify user personal information based on the user's response. Specifically, a user's answer to a question related to a change in personal information may be first classified into positive and negative. Upon receiving the positive answer, the processor 120 may generate an answer from the chatbot requesting an additional input for the changed personal information through the chatbot interface. The response of the generated chatbot may be transmitted from the network unit 110 to the user terminal 10 so as to be displayed by the chatbot interface. The processor 120 may modify personal information based on a positive answer and additional input from the user terminal 10. For example, when an affirmative answer and a change in marital status are additionally input from the user terminal 10, the processor 120 may modify the marital status of the user's personal information based on the change.

The processor 120 according to an embodiment of the present disclosure may select one or more insurance products having a high subscription probability of a user as recommended insurance based on user personal information. Specifically, the processor 120 may select an insurance product having a high subscription probability based on personal information or an insurance product having a high subscription probability based on a change in personal information as the recommended insurance. For example, the processor 120 may select an insurance product having a high subscription probability as a recommended insurance based on the insurance need situation that may occur to the user and the user's subscription capacity. The insurance need situation and the user's income may be determined by the processor 120 based on the user's personal information. For example, the processor 120 may determine that an unmarried user needs insurance as a disability situation and medical expenses. Accordingly, it is possible to select insurance for unmarried users as recommended insurance, which is related to medical expenses for injured medical expenses, late disabilities, and diagnosis expenses for diseases requiring medical treatment. For another example, the processor 120 may determine that the condition in need of insurance for a user with dependents is the death of a main source of income. Accordingly, the processor 120 may select death insurance as a recommended insurance for a user with dependents. In addition, the processor 120 may select an insurance product related to either life insurance or periodic insurance as a recommended insurance based on the user's subscription capacity.

The processor 120 according to an embodiment of the present disclosure may select one or more insurance products without a user's insurance subscription history as recommended insurance based on the chat record. Specifically, the processor 120 may obtain an insurance subscription history based on the user's personal information, and determine an insurance product without the user's insurance subscription history as a recommended insurance. In addition, the processor 120 may select an insurance required for the user as a recommended insurance based on the user's chat record. In addition, the processor 120 may select a recommended insurance by selecting an insurance required for the user based on the user's chat record and filtering through the user's subscription history. For example, the processor 120 may receive a chat record of a user's visit to a hospital. In this case, the processor 120 may determine that the insurance required for the user is medical expenses insurance or accident insurance, and the processor 120 may determine that there is accident insurance in the user's insurance subscription history, but if it is determined that there is no actual expenses insurance, the actual expenses Insurance products related to insurance can be selected as recommended insurance.

According to an embodiment of the present disclosure, one or more recommended insurances selected by the processor 120 may be displayed through the chatbot interface of the user terminal 10. Specifically, the processor 120 may generate a response from the chatbot related to the recommended insurance and transmit it to the user terminal 10 through the network unit 110. Accordingly, by displaying an answer related to the recommendation insurance through the chatbot interface of the user terminal 10, a recommendation insurance with a high probability of a user signing up can be recommended in the form of a chat message in the chatbot interface. In addition, the selected one or more recommended insurance may be displayed as options so that the user can select through the chatbot interface. Accordingly, the processor 120 may select an insurance product having a high subscription probability as a recommended insurance based on the user's personal information and chat content. In addition, it is possible to induce the user to subscribe to insurance by recommending a recommended insurance with a high subscription probability to the user through the chatbot interface of the user terminal 10.

According to an embodiment of the present disclosure, the processor 120 may transmit insurance information including recommended insurance information to the user terminal 10 through the network unit 110. In addition, when generating insurance information including recommended insurance, the processor 120 may differentiate and display insurance information requested by the user and recommended insurance information. For example, the processor 120 may receive a chat record including a request for information related to death insurance from the user terminal 10 through the network unit 110. In this case, the processor 120 may generate information related to insurance requested by the user and recommended insurance information based on the user request. Also, based on the fact that the user's income level is not high, the processor 120 may display information on the death insurance related to the periodic insurance separately from information requested by the user among the death insurance. In addition, the processor 120 may allow the user to display information on recommended insurance and insurance information requested by the user separately through the chatbot interface of the user terminal 10.

According to an embodiment of the present disclosure, the processor 120 may perform an additional operation by grasping a user intention for insurance information. For example, the user intention for insurance information may be determined based on the user's chat record input through the chatbot interface after providing the insurance information. The processor 120 may determine the user intention for the insurance information based on the user's chat record input through the chatbot interface after the insurance information is provided. The user intention for insurance information may include, for example, initiating insurance-related procedures for insurance information, requesting additional information for insurance information, requesting other insurance information, and the like.

After transmitting the insurance information according to an embodiment of the present disclosure, the processor 120 may additionally transmit a question for determining the user's intention for insurance information including recommended insurance information to the user terminal. A question for identifying the user's intention for insurance information may be displayed through a chatbot interface of the user terminal 10, and the processor 120 may receive a user's opinion based on the user's input to the chatbot interface. For example, the processor 120, which has transmitted the actual cost insurance as a recommended insurance, may generate a message from the chatbot as "What do you think of the recommended actual cost insurance?" in order to determine the user's subscription intention. In this case, the generated chatbot message may be a form that requires a user's natural language input, but may be a form in which the user selects an option suggested by the chatbot message in the chatbot interface. The above-described answer from the chatbot is only an example, and the present disclosure is not limited thereto. Accordingly, the processor 120 may identify the user's intention from the user response received from the user terminal 10 and generate a new chatbot response.

Specifically, the processor 120 may receive a response from a user who intends to request information about the recommended insurance. For example, when the processor 120 receives, through the user terminal 10, a question related to coverage for actual cost insurance, which is a recommended insurance, it may determine that the user intends to request information. Accordingly, the processor 120 may generate an answer including information related to the insurance coverage and transmit it to the user terminal 10 to be displayed through the chatbot interface.

After transmitting the insurance information to the chatbot interface, the processor 120 may recognize the user's intention and propose an additional insurance-related procedure when receiving a negative chat record from the user. When the processor 120 receives an opinion that there is no intention to subscribe from the user terminal 10, the processor 120 may generate a question about the reason for not signing up as an answer of the chatbot so that the question about the reason for not signing up is displayed through the chatbot interface. In addition, the processor 120 may display a question related to a user's intention to join the chatbot interface in the form of a natural language or a choice. For example, the option for why you are not willing to subscribe may be indicated by the insurance you have already subscribed to, the amount of coverage, and premiums. At least one of the options may be selected based on the touch input of the user terminal 10 because there is no intention to subscribe. In this case, the processor 120 may recommend another insurance based on the reason that the user does not intend to subscribe, or may generate a response from a chatbot that provides detailed information of the insurance. For example, if the reason for not signing up for life insurance is an insurance premium based on the input signal of the user terminal 10, the processor 120 may select an insurance that recommends periodic insurance instead of life insurance. Accordingly, the processor 120 may determine the reason for not signing up through the chatbot and provide real-time feedback on insurance that is not suitable for the user. Accordingly, the processor 120 may recommend an optimal insurance to the user to induce subscription. In the case of a user, when purchasing insurance, it is possible to conveniently request information on a number of insurances, and since the chatbot can provide information whenever the user wants by conducting 24-hour consultation, convenience of use can be improved. In addition, in the case of insurance companies, by performing subscription counseling through a chatbot interface, it is possible to reduce the cost of manpower related to counseling.

The processor 120 according to an embodiment of the present disclosure may generate a response from the chatbot to detailed information of the recommended insurance based on the reason that the user does not intend to subscribe. For example, if the recommended insurance is a regular insurance related to medical expenses, the processor 120 may generate an answer from a chatbot that recommends one or more special contracts based on the reason that there is no intention to subscribe. The processor 120 may select one or more special contracts based on the user's opinion on the response of the chatbot. Accordingly, the processor 120 may determine the insurance that the user intends to subscribe to based on the selected special contract. Accordingly, the processor 120 may provide insurance that satisfies the user's intention to subscribe by selecting detailed conditions of the recommended insurance through the chatbot interface. The user can conveniently add or remove a number of special contracts that can be added to insurance through the chatbot interface, thereby improving usability in insurance subscription.

The processor 120 may determine a user's intention to initiate an insurance-related procedure based on the chat record. In this case, the processor 120 may determine what insurance-related procedures the user wants to process through the chatbot interface, based on chat records, user information, and the like. The processor 120 may determine whether an insurance-related procedure that the user intends to process through the chatbot interface can be processed through the chatbot interface or a procedure that should be processed by an administrator. For example, the processor 120 may determine to initiate an insurance subscription procedure when the user desires to subscribe to simple daily accident insurance based on the user's chat record. In this case, the processor 120 may determine that the corresponding procedure can be processed as a consultation through a chatbot interface according to the type of insurance-related procedure that the user intends to initiate. In other words, if the procedure that the user wants to handle is of low degree of attention based on the insurance company's predetermined criteria (e.g., the sign-up procedure is simple, there are few required documents, the amount of coverage is small, the coverage period is short, etc.) , The processor 120 may determine that this is a process that can be processed through the chatbot interface, and determine the start of processing through the chatbot interface of the insurance-related procedure.

As described above, a user can conveniently inquire about insurance through the chatbot interface according to an embodiment of the present disclosure, and the chatbot can respond 24 hours a day, thereby improving user convenience. In addition, insurance-related information can be easily provided through a familiar chatbot interface for generations who are reluctant to consult by phone or face-to-face, so usability can be improved.

The processor 120 according to an embodiment of the present disclosure may start a subscription process for insurance for which a user has applied for a subscription process through a chatbot interface. Specifically, when receiving an opinion that there is an intention to subscribe from the user terminal 10, the processor 120 may select an insurance product related to the intention to subscribe. In addition, after providing insurance information to the user, the processor 120 may determine an insurance product by hitting an input on the user's chatbot interface for insurance information. The processor 120 may generate a message for performing an insurance subscription procedure on the chatbot interface and transmit the message to the chatbot interface.

The processor 120 may generate a response from the chatbot to inform the user about one or more essential documents required for the subscription process in relation to the insurance to initiate the subscription process. For example, the processor 120 may generate an answer from the chatbot about the types of documents required for registration. For example, the processor 120 may generate a medical certificate and a medical examination result sheet as a chatbot message to be transmitted to the user through the chatbot interface to sign up. The above-described answer from the chatbot is only an example, and the present disclosure is not limited thereto.

The processor 120 according to an embodiment of the present disclosure may transmit a message of the chatbot for uploading essential documents for insurance procedures to the chatbot interface. The processor 120 may generate a response from the chatbot to notify the user about how to upload one or more required documents. For example, the processor 120 may designate an update location for each required document, generate a chatbot message, and transmit it to the chatbot interface. For example, a user can access the document upload interface by clicking a message on the chatbot and upload documents to the document upload interface. In addition, the processor 120 transmits a message specifying the document to be requested to the user through the chatbot interface, and after the message specifying the document, a predetermined time range, files received within the message range (e.g., images, pdf files, etc.) ) Can be determined as an upload for the document. The processor 120 transmits the requested document to the user through the chatbot interface in the form of a chat message, and associates the file uploaded from the user with the chat message for the request document of the chatbot to determine the type of the document of the file uploaded from the user. . For example, the processor 120 may deliver a message requesting a health checkup result sheet to the user through a chatbot interface, and the user may upload a file related to the medical checkup result sheet through a file sharing interface provided in the chatbot interface. have. The processor 120 may determine a correlation between a file uploaded by a user and a message of a chatbot within a time range or a message range, and determine that the corresponding file is a health checkup result sheet.

The processor 120 according to an embodiment of the present disclosure may transmit a guide document requiring a user's signature for insurance subscription, and obtain a user's signature from an input to the user's signature input interface provided in the chatbot interface. have.

Hereinafter, a method for analyzing documents related to insurance subscription according to an embodiment of the present disclosure will be described.

According to an embodiment of the present disclosure, the processor 120 may receive a document related to insurance subscription from the user terminal 10. Specifically, the processor 120 may transmit insurance guide information to the user terminal 10 based on the type of insurance for which the user has applied for subscription through a chatbot interface provided to the user terminal 10. The insurance guide information may include information capable of providing a user with a guide related to subscription based on an insurance product. For example, the insurance guide information may include information related to required documents required for subscribing to an insurance product. Accordingly, the processor 120 may receive a user's confirmation signal for the insurance guidance information, and receive the insurance subscription document based on the upload input of the user's insurance subscription document through a chatbot interface provided to the user terminal I can. The insurance subscription document may include one or more documents related to the subscription of the insurance for which the user has applied for subscription. For example, it may be a photographed image of a document (eg, insurance subscription application) related to insurance subscription.

According to an embodiment of the present disclosure, the processor 120 may obtain information on one or more essential documents from the external server 20 based on the type of insurance for which the user has applied for subscription. Required documents may include documents required for insurance-related procedures, and may be existing documents stored in a database. For example, the required documentation may include the documentation necessary to purchase insurance. For example, it may include a medical certificate, a contract, etc. necessary for insurance subscription. Insurance subscription of the user may be determined through at least one review process including contract review.

According to an embodiment of the present disclosure, the processor 120 may analyze the insurance subscription document using the document analysis model by inputting the insurance subscription document into the document analysis model. Specifically, the processor 120 may identify one or more characters and signatures included in the insurance subscription document by using the document analysis model on the insurance subscription document received from the user terminal 10. The signature may include a handwritten signature and an electronic signature that can identify at least one of the policyholder and the insured. In addition, the document analysis model may identify one or more characters included in the imaged insurance policy using optical character recognition (OCR) technology. One or more characters included in the insurance subscription document may include characters or symbols written by the user or the insured. Specifically, one or more characters included in the insurance subscription document may include characters or symbols recorded on the insurance subscription document by handwriting of the user or the insured.

In addition, the document analysis model can read the structure of the insurance policy by performing natural language processing on the identified one or more characters. The structure of the read insurance policy document may be generated as structure information of the insurance policy document through a document analysis model. The structure information of the insurance policy document includes information on one or more items included in the insurance policy document. It can include information about each location. Specifically, when the imaged insurance subscription document is an insurance subscription application, the document analysis model may first classify titles and contents by performing natural language processing on one or more characters identified through optical character recognition. For example, the document analysis model may determine the type of the insurance subscription document as the insurance subscription application based on the title of the insurance subscription document imaged through context analysis on the insurance subscription document. In addition, the document analysis model may determine one or more items including names such as “name” and “gender” indicating identification information of the insured in the contents of the insurance subscription document. In addition, the document analysis model may extract location information for one or more items as information on the structure of an imaged insurance subscription document, and associate the location information with the identified name information for each item. The location information for one or more items may include information on a location relationship between items and a location of each item in the document.

The processor 120 according to an embodiment of the present disclosure may obtain information on essential documents of an existing stored database from the external server 20 through the external server 20 based on the type of insurance product applied through the user terminal. I can. The required document is a document related to insurance subscription including one or more essential items required for insurance subscription based on the type of insurance the user has applied for, and at least one or more essential documents based on one or more types may each be included in the database. . The information on the essential document includes information on the structure of the essential document, and the structure information on the essential document may include name and location information on one or more essential items.

In addition, the processor 120 generates document supplementation information when at least one or more types of insurance subscription documents received from the user do not correspond based on the types of one or more essential documents obtained from the external server 20. can do. Specifically, if there are 4 required documents based on the type of insurance, and there are not 4 insurance documents received from the user, or there is no corresponding type of insurance subscription documents, document supplementation information for requesting feedback from the user is provided. Can be generated. In addition, the processor 120 may generate document supplementation information based on whether characters and symbols corresponding to the structure information of the insurance subscription document generated through the document analysis model are identified.

The processor 120 according to an embodiment of the present disclosure acquires information on essential documents for analyzing the structure information of the insurance subscription document through the network unit 110, and the structure information on the items of the insurance subscription document is It may be stored in the memory 130.

According to an embodiment of the present disclosure, the processor 120 may compare the context analysis result of the document analysis model with the structure of an essential document that is an existing insurance subscription document to read the structure of the insurance subscription document, and perform the structure analysis submodel. Through this, the structure of the insurance policy can be read. The processor 120 may search for essential documents for efficiently determining the structure of the insurance subscription document through the structure analysis submodel learned using the reinforcement learning method. The structural analysis sub-model may be a reinforcement learning model using a policy network and a value network.

The structural analysis sub-model is learned based on the analysis result of the imaged insurance policy document. The structural analysis sub-model determines whether to select another required document by processing the comparison result of the structure information included in the selected required document and the structure information of the imaged insurance policy using the policy network, and determines whether to select another required document. It can be learned to calculate the reward based on the structure information and the structure information of the imaged insured document, and to select another required document that can maximize the reward.

The structure analysis sub-model, in which structure information on one or more items included in the insurance subscription document is input through the document analysis model, can search for essential documents having a structure similar to the input structure information. The structural analysis sub-model has the searched essential document as a state, the document search is an action, and the reward is calculated based on the comparison result of the searched essential document and the structure information of the input insurance policy. can do. The structural analysis sub-model can calculate rewards based on essential documents and structural information. The structure analysis submodel may determine whether to search for a new document based on a result of comparing the searched essential document with the structure information. The structure analysis sub-model may determine at least one of whether to search for a new document or which new document is to be searched by processing the searched essential documents and structure information using the policy network. The processor 120 may determine whether to search for a new document or which new document to search by using the structure analysis submodel. The structural analysis sub-model can explore new documents to maximize rewards. The value network of the structural analysis submodel can be learned as to which new document exploration will maximize the rewards. The structure analysis sub-model may determine a reward based on the newly searched document and structure information. The reward may be determined based on a result of comparison between the searched document and structure information, and may be a measure related to the degree of similarity between the searched document and structure information. The processor 120 may search for an essential document having a structure most similar to the insurance subscription document by using the structure analysis sub-model learned using the reinforcement learning methodology.

The structure analysis sub-model's policy network can determine whether to search for new essential documents or which new documents to search. Required documents may include documents required for insurance-related procedures, and may be existing documents stored in a database. For example, the required documentation may include the documentation necessary to purchase insurance. For example, it may include a medical certificate, a contract, etc. necessary for insurance subscription. Which new document is to be searched may include, for example, a type of a document to be searched newly, and a criterion for selecting a document in a next state based on what from a group to which the document to be searched belongs. For example, when the correspondence between the currently searched essential document and the structure information is high, the policy network may be learned to determine that searching for a new essential document is unnecessary. Also, for example, when the correspondence between the currently searched essential document and the structure information is low, the policy network may be learned to determine that search for a new document is necessary. In this case, the policy network may determine a cluster of essential documents to newly search for a document based on the type of the insurance policy document identified based on the structure information. For example, if the insurance subscription document is an insurance subscription application, the policy network may be trained to determine the type of the identified insurance subscription document and the search for the required document belonging to the corresponding group, if a new required document search is required.

Specifically, the type and structure information of the insurance subscription document received from the user is generated through the document analysis model of the processor 120, and the document analysis model analyzes the structure information on the type and item of the received insurance subscription document. Can be entered into the model. The structure analysis sub-model is trained to find a cluster of one or more essential documents corresponding to a predetermined type of insurance policy through the document analysis model in the database. The structure analysis sub-model performs a search for one or more essential items included in one essential document in order to select an optimal essential document for determining the structure of the insurance policy. The purpose of the search is to find the required documents that contain the most essential items that are most similar to one or more of the items contained in the insurance policy. The structural analysis sub-model determines the reward according to the similarity between the structural information of the essential document and the insurance policy, and when the processor 120 performs an action to search for other essential documents, other essential documents newly retrieved through this action are currently It can be learned to evaluate whether it contains more essential items of structural information that are more similar than essential documents through the value function. For example, when determining the insurance subscription document received from the user terminal 10 as an insurance subscription application, the processor 120 inputs information on the name of each item included in the insurance subscription document into the structure analysis submodel. I can. The structure analysis sub-model may be a model trained to find a cluster of required documents corresponding to a predetermined insurance subscription application form from a database. Accordingly, the processor 120 uses the structure analysis submodel to select any one essential document from the cluster of essential documents, the insurance subscription document (ie, the insurance subscription document received from the user terminal) determined as the insurance subscription application. Rewards are calculated based on how many required items are most similar to the name for each item. Specifically, the processor 120 includes all the items of the insurance subscription application in which the essential items included in the first insurance subscription application arbitrarily selected from the cluster of essential documents using the structural analysis submodel are received, and other essential documents of the cluster In the case where the mandatory items included in the second insurance application form include only the name of the insured, the reward for the first insurance application may be determined to be higher than the reward for the second insurance application. In this case, the processor 120 selects the first insurance subscription application using the structure analysis model and calculates a reward for the first insurance subscription application. Accordingly, the structural analysis sub-model using reinforcement learning determines the reward based on the comparison result of each required document for the document to be judged, and compares the reward for each required document to the required document with a larger reward. There is a tendency to choose. After much learning, you can find the required documents that contain the most essential items that are similar to the names of the items included in the insurance subscription documents you received. In addition, when reinforcement learning is used to search for essential documents including the most items included in insurance subscription documents, it is possible to search faster than other machine learning.

For a description of specific contents related to information retrieval using reinforcement learning for quick retrieval, see the information retrieval using reinforcement learning of the Korean Information Science Society, which is incorporated by reference in its entirety in this disclosure. Byeong-tak Jang) is discussed in detail

The processor 120 according to an embodiment of the present disclosure reads the structure of the insurance subscription document based on the last selected essential document including the most names of the items of the insurance subscription document through the structure analysis submodel. It may not be a fully responsive structure for all items in the insurance policy document you receive. That is, the position and name of the required item of the finally selected essential document and the item included in the received insurance subscription document may not correspond to each other. For example, if a required document that is most similar to the insurance policy document is finally selected from the database through the structural analysis submodel, and the required document contains 7 required items, the items included in the insurance policy may not be 7 have. Alternatively, for the items included in the insurance subscription document, the required items included in the essential documents may not correspond to the required items. Alternatively, each location of the essential items and each location of the items included in the insurance subscription document may not correspond to each other.

The processor 120 according to an embodiment of the present disclosure may determine the insurance document received from the user terminal 10 based on the essential document finally selected by the structural analysis submodel. Insurance documents can include all kinds of documents related to insurance. For example, the insurance document may include documents related to insurance subscription, claim, and the like. The insurance document may include, for example, an insurance subscription document, and may include an application form, medical certificate, and the like required for insurance subscription. Specifically, the processor 120 states that the number of essential items of the finally selected essential document and the number of items of the insurance subscription document do not correspond, or that the name of at least one essential item does not correspond to the name of the item of the insurance subscription document. I can judge. In this case, the processor 120 may generate document supplementation information for informing the user of information on a non-corresponding essential item received from the user terminal 10. For example, the applicant's name, age, and insurance application content items are included in the first insurance subscription application finally selected by the structural analysis submodel, and the insurance subscription application received from the user includes the applicant's name and insurance subscription content items. In the case of including only, the processor 120 may generate information on the age item of the first insurance subscription application that does not correspond to the required item of the selected first insurance subscription application as document supplement information.

In addition, the processor 120 compares the location information of the items included in the received insurance subscription document based on the last selected essential document, and when the location information is different, the processor 120 provides structure information for the uncorresponding item. Can be created as new structural information. Specifically, the processor 120 may determine whether the items of the received insurance subscription document correspond to the essential items included in the essential document by using the structure analysis submodel. The processor 120 may compare the location information of the items of the insurance subscription document received from the document analysis model based on the location information of the essential items using the structure analysis submodel. When the location information of the at least one item does not correspond to the location information of the essential item, the processor 120 may generate structure information of the insurance subscription document including the non-corresponding item as new structure information. The structure information of the insurance subscription document is based on a result analyzed through the document analysis model, and may include structure information on one or more items included in the insurance subscription document. For example, the location of the name item of the applicant included in the received insurance application is at the top of the insurance application, but the location of the name of the applicant in the first insurance application, which is a required document, is the location of the first insurance application. It could be the bottom. Accordingly, the processor 120 recognizes the applicant's name item that is different from the position of the required item of the essential document as new structure information of the insurance subscription application using the structure analysis submodel, and includes the applicant's name item located at the top. The entire structure information of the insurance application form can be created as new structure information. Accordingly, the structure analysis sub-model of the processor 120 may transmit new structure information in which the names and locations of each item of the received insurance subscription document are associated with each other to the external server 20. Accordingly, the new structure information may be stored in a database of the external server 20 as structure information of an essential document and transmitted to the document analysis server 100. The transmitted new structure information may be used by the processor 120 to determine the structure of the insurance subscription document.

Also, the processor 120 may determine an insurance subscriber based on at least one of first user information obtained from the user terminal 10 or second user information obtained from an insurance subscription document. Accordingly, the insurance subscription for the insured may be determined based on at least one of the types or contents of the insured document based on the analysis result of the insured document. The decision on insurance subscription may perform an examination on insurance subscription based on at least one of the types or contents of the insurance subscription document for the insured, and may generate a result of the examination. The result of the examination may include conditional approval information based on the recognition result for each item included in the insurance subscription document. Specifically, the processor 120 may generate conditional approval information based on the recognition result for each item included in the insurance subscription document, and the processor 120 may generate a review result for insurance subscription based on the conditional approval information. I can. For example, the processor 120 may generate conditional approval information including a condition for setting an exemption range for an insurance subscriber with diabetes. Alternatively, the processor 120 may generate conditional approval information by recognizing an item based on a doctor's certificate including a proof that the subscriber's blood sugar has decreased by a predetermined value for the insurance subscriber with diabetes. Alternatively, the processor 120 provides conditional approval information based on whether or not conditions for the recorded information are achieved by linking with an exercise-related application such as a Samsung Health application for an insurance subscriber with diabetes to achieve a certain number of workouts or more for a certain period of time. Can be generated. The processor 120 may generate a screening result for insurance subscription for an insurance subscriber with diabetes based on the recorded information on which exercise has been achieved or a medical certificate in which blood sugar reduction is recorded. The foregoing description is for illustrative purposes only, and the present disclosure is not limited thereto.

The processor 120 according to an embodiment of the present disclosure may perform a review on insurance subscription to determine insurance subscription based on an analysis result of the insurance subscription document. The examination for insurance subscription may include a contract examination, and the processor 120 may generate underwriting supplementary information based on the recognition result for each item included in the insurance subscription document for the examination. The underwriting supplement information may include information based on the contract examination result, and the processor 120 may generate the examination result based on the underwriting supplement information. Accordingly, the processor 120 may request a user's feedback based on the underwriting supplement information to the user terminal 10 or transmit a notification about the information of the examination result.

The document analysis server 100 according to an embodiment of the present disclosure recognizes at least one of the types or contents of the insurance subscription documents based on the analysis result of the insurance subscription documents, and determines at least one of the types or contents of the insurance subscription documents. You can decide to buy insurance on the basis of it. In addition, the document analysis server 100 may perform a review on insurance subscription based on at least one of the types or contents of the insurance subscription document, and generate a review result accordingly. In addition, the document analysis server 100 may generate underwriting supplemental information and conditional approval information based on a recognition result for each item included in the insurance subscription document.

In addition, the processor 120 may generate document supplement information for insurance subscription based on the analysis result of the received insurance subscription document. Document supplementation information is required to supplement the document, such as information on whether omission items exist in the insurance subscription document received from the user terminal 10, information on whether or not anomaly exists, and what supplementation should be made if necessary. It may contain information related to. Accordingly, the processor 120 may determine whether at least one missing item exists in the insurance subscription document, and may generate document supplementation information based on the one or more missing items. Specifically, the document supplemental information includes one or more information related to essential documents requiring user feedback, such as information on essential items that are missing or not recognized in the insurance subscription document, or additional input is required. Can include. Also, the document supplementation information may be generated by the processor 120 based on whether anomaly exists in an item included in the insurance subscription document. The anomaly may include, for example, a case where the item value for the item included in the insurance policy document deviates from the general trend. Specifically, the processor 120 may determine whether one or more anomalies exist in an item included in the insurance subscription document, and may generate document supplementation information based on the one or more anomalies. For example, the processor 120 may determine that an anomaly exists for the weight item when the content on the weight item of the insured is identified as “70g” in the analyzed document related to the insurance subscription. Accordingly, the processor 120 may generate document supplementation information on the weight item in order to request feedback from the user. In addition, the processor 120 may determine that an anomaly exists even when the contents of the same item are not matched for at least two or more insurance subscription documents for a corresponding type of insurance subscription. For example, the processor 120 may analyze two insurance subscription documents received from the user terminal 10 for the same type of insurance subscription, respectively. If the analyzed insurance enrollment document is an insurance enrollment application and a doctor's certificate, if it is judged that the name of the insurance enrollment application and the name of the physician's certificate are different among the items included in the identification item of the insured in each analyzed document In order to request feedback from the user, it is possible to create document supplementary information for the name of the insured. The generated document supplementation information may be transmitted to the user terminal 10 by the processor 120.

According to an embodiment of the present disclosure, the underwriting supplement information and the document security information may be transmitted to the user terminal 10 through the network unit 110, and feedback from the user terminal 10 based on the underwriting supplement information and the document supplement information You can receive information. The processor 120 may request this from the user through the chatbot interface when it is necessary to supplement the recognition content of the document or when an additional document for underwriting is required. The processor 120 may receive a supplemental message from a user through a chatbot interface and perform supplementation such as document recognition or underwriting. In this case, the feedback information may be information including the corrected content of the document corresponding to the document supplement information. Specifically, the processor 120 may generate a response from a chatbot notifying a user about a portion in the document where an anomaly exists and a portion that is not recognized as a text based on the document supplement information. For example, if the processor 120 determines that the name has not been recognized based on the document supplement information, the chatbot responds to this as "The name is not recognized in the document." Can be created with For another example, if the user's signature is not recognized, the processor 120 may generate “signature not recognized” as a response from the chatbot. For another example, if the processor 120 determines that an anomaly exists in the weight based on the document supplement information, the chatbot responds with "The weight is strange." I can create it with "Please check your weight again." The above-described answer from the chatbot is only an example, and the present disclosure is not limited thereto. Also, the processor 120 may transmit the document to the user terminal 10 so that the user can check the part of the document notified through the chatbot based on the document supplement information through the chatbot interface.

In addition, the processor 120 may additionally generate a response from a chatbot requesting to modify a portion of the document that is not recognized through the user terminal 10. For example, for a document whose name is not recognized, the processor 120 responds to the chatbot, “The name is not recognized in the document. Please enter your name here.” can be created. For example, if the processor 120 determines that the user's name is not recognized based on the document supplement information, it may transmit an input interface for inputting the user's name to the chatbot interface to request input of the name. For example, for a document whose signature has not been recognized, the processor 120 may say, “The signature is not recognized. Please sign here” as a response from the chatbot. For another example, for the anomaly of the weight of the document, the processor 120 says, “The weight is weird. Please re-enter your weight here.” can be generated as a response from the chatbot. Accordingly, the processor 120 may determine whether to modify the part of the document based on the input signal of the user terminal 10 in response to the chatbot's response. The above-described answer from the chatbot is only an example, and the present disclosure is not limited thereto.

In this way, if user confirmation or additional input is required for document recognition, the accuracy of document recognition can be improved by conveniently requesting an answer from the user through the chatbot interface, and the user can check the recognition result of the chatbot. As a result, the user's confidence in the chatbot can be increased, and the usability can be improved. Users can conveniently perform supplementary work on documents through chat with a chatbot, providing convenience to generations familiar with chatting, and insurance companies can improve recognition accuracy by requesting additional input from users. .

In addition, the chat record additionally input by the user may be stored in the server 100 and included in learning data for document recognition. In the case of a document that the user confirms the recognition result through the chatbot interface, new learning data can be generated by using the user's recognition result confirmation as a label for the document.

The processor 120 according to an embodiment of the present disclosure may transmit a confirmation request to the manager terminal for the modified part through the chatbot interface. In more detail, the processor 120 may transmit a document to which the modified portion is applied based on the input signal of the user terminal 10 and a request for confirmation thereof to the administrator terminal. Also, before receiving the confirmation signal from the manager terminal, the processor 120 may generate and transmit a response from the chatbot notifying the status in which the manager is performing the verification, and transmit it to the user terminal 10. In addition, when a confirmation signal for document modification is received from the administrator terminal, the processor 120 may generate a chatbot's answer notifying the administrator's confirmation and transmit it to the user terminal 10. The processor 120 may determine a modified item and description content to be reflected in the document structure analysis result based on the manager confirmation signal. Accordingly, the processor 120 may provide the user with a function of easily correcting typographical errors and unwritten portions of the document through the chatbot interface without the need to re-upload the entire document. In addition, the processor 120 may generate a response from the chatbot notifying about at least one missing document based on the document supplement information. Specifically, the processor 120 may generate information on the missing document and a response from the chatbot for requesting the missing document based on the document supplement information, and transmit the generated information to the user terminal 10. In this way, the user can easily grasp the progress through the chatbot interface. In the case of insurance companies, since the chat record through the chatbot interface is retained in the server 100, it can be used as proof data in case of a dispute such as incomplete sales to a user in the future.

The processor 120 according to an embodiment of the present disclosure may generate an answer of a chatbot that asks a user a question about one or more documents of unclear types. Specifically, when the types of one or more documents analyzed through the document analysis model are not determined, the processor 120 may predict one or more types of documents based on the document analysis result. Accordingly, the processor 120 may generate a question about the type of one or more expected documents as an answer from the chatbot or as a choice. Responses and options of the generated chatbot may include one or more documents and are transmitted to the user terminal 10 and displayed through the chatbot interface. Accordingly, the processor 120 may determine the type of document based on the user's input signal and response. For example, if the processor 120 expects an insurance application for the first document and the second document, the chatbot responds to the question, "Is there any insurance application?" Or you can create "Is document 1 an insurance application?" Accordingly, the processor 120 may determine the type of an unclear document based on the input signal of the user terminal 10.

The processor 120 according to an embodiment of the present disclosure may transmit the analyzed document to the user terminal 10 through the document analysis model in order to request a user's confirmation signal. Specifically, the processor 120 may transmit a document displayed as one or more texts to the user terminal 10 through the OCR of the document analysis model. For example, the processor 120 may transmit the recognized document to the chatbot interface in the form of an html document. The user can review whether the document recognized in the chatbot interface is correctly recognized. In this case, the processor 120 may highlight a specific portion of a document including one or more texts. For example, in the case of an application for an insurance product that is not covered when blood pressure is high, a part related to blood pressure may be highlighted in the document. The document including the highlight mark may be transmitted to the user terminal 10 and displayed through a chatbot interface. Since these document recognition records can be held in the server 100 through the chatbot interface, the user can better look at important parts of the insurance contract, and can be viewed at any time through the chat record, so that the user's insurance document can be viewed. Understanding can be improved, and insurance companies can use it in case of insurance-related disputes such as incomplete sales.

According to an embodiment of the present disclosure, the processor 120 may receive a user's confirmation signal through a chatbot interface and transmit the insurance subscription examination result to the user terminal 10. Specifically, the processor 120 may receive from the user terminal 10 a confirmation signal for a modified matter based on the underwriting supplement information and the document supplement information. The processor 120 receiving the confirmation signal from the user terminal 10 may transmit an insurance subscription examination result based on the insurance document reflecting the revised information to the user terminal 10. In addition, the processor 120 may transmit the insurance subscription examination result to the external server 20, and may select another insurance as a recommended insurance based on the examination result. Specifically, the processor 120 may modify the user insurance subscription history based on the insurance subscription examination result. Even when the user's insurance subscription is rejected, it may be added to the insurance subscription history through the processor 120. Accordingly, the processor 120 may select another insurance as the recommended insurance based on the reason why the user's insurance subscription is rejected. The selected other insurance may be transmitted and displayed to the user terminal 10 through the chatbot interface. Accordingly, the document analysis server 100 may process a request and examination for insurance subscription of a user on holidays and 24 hours. In particular, the document analysis server 100 may provide a function for reviewing insurance subscriptions for a user who works at work, on weekends or holidays, and for correcting and reviewing within a short time.

The user can conveniently consult with insurance related to insurance through the chatbot interface, and can deliver insurance-related documents in a convenient manner like sharing files in a chat, and the delivered insurance documents can be recognized and processed automatically by the chatbot through OCR. So, insurance companies can save money. In addition, since recognition of insurance-related documents or additional documents can be requested through a convenient interface between the user and the chatbot in the form of a chat, the accuracy of recognition can be improved and the accuracy of insurance-related processing can be improved.

3 is a schematic diagram showing a network function that is a basis of a structure analysis model for determining the structure of a document according to an embodiment of the present disclosure.

Throughout this specification, a computational model, a neural network, a network function, and a neural network may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may generally be referred to as nodes. These nodes may also be referred to as neurons. The neural network is composed of at least one or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more links.

In a neural network, one or more nodes connected through a link may relatively form a relationship between an input node and an output node. The concepts of an input node and an output node are relative, and any node in an output node relationship with respect to one node may have an input node relationship in a relationship with another node, and vice versa. As described above, the relationship between the input node and the output node can be created around a link. One or more output nodes may be connected to one input node through a link, and vice versa.

In the relationship between the input node and the output node connected through one link, the value of the output node may be determined based on data input to the input node. Here, a node interconnecting the input node and the output node may have a weight. The weight may be variable and may be changed by a user or an algorithm in order for the neural network to perform a desired function. For example, when one or more input nodes are interconnected to one output node by each link, the output node includes values input to input nodes connected to the output node and weights set in the link corresponding to each input node. The output node value may be determined based on.

As described above, in a neural network, one or more nodes are interconnected through one or more links to form an input node and an output node relationship in the neural network. The characteristics of the neural network may be determined according to the number of nodes and links in the neural network, the association relationship between the nodes and the links, and a weight value assigned to each of the links. For example, when there are the same number of nodes and links, and two neural networks having different weight values between the links exist, the two neural networks may be recognized as being different from each other.

A neural network may be configured including one or more nodes. Some of the nodes constituting the neural network may configure one layer based on distances from the initial input node. For example, a set of nodes having a distance n from an initial input node may constitute n layers. The distance from the first input node may be defined by the minimum number of links that must be passed from the first input node to the corresponding node. However, the definition of such a layer is arbitrary for explanation, and the order of the layer in the neural network may be defined in a different manner than that described above. For example, the layer of nodes may be defined by the distance from the last output node.

The first input node may mean one or more nodes to which data is directly input without going through a link in a relationship with other nodes among nodes in the neural network. Alternatively, in a neural network network, in a relationship between nodes based on a link, it may mean nodes that do not have other input nodes connected by a link. Similarly, the final output node may mean one or more nodes that do not have an output node in a relationship with other nodes among nodes in the neural network. In addition, the hidden node may refer to nodes constituting a neural network other than the first input node and the last output node. In the neural network according to an embodiment of the present disclosure, the number of nodes in the input layer may be the same as the number of nodes in the output layer, and the number of nodes decreases and then increases again as the input layer proceeds to the hidden layer. I can. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes of an input layer is less than the number of nodes of an output layer, and the number of nodes decreases as the number of nodes progresses from the input layer to the hidden layer. have. In addition, the neural network according to another embodiment of the present disclosure may be a neural network in which the number of nodes of an input layer is greater than the number of nodes of an output layer, and the number of nodes increases as the number of nodes progresses from the input layer to the hidden layer. I can. A neural network according to another embodiment of the present disclosure may be a neural network in the form of a combination of the aforementioned neural networks.

A deep neural network (DNN) may mean a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can be used to identify potential structures in data. In other words, it is possible to grasp the potential structure of photos, texts, videos, voices, and music (e.g., what objects are in photos, what are the content and emotions of the text, what are the contents and emotions of the voice, etc.) . Deep neural networks include convolutional neural networks (CNNs), recurrent neural networks (RNNs), auto encoders, Generative Adversarial Networks (GANs), and restricted Boltzmann machines (RBMs). boltzmann machine), deep belief network (DBN), Q network, U network, and Siam network. The description of the above-described deep neural network is only an example, and the present disclosure is not limited thereto.

In an embodiment of the present disclosure, the network function may include an auto encoder. The auto encoder may be a kind of artificial neural network for outputting output data similar to input data. The auto encoder may include at least one hidden layer, and an odd number of hidden layers may be disposed between the input/output layers. The number of nodes of each layer may be reduced from the number of nodes of the input layer to an intermediate layer called a bottleneck layer (encoding), and then reduced and expanded symmetrically from the bottleneck layer to an output layer (symmetric with the input layer). In this case, in the example of FIG. 3, the dimensionality reduction layer and the dimensional restoration layer are illustrated as being symmetrical, but the present disclosure is not limited thereto, and nodes of the dimensionality reduction layer and the dimensional restoration layer may or may not be symmetrical. Auto encoders can perform nonlinear dimensionality reduction. The number of input layers and output layers may correspond to the number of sensors remaining after pre-processing of input data. In the auto-encoder structure, the number of nodes of the hidden layer included in the encoder may have a structure that decreases as it moves away from the input layer. If the number of nodes in the bottleneck layer (the layer with the fewest nodes located between the encoder and the decoder) is too small, a sufficient amount of information may not be delivered, so more than a certain number (e.g., more than half of the input layer, etc.) ) May be maintained.

The neural network may be learned in at least one of supervised learning, unsupervised learning, and semi supervised learning. Learning of neural networks is to minimize output errors. In learning of a neural network, iteratively inputs training data to the neural network, calculates the output of the neural network for the training data and the error of the target, and transfers the error of the neural network from the output layer of the neural network to the input layer in a direction to reduce the error. This is a process of updating the weight of each node of the neural network by backpropagation in the direction. In the case of teacher learning, learning data in which the correct answer is labeled in each learning data is used (ie, labeled learning data), and in the case of non-satellite learning, the correct answer may not be labeled in each learning data. That is, for example, in the case of teacher learning related to data classification, the learning data may be data in which a category is labeled with each learning data. Labeled training data is input to a neural network, and an error may be calculated by comparing an output (category) of the neural network with a label of the training data. As another example, in the case of comparative history learning regarding data classification, an error may be calculated by comparing training data as input with a neural network output. The calculated error is backpropagated in the neural network in the reverse direction (ie, from the output layer to the input layer), and the connection weight of each node of each layer of the neural network may be updated according to the backpropagation. The amount of change in the connection weight of each node to be updated may be determined according to a learning rate. The computation of the neural network for the input data and the backpropagation of the error can constitute a learning cycle (epoch). The learning rate may be applied differently according to the number of repetitions of the learning cycle of the neural network. For example, a high learning rate is used in the early stages of training of a neural network, so that the neural network quickly secures a certain level of performance to increase efficiency, and a low learning rate can be used in the later stages of training to increase accuracy.

In learning of a neural network, in general, training data may be a subset of actual data (that is, data to be processed using a trained neural network), and thus, errors in training data decrease but errors in actual data There may be increasing learning cycles. Overfitting is a phenomenon in which errors in actual data increase due to over-learning on learning data. For example, a neural network learning a cat by showing a yellow cat may not recognize that it is a cat when it sees a cat other than yellow, which may be a kind of overfitting. Overfitting can cause an increase in errors in machine learning algorithms. Various optimization methods can be used to prevent such overfitting. In order to prevent overfitting, methods such as increasing training data, regularization, and dropout in which some nodes of the network are omitted during the training process may be applied.

4 is a diagram illustrating a method of analyzing documents related to insurance subscription according to an embodiment of the present disclosure.

The document analysis server 100 may determine whether to start an insurance-related procedure with a user based on a chat message received from the user terminal 10.

According to an embodiment of the present disclosure, the document analysis server 100 may receive a user confirmation signal for insurance guide information and a document for insurance subscription from the user terminal 10 (201 ). Specifically, the document analysis server 100 may transmit guide information for insurance subscription to the user terminal through a chatbot interface provided to the user terminal 10 and receive a user confirmation signal for the insurance guide information. The document analysis server 100 may receive an insurance subscription document based on an upload input of a user's insurance subscription document through a chatbot interface provided to the user terminal 10. The insurance subscription document may include an image of a document related to insurance subscription.

In addition, the document analysis server 100 may obtain information on essential documents for insurance for which the user has applied for subscription from the external server 20 in order to identify the type of the insurance subscription document (203). Specifically, the document analysis server 100 may obtain information on the types of one or more essential documents required for insurance subscription from the external server 20 based on the type of insurance product for which the user has applied for subscription.

In addition, the document analysis server 100 may analyze the insurance subscription document imaged through the document analysis model (205). Specifically, the document analysis model may be used to identify one or more characters and signatures included in the insurance policy. In addition, the document analysis model may identify one or more characters included in the imaged insurance policy using optical character recognition (OCR) technology. In addition, the document analysis model can read the structure of the insurance policy by performing natural language processing on the identified one or more characters. The structure of the read insurance policy document may be generated as structure information of the insurance policy document through a document analysis model. The structure information of the insurance policy document includes information on one or more items included in the insurance policy document. It can include information about each location.

In addition, the document analysis server 100 may read the structure of the insurance subscription document imaged through the structure analysis submodel (207). Specifically, the structural analysis sub-model is learned based on the analysis result of the imaged insurance policy document. The structural analysis sub-model determines whether to select another required document by processing the comparison result of the structure information included in the selected required document and the structure information of the imaged insurance policy using the policy network, and determines whether to select another required document. It can be learned to calculate the reward based on the structure information and the structure information of the imaged insured document, and to select another required document that can maximize the reward. The document analysis server 100 may determine the insurance document received from the user terminal 10 based on the essential document finally selected by the structure analysis submodel.

In addition, the document analysis server 100 may generate information on the examination result for insurance subscription and document supplement information based on the result of analyzing the imaged insurance subscription document (209 ). Specifically, the document analysis server 100 may generate an examination result for insurance subscription based on at least one of the types or contents of the insurance subscription document. In addition, the document analysis server 100 may generate document supplementation information for insurance subscription based on the analysis result of the insurance subscription document. The document supplementary information may include information on an item that is omitted in the insurance policy document and information on whether an anomaly exists in the item included in the insurance policy document. In addition, the document analysis server 100 may generate underwriting supplementary information based on a recognition result for each item included in the insurance subscription document. In addition, the document analysis server 100 may generate conditional approval information based on the recognition result for each item included in the insurance subscription document. Accordingly, the document analysis server 100 may generate a review result based on the underwriting supplement information and conditional approval information.

In addition, the document analysis server 100 may transmit document supplementation information for requesting feedback from the user to the user terminal 10 (211).

In addition, the document analysis server 100 may receive feedback information based on the document supplement information from the user terminal 10 (213). Specifically, the feedback information includes information on an item to which a user's correction is performed on an item for which feedback is requested, and accordingly, an item to which the correction is applied. The feedback information may be received by the document analysis server 100 through the user terminal 10.

In addition, the document analysis server 100 may determine insurance subscription based on the feedback information received from the user terminal 10 and a review result of the insurance subscription document (215). Specifically, the document analysis server 100 recognizes at least one of the types or contents of the insurance subscription documents based on the analysis result of the insurance subscription documents, and makes insurance subscription based on at least one of the types or contents of the insurance subscription documents. You can decide.

In addition, the document analysis server 100 may transmit information on insurance subscribers and information on insurance subscription to the external server 20 based on the insurance subscription decision (217).

In addition, the document analysis server 100 may transmit information on insurance payments and notification information on insurance subscription to the user terminal 10 based on the insurance subscription (219). Specifically, the notification information on insurance subscription may include information on whether to subscribe to insurance and information on a review result.

According to an embodiment of the present disclosure, a user may conveniently input a chat message through a chatbot interface to initiate insurance-related procedures, and conduct insurance-related consultation through a chat with the document analysis server 100, According to a document request from the document analysis server 100, a document can be conveniently shared through a chatbot interface. In addition, the user can supplement the contents of insurance-related documents shared through the chatbot interface through a chat with the document analysis server 100, and finally can conveniently subscribe to insurance.

5 is a flowchart illustrating a method for analyzing documents related to insurance subscription according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, it may be determined whether to start an insurance-related procedure based on the user's chat record received through the chatbot interface of the user terminal (510).

According to an embodiment of the present disclosure, an insurance subscription document input through a chatbot interface may be received from a user terminal (520).

According to an exemplary embodiment of the present disclosure, an insurance subscription document may be analyzed using the document analysis model by inputting the insurance subscription document into the document analysis model (530 ).

According to an embodiment of the present disclosure, insurance subscription may be determined based on an analysis result of an insurance subscription document (540 ).

The order of the steps illustrated in FIG. 5 may be changed as necessary, and at least one or more steps may be omitted or added. That is, the above-described steps are only one embodiment of the present disclosure, and the scope of the present disclosure is not limited thereto.

6 illustrates logic for implementing a document analysis method related to insurance subscription according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, a computer program for providing a document analysis method related to insurance subscription may be implemented by the following logic.

The computer program receives a logic 610 for determining whether to initiate an insurance-related procedure based on the user's chat record received through the chatbot interface of the user terminal, and receives an insurance subscription document input through the chatbot interface from the user terminal. The logic 620 for inputting the insurance subscription document into the document analysis model and analyzing the insurance subscription document using the document analysis model 630 and determining the insurance subscription based on the analysis result of the insurance subscription document Logic 640 may be included.

Alternatively, the insurance subscription document may include an image of the document related to the insurance subscription.

Alternatively, the logic for receiving the insurance subscription document input through the chatbot interface from the user terminal may include a logic for transmitting insurance guide information to the user terminal through the chatbot interface provided to the user terminal; Logic for receiving a user confirmation signal for insurance guidance information of a user through a chatbot interface provided to a user terminal; And logic for receiving the insurance subscription document based on the upload input of the user's insurance subscription document through the chatbot interface provided to the user terminal.

Alternatively, the logic for analyzing the insured document using the document analysis model by inputting the insured document into the document analysis model is a logic that identifies one or more characters and signatures included in the insured document using the document analysis model. ; And logic for reading the structure of the insurance policy by performing natural language processing on one or more characters using the document analysis model.

Alternatively, the logic for reading the structure of the insured document by performing natural language processing on one or more characters using the document analysis model is one included in the insured document based on the natural language processing result for the insured document. Logic to identify the above items; And logic for reading the structure of the insurance policy document based on a result of context analysis for one or more items.

Alternatively, the logic for reading the structure of the insurance policy document based on the context analysis result of the one or more items may include logic for reading the structure of the insurance policy document by comparing the context analysis result with the existing insurance policy document structure; Or logic for reading the structure of the insurance subscription document by inputting the context analysis result to the structure analysis submodel; It may include at least one of the logic.

Alternatively, the structural analysis sub-model is learned based on the analysis result of the imaged insurance policy document, and the comparison result of the structure information included in the selected essential document and the structure information of the imaged insurance policy document is compared using a policy network. Process to determine whether to select other required documents, calculate rewards based on the structure information contained in the other required documents and the structure information of the imaged insured documents, and select other required documents that can maximize the rewards. Can be learned.

Alternatively, the logic for determining the insurance subscription based on the analysis result of the insurance subscription document may include: logic for recognizing at least one of the types or contents of the insurance subscription document based on the analysis result of the insurance subscription document; And logic for determining insurance subscription based on at least one of the type or content of the insurance subscription document.

Alternatively, the logic for determining insurance subscription based on at least one of the types or contents of the insurance subscription document includes logic for generating a screening result for insurance subscription based on at least one of the type or contents of the insurance subscription document. can do.

Alternatively, the logic for generating the examination result for insurance subscription based on at least one of the type or content of the insurance subscription document includes a logic for generating underwriting supplementary information based on the recognition result for each item included in the insurance subscription document. can do.

Alternatively, the logic for generating the examination result for insurance subscription based on at least one of the type or content of the insurance subscription document includes logic for generating conditional approval information based on the recognition result for each item included in the insurance subscription document. can do.

Alternatively, the logic for determining insurance subscription based on the analysis result of the insurance subscription document may include: logic for generating document supplementation information for insurance subscription based on the analysis result of the insurance subscription document; And logic for transmitting supplemental information to the user terminal.

Alternatively, the logic for generating document supplemental information for insurance subscription based on the analysis result of the insurance subscription document may include logic for determining whether an omission item exists in the insurance subscription document; And logic for determining whether an anomaly exists in the item included in the insurance subscription document.

According to another embodiment of the present disclosure, logic for implementing a computer program may be implemented by a model, circuit or means for implementing the computer program.

Those of skill in the art would further describe the various illustrative logical blocks, configurations, models, circuits, means, logics and algorithm steps described in connection with the embodiments disclosed herein, in electronic hardware, computer software, or combinations of both. It should be recognized that it can be implemented as To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, models, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented in hardware or as software depends on the specific application and design limitations imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

7 shows a simplified and general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

While the present disclosure has generally been described above with respect to computer-executable instructions that can be executed on one or more computers, those skilled in the art will appreciate that the present disclosure may be implemented in combination with other program models and/or as a combination of hardware and software. will be.

In general, program models include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, to those skilled in the art, the method of the present disclosure is not limited to single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, handheld computing devices, microprocessor-based or programmable household appliances, etc. (each of which is It will be appreciated that other computer system configurations may be implemented, including one that may operate in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in a distributed computing environment where certain tasks are performed by remote processing devices that are connected through a communication network. In a distributed computing environment, program models can be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Computer-readable media can be any computer-readable media, including volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. Includes removable media. By way of example and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media include volatile and nonvolatile media, temporary and non-transitory media, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions, data structures, program models or other data. Includes the medium. Computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device, Or any other medium that can be accessed by a computer and used to store the desired information.

Computer-readable transmission media typically implement computer-readable instructions, data structures, program models or other data on a modulated data signal such as a carrier wave or other transport mechanism. Includes all information delivery media. The term modulated data signal refers to a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example and not limitation, computer-readable transmission media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above-described media are also intended to be included within the scope of computer-readable transmission media.

An exemplary environment 1100 is shown that implements various aspects of the present disclosure, including a computer 1102, which includes a processing device 1104, a system memory 1106, and a system bus 1108. do. System bus 1108 couples system components, including but not limited to, system memory 1106 to processing device 1104. The processing unit 1104 may be any of a variety of commercially available processors. Dual processors and other multiprocessor architectures may also be used as processing unit 1104.

The system bus 1108 may be any of several types of bus structures that may be additionally interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read-only memory (ROM) 1110 and random access memory (RAM) 1112. The basic input/output system (BIOS) is stored in non-volatile memory 1110 such as ROM, EPROM, EEPROM, etc. This BIOS is a basic input/output system that helps transfer information between components within the computer 1102, such as during startup. Includes routines. RAM 1112 may also include high speed RAM such as static RAM for caching data.

The computer 1102 also has an internal hard disk drive (HDD) 1114 (e.g., EIDE, SATA), and the internal hard disk drive 2114 can also be configured for external use within a suitable chassis (not shown). ), a magnetic floppy disk drive (FDD) 1116 (e.g., for reading from or writing to a removable diskette 1118), and an optical disk drive 1120 (e.g., a CD-ROM disk (For reading 1122) or reading from or writing to other high-capacity optical media such as DVD). The hard disk drive 1114, the magnetic disk drive 1116, and the optical disk drive 1120 are each connected to the system bus 1108 by a hard disk drive interface 1124, a magnetic disk drive interface 1126, and an optical drive interface 1128. ) Can be connected. The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer readable media provide non-volatile storage of data, data structures, computer executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of the computer-readable medium above refers to a removable optical medium such as a HDD, a removable magnetic disk, and a CD or DVD, those skilled in the art may use a zip drive, a magnetic cassette, a flash memory card, a cartridge, etc. It will be appreciated that other types of computer-readable media, such as the like, may also be used in the exemplary operating environment and that any such media may contain computer-executable instructions for performing the methods of the present disclosure.

A number of program models may be stored in the drive and RAM 1112, including the operating system 1130, one or more application programs 1132, other program models 1134, and program data 1136. All or part of the operating system, application, model and/or data may also be cached in RAM 1112. It will be appreciated that the present disclosure may be implemented in several commercially available operating systems or combinations of operating systems.

A user may input commands and information into the computer 1102 through one or more wired/wireless input devices, for example, a pointing device such as a keyboard 1138 and a mouse 1140. Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. These and other input devices are often connected to the processing unit 1104 through the input device interface 1142, which is connected to the system bus 1108, but the parallel port, IEEE 1394 serial port, game port, USB port, IR interface, It can be connected by other interfaces such as etc.

A monitor 1144 or other type of display device is also connected to the system bus 1108 through an interface such as a video adapter 1146. In addition to the monitor 1144, the computer generally includes other peripheral output devices (not shown) such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148, via wired and/or wireless communication. The remote computer(s) 1148 may be a workstation, a computing device computer, a router, a personal computer, a portable computer, a microprocessor-based entertainment device, a peer device, or other common network node, and is generally connected to the computer 1102. Although it includes many or all of the components described for simplicity, only memory storage device 1150 is shown. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or to a larger network, eg, a wide area network (WAN) 1154. Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks such as intranets, all of which can be connected to worldwide computer networks, for example the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 via a wired and/or wireless communication network interface or adapter 1156. Adapter 1156 may facilitate wired or wireless communication to LAN 1152, which also includes a wireless access point installed therein to communicate with wireless adapter 1156. When used in a WAN networking environment, the computer 1102 may include a modem 1158, connected to a communication computing device on the WAN 1154, or through the Internet, to establish communication through the WAN 1154. Have other means. The modem 1158, which may be an internal or external and a wired or wireless device, is connected to the system bus 1108 through a serial port interface 1142. In a networked environment, program models described for computer 1102 or portions thereof may be stored in remote memory/storage device 1150. It will be appreciated that the network connections shown are exemplary and other means of establishing communication links between computers may be used.

Computer 1102 is associated with any wireless device or entity deployed and operated in wireless communication, e.g., a printer, scanner, desktop and/or portable computer, portable data assistant (PDA), communication satellite, wireless detectable tag. It operates to communicate with any device or place, and a phone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) allows you to connect to the Internet or the like without wires. Wi-Fi is a wireless technology such as a cell phone that allows such devices, for example computers, to transmit and receive data indoors and outdoors, that is, anywhere within the coverage area of a base station. Wi-Fi networks use a wireless technology called IEEE 802.11 (a, b, g, etc.) to provide a secure, reliable and high-speed wireless connection. Wi-Fi can be used to connect computers to each other, to the Internet, and to a wired network (using IEEE 802.3 or Ethernet). Wi-Fi networks can operate in unlicensed 2.4 and 5 GHz radio bands, for example at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual bands). have.

Those of ordinary skill in the art of this disclosure will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, commands, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields. Or particles, or any combination thereof.

A person of ordinary skill in the art of the present disclosure includes various illustrative logical blocks, models, processors, means, circuits and algorithm steps described in connection with the embodiments disclosed herein, electronic hardware, (convenience). For the sake of clarity, it will be appreciated that it may be implemented by various forms of program or design code or a combination of both (referred to herein as "software"). To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, models, circuits and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. A person of ordinary skill in the art of the present disclosure may implement the described functions in various ways for each specific application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques. The term “article of manufacture” includes a computer program, carrier, or media accessible from any computer-readable device. For example, computer-readable media include magnetic storage devices (e.g., hard disks, floppy disks, magnetic strips, etc.), optical disks (e.g., CD, DVD, etc.), smart cards, and flash memory. Devices (eg, EEPROM, card, stick, key drive, etc.). In addition, the various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is to be understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on the design priorities, it is to be understood that within the scope of this disclosure a specific order or hierarchy of steps in processes may be rearranged. The appended method claims provide elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

A description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be apparent to those of ordinary skill in the art, and general principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (15)

A computer program stored on a computer-readable storage medium, wherein the computer program, when executed by one or more processors, causes the following operations to be performed for analyzing a document for insurance subscription, the operations:
Determining whether to start insurance-related procedures based on the user's chat record received through the chatbot interface of the user terminal;
Receiving an insurance subscription document input through the chatbot interface from the user terminal;
Inputting the insurance subscription document into a document analysis model and analyzing the insurance subscription document using the document analysis model; And
Determining an insurance subscription based on the analysis result of the insurance subscription document;
Containing
A computer program stored on a computer readable storage medium.
The method of claim 1,
The above insurance subscription document,
Containing images of documents related to insurance coverage,
A computer program stored on a computer readable storage medium.
The method of claim 1,
The operation of receiving an insurance subscription document from the user terminal through the chatbot interface,
Transmitting insurance guide information to the user terminal through a chatbot interface provided to the user terminal;
Receiving a user confirmation signal for the insurance guide information of the user through a chatbot interface provided to the user terminal; And
Receiving the insurance subscription document based on an upload input for the insurance subscription document by a user through a chatbot interface provided to the user terminal;
Containing,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The operation of analyzing the insurance subscription document using the document analysis model by inputting the insurance subscription document into a document analysis model,
Identifying one or more characters and signatures included in the insurance policy using the document analysis model; And
Reading the structure of the insurance policy by performing natural language processing on the one or more characters using the document analysis model;
Containing,
A computer program stored on a computer-readable storage medium.
The method of claim 4,
The operation of reading the structure of the insurance subscription document by performing natural language processing on the one or more characters using the document analysis model,
Identifying one or more items included in the insurance policy document based on a result of natural language processing of the insurance policy document; And
Reading a structure of the insurance policy document based on a result of a context analysis of the one or more items;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 5,
Reading the structure of the insurance policy document based on the result of context analysis for the one or more items,
Comparing the context analysis result with an existing insurance policy document structure and reading the structure of the insurance policy document; or
Inputting the context analysis result to a structure analysis submodel to read the structure of the insurance subscription document; Including at least one operation of,
A computer program stored on a computer readable storage medium.
The method of claim 6,
The structural analysis sub-model,
It is learned based on the analysis result of the imaged insurance subscription document,
By processing the comparison result of the structure information included in the selected essential document and the structure information of the imaged insurance policy using the policy network, it is determined whether to select another required document, and the structure information included in the other required document It is learned to calculate a reward based on the structure information of the insured document, and to select another required document capable of maximizing the reward,
A computer program stored on a computer readable storage medium.
The method of claim 1,
The operation of determining insurance subscription based on the analysis result of the insurance subscription document,
Recognizing at least one of the types or contents of the insurance subscription document based on the analysis result of the insurance subscription document; And
Determining insurance subscription based on at least one of the types or contents of the insurance subscription document;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 8,
The operation of determining insurance subscription based on at least one of the types or contents of the insurance subscription document,
Generating an examination result for insurance subscription based on at least one of the types or contents of the insurance subscription document;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 9,
The operation of generating an examination result for insurance subscription based on at least one of the type or content of the insurance subscription document,
Generating underwriting supplemental information based on a recognition result for each item included in the insurance subscription document;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 9,
The operation of generating an examination result for insurance subscription based on at least one of the type or content of the insurance subscription document,
Generating conditional approval information based on a recognition result for each item included in the insurance subscription document;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 1,
The operation of determining insurance subscription based on the analysis result of the insurance subscription document,
Generating document supplemental information for insurance subscription based on the analysis result of the insurance subscription document; And
Transmitting the supplemental information to the user terminal;
Containing,
A computer program stored on a computer readable storage medium.
The method of claim 12,
The operation of generating document supplemental information for insurance subscription based on the analysis result of the insurance subscription document,
Determining whether there is an omission item in the insurance subscription document; And
Determining whether an anomaly exists in the item included in the insurance subscription document;
Containing,
A computer program stored on a computer readable storage medium.
A method for analyzing a document for insurance coverage performed on one or more processors of a computing device, comprising:
Determining whether to start an insurance-related procedure based on the user's chat record received through the chatbot interface of the user terminal;
Receiving an insurance subscription document input through the chatbot interface from the user terminal;
Inputting the insurance subscription document into a document analysis model and analyzing the insurance subscription document using the document analysis model; And
Determining insurance subscription based on the analysis result of the insurance subscription document;
Containing,
A method of customer service performed on one or more processors of a computing device.
As a server for a document analysis method related to insurance subscription,
A processor including one or more cores;
Memory; And
A network unit for transmitting and receiving data to and from a user terminal;
Including,
The processor,
Determine whether to start insurance-related procedures based on the user's chat record received through the chatbot interface of the user terminal,
Receiving an insurance subscription document input through the chatbot interface from the user terminal,
Analyzing the insurance subscription document using the document analysis model by inputting the insurance subscription document into a document analysis model, and,
Determining insurance subscription based on the analysis result of the insurance subscription document,
Server for document analysis method.

Images