KR20210146755A - Service model using big data - Google Patents

Abstract

The present invention provides a method of acquiring accurate raw data of a user by using a reward system. According to the specification, a method in which a terminal uses big data to provide a financial service comprises: a step of displaying a first questionnaire to acquire raw data for recommending a financial service suitable for a user; a step of transmitting a request message for updating point information of the user to a first server based on an input value of the first questionnaire; a step of classifying the input value of the first questionnaire into four parts based on an interpretation algorithm or a persona preset in the terminal; a step of clustering classified data based on the interpretation algorithm or the persona; a step of generating a behavior map of the user based on a cluster of the classified data based on the interpretation algorithm or the persona; and a step of transmitting the behavior map of the user to a second server. The first server is a server associated with a company providing a service to which the user subscribes, and the second server is a server associated with a company providing a financial work service which the user intends to use.

Description

Service model using big data {SERVICE MODEL USING BIG DATA}

The present specification relates to a device for providing a membership service to a user by using big data to the user and a method for controlling the same.

Big data is a technology that extracts value from data including even unstructured data sets that are not in the form of a large (tens of terabytes) structured or database that exceed the capabilities of existing database and management tools and analyzes the results. Big data technology makes it possible to provide, manage, and analyze information for each individual, and plans to use such big data to provide customized services to users are under discussion.

An object of the present specification is to propose a method of providing a service suitable for a user through a user behavior map generated using big data.

In addition, an object of the present specification is to propose a method of acquiring user's raw data with high accuracy by using a compensation system.

The technical problems to be achieved by the present specification are not limited to the technical problems mentioned above, and other technical problems not mentioned are clear to those of ordinary skill in the art to which this specification belongs from the detailed description of the invention below. can be understood clearly.

An aspect of the present specification provides a method for a terminal to provide a financial service using big data, the method comprising: displaying a first questionnaire to recommend a financial service suitable for a user, to obtain raw data; transmitting a request message for updating the user's point information to a first server based on the input value of the first questionnaire; and based on a persona interpretation algorithm preset in the terminal: classifying the input value of the first questionnaire into four parts; clustering the classified data; and generating a behavior map of the user based on a cluster of the classified data. transmitting, to a second server, the behavior map of the user; Including, wherein the first server may be a server related to a company providing a service to which the user subscribes, and the second server may be a server related to a company providing a financial business service that the user wants to use.

The method may further include: receiving, from the second server, financial service information suitable for the user, based on the user's behavior map, and displaying; may further include.

In addition, the four parts may be classified based on 1) personal user information, 2) personal objective information, 3) collective culture (inner) information, and 4) collective objective information.

In addition, receiving a second questionnaire from the second server, and displaying the second questionnaire; and displaying a financial service suitable for the user based on the input value of the second questionnaire. may further include, wherein the second questionnaire is 1) generated based on a financial service service related to the cluster and the second server, and 2) a question related to the user's personal information may not be included.

Another aspect of the present specification provides a terminal for providing a financial service using big data, comprising: a communication module; Memory; display unit; a processor, wherein the display unit displays a first questionnaire to recommend a financial service suitable for a user, to obtain raw data, and the communication module is configured to: One server sends a request message for updating the user's point information, and the processor based on a preset persona interpretation algorithm: classifies the input value of the first questionnaire into four parts, and clusters the classified data (clustering), generating the user's behavior map based on the cluster of the classified data, the communication module transmits the user's behavior map to a second server, the first server The server may be a server related to a company providing a service to which the user has subscribed, and the second server may be a server related to a company providing a financial business service that the user wants to use.

Another aspect of the present specification is, In a service model for providing a financial service using big data, a questionnaire is displayed to recommend a financial service suitable for a user, raw data is obtained, and an input value of the questionnaire received from the user is displayed. Classifying into four parts, clustering the classified data, generating the user's behavior map based on the cluster of the classified data, and generating the first a terminal for transmitting a request message for updating the user's point information to a server and transmitting the action map to a second server; a first server associated with a company providing the service to which the user has subscribed; and a second server that is related to a company that provides a financial business service that the user wants to use, and determines a financial service suitable for the user based on the behavior map. may include

In addition, the four parts may be classified based on 1) personal user information, 2) personal objective information, 3) collective culture (inner) information, and 4) collective objective information.

According to an embodiment of the present specification, a service suitable for a user may be provided through a user behavior map generated using big data.

In addition, by using the compensation system, it is possible to obtain the user's raw data with high accuracy.

The effects obtainable in the present specification are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those of ordinary skill in the art to which this specification belongs from the description below. .

1 illustrates a block diagram of a wireless communication system to which the methods proposed in the present specification can be applied.
2 is a block diagram of an AI device according to an embodiment of the present specification.
3 is an example of a DNN model to which this specification can be applied.
4 is an example of a service system to which this specification can be applied.
5 is an example of a service model to which this specification can be applied.
6 is an embodiment of a service model to which this specification can be applied.
7 is an example of a method for generating a behavior map to which the present specification can be applied.
8 is an embodiment to which the present specification can be applied.
9 is an embodiment to which the present specification can be applied.
10 is an example of a compensation system to which this specification can be applied.
11 is an embodiment to which the present specification can be applied.
BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as a part of the detailed description to help the understanding of the present specification, provide embodiments of the present specification, and together with the detailed description, explain the technical features of the present specification.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numbers regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "part" for components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have distinct meanings or roles by themselves. In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed in this specification is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present specification , should be understood to include equivalents or substitutes.

Terms including an ordinal number such as 1st, 2nd, etc. may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another.

When an element is referred to as being “connected” or “connected” to another element, it is understood that it may be directly connected or connected to the other element, but other elements may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The singular expression includes the plural expression unless the context clearly dictates otherwise.

In the present application, terms such as “comprises” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Hereinafter, 5G communication (5th generation mobile communication) required by an autonomous driving device and/or an AI processor requiring AI-processed information will be described.

1 illustrates a block diagram of a wireless communication system to which the methods proposed in the present specification can be applied.

Referring to FIG. 1 , a device (AI device) including an AI module may be defined as a first communication device ( 910 in FIG. 1 ), and a processor 911 may perform detailed AI operations.

A second communication device ( 920 in FIG. 1 ) may perform a 5G network including another device (AI server) that communicates with the AI device, and the processor 921 may perform detailed AI operations.

The 5G network may be represented as the first communication device, and the AI device may be represented as the second communication device.

For example, the first communication device or the second communication device may include a base station, a network node, a transmitting terminal, a receiving terminal, a wireless device, a wireless communication device, a vehicle, a vehicle equipped with an autonomous driving function, and a connected car. ), drone (Unmanned Aerial Vehicle, UAV), AI (Artificial Intelligence) module, robot, AR (Augmented Reality) device, VR (Virtual Reality) device, MR (Mixed Reality) device, hologram device, public safety device, MTC device , IoT devices, medical devices, fintech devices (or financial devices), security devices, climate/environment devices, devices related to 5G services, or other devices related to the 4th industrial revolution field.

For example, a terminal or user equipment (UE) is a mobile phone, a smart phone, a laptop computer, a digital broadcasting terminal, personal digital assistants (PDA), a portable multimedia player (PMP), a navigation system, a slate PC (slate PC), tablet PC (tablet PC), ultrabook (ultrabook), wearable device (e.g., watch-type terminal (smartwatch), glass-type terminal (smart glass), HMD (head mounted display)) and the like. For example, the HMD may be a display device worn on the head. For example, an HMD may be used to implement VR, AR or MR. For example, the drone may be a flying vehicle that does not have a human and flies by a wireless control signal. For example, the VR device may include a device that implements an object or a background of a virtual world. For example, the AR device may include a device implemented by connecting an object or background of the virtual world to an object or background of the real world. For example, the MR device may include a device that implements a virtual world object or background by fusion with a real world object or background. For example, the hologram device may include a device for realizing a 360-degree stereoscopic image by recording and reproducing stereoscopic information by utilizing an interference phenomenon of light generated by the meeting of two laser beams called holography. For example, the public safety device may include an image relay device or an image device that can be worn on a user's body. For example, the MTC device and the IoT device may be devices that do not require direct human intervention or manipulation. For example, the MTC device and the IoT device may include a smart meter, a bending machine, a thermometer, a smart light bulb, a door lock, or various sensors. For example, a medical device may be a device used for the purpose of diagnosing, treating, alleviating, treating, or preventing a disease. For example, a medical device may be a device used for the purpose of diagnosing, treating, alleviating or correcting an injury or disorder. For example, a medical device may be a device used for the purpose of examining, replacing, or modifying structure or function. For example, the medical device may be a device used for the purpose of controlling pregnancy. For example, the medical device may include a medical device, a surgical device, an (ex vivo) diagnostic device, a hearing aid, or a device for a procedure. For example, the security device may be a device installed to prevent a risk that may occur and to maintain safety. For example, the security device may be a camera, CCTV, recorder or black box. For example, the fintech device may be a device capable of providing financial services such as mobile payment.

1, the first communication device 910 and the second communication device 920 are a processor (processor, 911,921), a memory (memory, 914,924), one or more Tx / Rx RF module (radio frequency module, 915,925) , including Tx processors 912 and 922 , Rx processors 913 and 923 , and antennas 916 and 926 . Tx/Rx modules are also called transceivers. Each Tx/Rx module 915 transmits a signal via a respective antenna 926 . The processor implements the functions, processes, and/or methods salpinned above. The processor 921 may be associated with a memory 924 that stores program code and data. Memory may be referred to as a computer-readable medium. More specifically, in DL (communication from a first communication device to a second communication device), the transmit (TX) processor 912 implements various signal processing functions for the L1 layer (ie, the physical layer). The receive (RX) processor implements the various signal processing functions of L1 (ie the physical layer).

The UL (second communication device to first communication device communication) is handled in the first communication device 910 in a manner similar to that described with respect to the receiver function in the second communication device 920 . Each Tx/Rx module 925 receives a signal via a respective antenna 926 . Each Tx/Rx module provides an RF carrier and information to the RX processor 923 . The processor 921 may be associated with a memory 924 that stores program code and data. Memory may be referred to as a computer-readable medium.

According to an embodiment of the present specification, the first communication device may be a vehicle, and the second communication device may be a 5G network.

2 is a block diagram of an AI device according to an embodiment of the present specification.

The AI device 20 may include an electronic device including an AI module capable of performing AI processing, or a server including the AI module. The AI device 20 may include an AI processor 21 , a memory 25 and/or a communication unit 27 .

The AI device 20 is a computing device capable of learning a neural network, and may be implemented in various electronic devices such as a server, a desktop PC, a notebook PC, and a tablet PC.

The AI processor 21 may learn the neural network using a program stored in the memory 25 . In particular, the AI processor 21 may learn a neural network for recognizing vehicle-related data. Here, the neural network for recognizing vehicle-related data may be designed to simulate a human brain structure on a computer, and may include a plurality of network nodes having weights that simulate neurons of the human neural network. The plurality of network modes may transmit and receive data according to a connection relationship, respectively, so as to simulate a synaptic activity of a neuron in which a neuron sends and receives a signal through a synapse. Here, the neural network may include a deep learning model developed from a neural network model. In a deep learning model, a plurality of network nodes can exchange data according to a convolutional connection relationship while being located in different layers. Examples of neural network models include deep neural networks (DNN), convolutional deep neural networks (CNN), Recurrent Boltzmann Machine (RNN), Restricted Boltzmann Machine (RBM), deep trust It includes various deep learning techniques such as neural networks (DBN, deep belief networks) and deep Q-networks, and can be applied to fields such as computer vision, speech recognition, natural language processing, and voice/signal processing.

Meanwhile, the processor performing the above-described function may be a general-purpose processor (eg, CPU), but may be an AI-only processor (eg, GPU) for artificial intelligence learning.

The memory 25 may store various programs and data necessary for the operation of the AI device 20 . The memory 25 may be implemented as a non-volatile memory, a volatile memory, a flash-memory, a hard disk drive (HDD), or a solid state drive (SDD). The memory 25 is accessed by the AI processor 21 , and reading/writing/modification/deletion/update of data by the AI processor 21 may be performed. Also, the memory 25 may store a neural network model (eg, the deep learning model 26 ) generated through a learning algorithm for data classification/recognition according to an embodiment of the present specification.

Meanwhile, the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition. The data learning unit 22 may learn a criterion regarding which training data to use to determine data classification/recognition and how to classify and recognize data using the training data. The data learning unit 22 may learn the deep learning model by acquiring learning data to be used for learning and applying the acquired learning data to the deep learning model.

The data learning unit 22 may be manufactured in the form of at least one hardware chip and mounted on the AI device 20 . For example, the data learning unit 22 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or is manufactured as a part of a general-purpose processor (CPU) or graphics-only processor (GPU) to the AI device 20 . may be mounted. In addition, the data learning unit 22 may be implemented as a software module. When implemented as a software module (or a program module including instructions), the software module may be stored in a computer-readable non-transitory computer readable medium. In this case, the at least one software module may be provided by an operating system (OS) or may be provided by an application.

The data learning unit 22 may include a training data acquiring unit 23 and a model learning unit 24 .

The training data acquisition unit 23 may acquire training data required for a neural network model for classifying and recognizing data. For example, the training data acquisition unit 23 may acquire vehicle data and/or sample data to be input to the neural network model as training data.

The model learning unit 24 may use the acquired training data to learn the neural network model to have a criterion for determining how to classify predetermined data. In this case, the model learning unit 24 may train the neural network model through supervised learning using at least a portion of the learning data as a criterion for determination. Alternatively, the model learning unit 24 may learn the neural network model through unsupervised learning for discovering a judgment criterion by self-learning using learning data without guidance. Also, the model learning unit 24 may train the neural network model through reinforcement learning using feedback on whether the result of the situation determination according to the learning is correct. Also, the model learning unit 24 may train the neural network model by using a learning algorithm including an error back-propagation method or a gradient decent method.

When the neural network model is learned, the model learning unit 24 may store the learned neural network model in a memory. The model learning unit 24 may store the learned neural network model in the memory of the server connected to the AI device 20 through a wired or wireless network.

The data learning unit 22 further includes a training data preprocessing unit (not shown) and a training data selection unit (not shown) to improve the analysis result of the recognition model or to save resources or time required for generating the recognition model You may.

The learning data preprocessor may preprocess the acquired data so that the acquired data can be used for learning for situation determination. For example, the training data preprocessor may process the acquired data into a preset format so that the model learning unit 24 may use the acquired training data for image recognition learning.

In addition, the learning data selection unit may select data necessary for learning from among the learning data acquired by the learning data acquiring unit 23 or the training data preprocessed by the preprocessing unit. The selected training data may be provided to the model learning unit 24 . For example, the learning data selector may select, as the learning data, only data about an object included in the specific region by detecting a specific region among images acquired through a vehicle camera.

In addition, the data learning unit 22 may further include a model evaluation unit (not shown) in order to improve the analysis result of the neural network model.

The model evaluator may input evaluation data to the neural network model and, when an analysis result output from the evaluation data does not satisfy a predetermined criterion, cause the model learning unit 22 to learn again. In this case, the evaluation data may be predefined data for evaluating the recognition model. As an example, the model evaluation unit may evaluate as not satisfying a predetermined criterion when, among the analysis results of the learned recognition model for the evaluation data, the number or ratio of evaluation data for which the analysis result is not accurate exceeds a preset threshold. have.

The communication unit 27 may transmit the AI processing result by the AI processor 21 to an external electronic device.

On the other hand, although the AI device 20 shown in FIG. 2 has been described as functionally divided into the AI processor 21, the memory 25, the communication unit 27, etc., the above-mentioned components are integrated into one module and the AI module Note that it may also be called

Deep Neural Network (DNN) Model

3 is an example of a DNN model to which this specification can be applied.

A deep neural network (DNN) is an artificial neural network (ANN) composed of several hidden layers between an input layer and an output layer. Deep neural networks can model complex non-linear relationships like general artificial neural networks.

For example, in a deep neural network structure for an object identification model, each object may be expressed as a hierarchical configuration of image basic elements. In this case, the additional layers may aggregate the characteristics of the gradually gathered lower layers. This feature of deep neural networks enables modeling of complex data with fewer units (units, nodes) compared to similarly performed artificial neural networks.

As the number of hidden layers increases, the artificial neural network is called 'deep', and the machine learning paradigm that uses the sufficiently deep artificial neural network as a learning model is called deep learning. In addition, a sufficiently deep artificial neural network used for such deep learning is collectively referred to as a deep neural network (DNN).

In the specification of this specification, the artificial neural network used for this deep learning method is collectively referred to as DNN, but if meaningful data can be output in a similar way, other deep learning methods or machine learning methods can be applied. Of course.

service system

4 is an example of a service system to which this specification can be applied.

Referring to FIG. 4 , in the service system, at least one of the terminal 400 , the database 420 , and the server 430 may be connected to the cloud network 410 . Alternatively, the cloud network 410 may include a database 420 and a server 430 . For the operation of the terminal 400 , the database 420 , the server 430 , and the cloud network 410 , the AI device 20 may be used. Accordingly, the terminal 400 , the database 420 , the server 430 , and the cloud network 410 may include the AI processor 21 .

Server 430 may include a 3 ^rd Party server associated with the service to provide the service provider 520 and the service provider 520 will be described later. For example, the service provider 520, if you want to provide services related to the point of the user signs up areas of the terminal 400, the service provider 520 is a 3 ^rd Party server to which a user has subscribed business operation Through this, it is possible to receive the user's point information. In addition, the service provider 520 may transmit the updated information to the point 3 ^rd Party server.

The database 420 may manage the persona data collected from the terminal 400 . The persona data collected in this way may be clustered and managed.

service model

5 is an example of a service model to which this specification can be applied.

Referring to FIG. 5 , the service model may include a persona data collection unit 500 , an artificial intelligence model 510 , and a service provider 520 . Such a service model may be installed in the form of an application and implemented through the terminal 400 , or may be implemented through a network including a server 430 connected to the user's terminal 400 and the like.

The persona data collection unit 500 may collect raw data related to the user's persona through the terminal 400 used by the user. In particular, in the present specification, persona data may include information about a user's inner personality, taste, and preferred social customs or norms that the user does not directly recognize together with raw data related to general user information. Also, the persona data collection unit 500 may classify the obtained raw data by clustering. Clustering refers to grouping raw data among similar entities, and clusters can be created to provide this to the artificial intelligence model 510 . For this, AI technology can be used, and they can be managed as big-data.

The artificial intelligence model 510 may use clusters of the user's persona data obtained through the persona data collection unit 500 to generate a user's profile that can be used to make a service recommendation in the service provider 520 . have. To this end, the artificial intelligence model 510 may include a persona data interpretation algorithm and a financial business algorithm. In order for the artificial intelligence model 510 to generate a user profile to provide a suitable financial service to the user, the input value of the financial business algorithm is not only user information through direct questionnaire, but also persona obtained through psychologically authorized questionnaires. Using data as an input value, it can be obtained secondarily through a persona data interpretation algorithm.

The service provider 520 may recommend a service suitable for the user by using the generated user profile. The AI techniques may be used for this purpose, terminal 400, etc. is over the network connection with the other service providers (e. G., 3 ^rd Party server), and may request a recommendation service, the service provider 520 is the terminal A service suitable for the user may be recommended or advertised through 400 or the like.

Through this, the service provider 520 can provide a service differentiated from the provided service through general user information, and the persona data, unlike general user information, is related to the user's disposition, so the possibility of maintenance As this is high, it can be used to continuously provide adequate services.

6 is an embodiment of a service model to which this specification can be applied.

FIG. 6 shows the service model shown in FIG. 5 in more detail from a code point of view.

Referring to FIG. 6 , persona data may be primarily classified into four parts, and persona data included in each part may be clustered among similar entities. For example, the four parts may include 1) personal user information, 2) personal objective information, 3) collective cultural (internal) information, and 4) collective objective information.

1) Personal user information is user information that can be obtained secondarily through a psychologically authorized questionnaire or obtained by a user directly entering a questionnaire, and may include, for example, personality, self-concept, etc. .

2) The personal objective information is user information that can be obtained by directly inputting the user through a questionnaire, and may include, for example, body information, skill, user preference, and the like.

3) Collective culture (inner) information is information about the user's view of the group and the user's view of the group. It can be obtained by input, and can also be inferred through the user's activity through SNS. For example, the terminal may acquire persona data related to relationships with other people and reputation through relationship information, contact frequency, and contact details of others that the user mainly contacts. Collective cultural (inner) information may include, for example, a user's reputation, relationships with people, worldview, and the like.

4) The collective objective information is user information that can be quantified on the occupation group to which it belongs, and may include, for example, average annual salary information of the user's occupation group, average length of service, and the like. It entered into or acquired by the user directly and can be obtained through the 3 ^rd Party servers associated with banks, schools, and workplaces.

The classification of the parts is only an example, and each part may be defined differently for a similar purpose. Persona data primarily classified for each part may be clustered, and each cluster may be used as an input value of a persona interpretation algorithm.

In addition, these persona data may be further classified into 4 parts defined for different purposes. For example, in addition to the first behavior map classified according to the 4 parts according to the above-described example, a second behavior map classified according to the 4 parts defined differently may also be generated. Through this, various behavior maps can be generated using the same persona data, and the persona interpretation algorithm can generate user profiles from more diverse viewpoints.

The generated user profile may include, for example, a user's emotional/physical development level, a group to which the user belongs, a relationship with others, a social position, financial information, and job information. For this, a persona interpretation algorithm may be used, and an AI technique may be used.

The financial business algorithm may generate information on financial products necessary for the user by using the user profile generated through the persona interpretation algorithm as an input value. The information on the financial product may include financial products that the user needs or can be recommended to the user, information on products that the user may be interested in, and the like. The service provider 520 may provide a service related thereto to the user by using the information on the financial product.

7 is an example of a method for generating a behavior map to which the present specification can be applied.

Referring to FIG. 7 , the terminal displays a questionnaire to the user ( S700 ). Such a questionnaire may be received from a server that provides a service that the user wants to use, or may be provided through an application installed in the terminal that provides a service that the user wants to use. For example, the terminal may provide an appropriate reward to the user for more active participation in the survey. More specifically, the terminal can receive the point information the user has from 3 ^rd party server that provides a user has subscribed to the service, if a user participating in the survey, it is possible to provide an additional point.

The terminal acquires persona data through an input value for a questionnaire received from the user (S710). Also, the terminal may request additional information related to the questionnaire. For example, if a user requests a person associated with the survey responses, the terminal may request the person's responses to a 3 ^rd party servers. For example, 3 ^rd party server may include a server that provides the user using SNS. Further, if the user to confirm the result of the poll, request additional information to the MS, the MS may request the additional information obtained as a 3 ^rd party server. When the terminal performs an operation of requesting additional information related to the questionnaire, the terminal may display a guide message indicating that the additional usage fee or charge corresponds to an additional fee, and this fee may be paid through the aforementioned points.

The terminal generates a behavior map based on the persona data (S720). For this, the terminal may use a persona interpretation algorithm. The generated behavior map may be plural, and each behavior map may be combined with each other to be used in a financial business algorithm.

8 is an embodiment to which the present specification can be applied.

Referring to FIG. 8 , the terminal may display an additional questionnaire using the cluster generated in FIG. 7 in order to recommend a financial service service to the user. Such a questionnaire may be received from a server that provides a financial business service that the user wants to use, or may be provided through an application installed in the terminal that provides a financial business service that the user wants to use.

In addition, the terminal may generate and display an additional integrated questionnaire based on a cluster stored in the terminal or obtained through a server in which a user profile is stored. For example, the integrated questionnaire may be composed of questionnaires that infringe on the user's privacy less than the questionnaire in FIG. 7 .

The terminal may acquire additional persona data through an input value for the integrated questionnaire input from the user. The terminal may generate a reinforced user profile using additional persona data through a persona interpretation algorithm. This persona interpretation algorithm may be the same as or different from the persona interpretation algorithm used in FIG. 7 . Alternatively, it may be a separate persona interpretation algorithm provided by a server/application providing a financial business service that the user wants to use.

The terminal may recommend a financial business service to the user through the financial business algorithm by using the generated result value as an input value through the persona interpretation algorithm. The financial business algorithm may also be the same as or different from the financial business algorithm used in FIG. 7 . Alternatively, it may be a separate financial business algorithm provided by a server/application providing a financial business service that the user wants to use.

Through this, the terminal can create an extended user profile suitable for each financial business service that the user wants to use, and the financial business service provider can create an extended user profile suitable for the financial service to be provided based on the initially created user profile. Extended user profiles are available.

The terminal may use the above-described reward system for more active participation in the survey by the user.

9 is an embodiment to which the present specification can be applied.

Referring to FIG. 9 , the first server includes a server related to a company that provides a service to which a user is subscribed (eg, an automobile dealer, an online shopping company, an airline company, a mileage service related to a gas company), and the second server The server includes a server related to a company that provides a financial business service (eg, a financial company, a card company) that the user wants to use.

The terminal displays the first questionnaire (S610). The terminal may display a first questionnaire for obtaining the first persona data to the user. The first questionnaire may be a questionnaire for obtaining raw data related to the user's persona, and may include a sensitive question related to the user's privacy.

The terminal updates the user's point information through the first server (S620). The terminal may be configured to provide points as a reward for the user's active participation in the first survey. When the user inputs an answer to the first gift, the terminal may provide the user with points as a reward. To this end, the terminal may update the user point through the first server.

The terminal generates first persona data based on the input value for the first questionnaire (S630). The first persona data may include the above-described behavior map. In more detail, the terminal may generate a behavior map of the user through a persona interpretation algorithm by using an input value for the first questionnaire. Since the input value for the first questionnaire may include information related to the user's privacy, it may be encrypted and stored in the terminal. Alternatively, it may be stored on a network server accessible only by the user.

The terminal transmits the first persona data to the second server (S640). Alternatively, the terminal may directly determine a financial business product to be recommended to the user by using the first persona data through a financial business algorithm. In this case, instead of transmitting the first persona data to the second server, the terminal may transmit a message requesting information on the determined financial business product to the second server.

The second server transmits the first product information to the terminal (S641). The second server may use the first persona data to determine a financial business product to be recommended to the user through its own financial business algorithm. In this case, the first product information may be related to a financial business product that is recommended to the user determined by the second server. Alternatively, the first product information may be information on a financial business product determined by the terminal.

The terminal displays the first product information (S650).

The terminal displays the second questionnaire (S660). Unlike the first questionnaire, the second questionnaire may be provided by a company that provides a specific financial business service. The second questionnaire includes the above-described integrated questionnaire. Accordingly, unlike the first questionnaire, since it may be provided through an external company, a question related to the user's privacy may not be included. Also, unlike the first questionnaire, it may be a questionnaire optimized for a specific financial business service that may be provided by a company that provides a specific financial business service. Operation S660 may be performed without performing operations S640, S641, and S650.

The terminal generates second persona data based on the first persona data and the input value for the second questionnaire (S661). The terminal may display a second questionnaire related to the first persona data, and generate the second persona data through a persona interpretation algorithm by using an input value for the second questionnaire.

The terminal transmits the second persona data to the second server (S670). The second server may use the second persona data to determine a financial business product to be recommended to the user through its own financial business algorithm.

The second server transmits second product information to the terminal (S671). For example, the second product information may be related to a financial business product that is recommended to the determined user by using the second persona data in the second server.

The terminal displays the second product information (S680).

The terminal transmits a user point payment request message to the first server (S690). For example, when the user requests to use the managed user point through the first server as a payment means for the first product or the second product, the terminal may transmit a user point payment request message to the first server .

10 is an example of a compensation system to which this specification can be applied.

The reward system of FIG. 10 may be applied for active participation of the user in the first and second questionnaires described above.

Referring to FIG. 10 , compensation may be performed when the user substitutes the information providing step from the information providing step to the information viewing step or when the user substitutes the information providing step from the information reading step when writing a questionnaire.

The information provision step may include steps of 1) basic survey response, 2) in-depth survey response, and 3) marketing mission response.

The information viewing step may include a step of reading information about 1) facts about myself (user), 2) group facts, and 3) objective facts.

For example, the first survey or the second survey may include 1) a basic survey, 2) an in-depth survey, and 3) a survey related to a marketing mission. The terminal may sequentially display the questionnaire of each stage.

In more detail, when the user responds to the basic questionnaire, the terminal may compensate the user point through the first server. The terminal may acquire facts about the user through the input value of the basic questionnaire.

When the user responds to the in-depth questionnaire, the terminal may additionally reward the user with user points. The terminal may acquire facts about the group to which the user belongs through the input value of the in-depth questionnaire. Additionally, the terminal may request the user for permission to read additional information related to the group to which the user belongs (eg, SNS information, etc.) in order to obtain the facts about the group to which the user belongs, In this case, user points may be compensated.

When the user responds to the questionnaire related to the marketing mission, the terminal may additionally reward the user with user points.

In order to analyze the input value of the questionnaire, the above-described persona interpretation algorithm may be used.

The above-described persona interpretation algorithm may generate a user profile based on 1) a fact about me (user), 2) a group fact, and 3) an objective fact. By combining 1) facts about myself (user), 2) group facts, and 3) objective facts, the terminal can provide a more suitable financial service service to the user. It can induce more active participation in the survey and obtain useful information.

11 is an embodiment to which the present specification can be applied. 11 illustrates the operation of FIG. 9 in more detail from the viewpoint of the terminal.

1. The terminal displays a questionnaire.

2. The terminal transmits a request message for updating user point information to the first server based on the input value of the questionnaire.

3. The terminal classifies the input value of the questionnaire into four parts using the set persona interpretation algorithm. For example, the four parts may be classified based on 1) personal user information, 2) personal objective information, 3) collective culture (inner) information, and 4) collective objective information.

4. The terminal clusters the classified data using the set persona interpretation algorithm.

5. The terminal generates a behavior map of the user based on each cluster, and transmits the behavior map of the user to the second server.

6. The terminal receives the financial business suitable for the user determined by the second server through the financial business algorithm, and displays it.

The above-described specification can be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all types of recording devices in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is also a carrier wave (eg, transmission over the Internet) that is implemented in the form of. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of this specification should be determined by a reasonable interpretation of the appended claims, and all modifications within the scope of equivalents of this specification are included in the scope of this specification.

In addition, although the above description has been focused on services and embodiments, these are merely examples and do not limit the present specification, and those of ordinary skill in the art to which this specification pertains within a range that does not deviate from the essential characteristics of the present service and embodiments. It can be seen that various modifications and applications not exemplified above are possible. For example, each component specifically shown in the embodiments may be implemented by modification. And differences related to such modifications and applications should be construed as being included in the scope of the present specification defined in the appended claims.

Although this specification has been mainly described as an example applied to a terminal (UE) based on a 5G (5 generation) system, it is possible to apply it to various wireless communication systems and autonomous driving devices.

Claims (10)

In a method for a terminal to provide a financial service using big data,
displaying a first questionnaire to obtain raw data for recommending a financial service suitable for a user;
transmitting a request message for updating the user's point information to a first server based on the input value of the first questionnaire; and
Based on the persona interpretation algorithm preset in the terminal:
classifying the input value of the first questionnaire into four parts;
clustering the classified data; and
generating a behavior map of the user based on a cluster of the classified data;
transmitting, to a second server, the behavior map of the user;
includes,
The method of claim 1, wherein the first server is a server related to a company providing a service to which the user has subscribed, and the second server is a server related to a company providing a financial business service that the user wants to use.
According to claim 1,
receiving, from the second server, financial service information suitable for the user based on the user's behavior map, and displaying the same;
A method further comprising:
3. The method of claim 2,
The above four parts are classified based on 1) personal user information, 2) personal objective information, 3) collective cultural information, and 4) collective objective information.
According to claim 1,
receiving a second questionnaire from the second server and displaying the second questionnaire; and
displaying a financial service suitable for the user based on the input value of the second questionnaire;
further comprising,
The second questionnaire is 1) generated based on a financial business service related to the cluster and the second server, and 2) a question related to the user's personal information is not included.
A terminal for providing financial services using big data, comprising:
communication module;
Memory;
display unit;
including a processor;
The display unit displays a first questionnaire to recommend a financial service suitable for the user, to obtain raw data,
The communication module transmits a request message for updating the user's point information to the first server based on the input value of the first questionnaire,
The processor is based on a preset persona interpretation algorithm:
classifying the input value of the first questionnaire into four parts, clustering the classified data, and generating a behavior map of the user based on the cluster of the classified data,
The communication module transmits the user's behavior map to the second server,
The first server is a server related to a company providing a service to which the user has subscribed, and the second server is a server related to a company providing a financial business service that the user wants to use, and the second server is a server related to a company providing a financial business service that the user wants to use.
6. The method of claim 5,
The communication module receives, from the second server, financial service information suitable for the user, based on the user's behavior map,
The display unit displays the financial service information, the terminal.
7. The method of claim 6,
The four parts are classified based on 1) personal user information, 2) personal objective information, 3) collective cultural information, and 4) collective objective information, a terminal.
6. The method of claim 5,
The communication module receives a second questionnaire from the second server,
The display unit displays the second questionnaire, and displays a financial service suitable for the user based on an input value of the second questionnaire;
The second questionnaire is 1) generated based on a financial service service related to the cluster and the second server, and 2) a question related to the user's personal information is not included, the terminal.
In the service model for providing financial services using big data,
To recommend a financial service suitable for a user, to obtain raw data, a questionnaire is displayed, and an input value of the questionnaire received from the user is classified into four parts, and the classified data is clustered. And, based on the cluster of classified data, the user's behavior map is generated, and based on the input value of the questionnaire, a request message for updating the user's point information is transmitted to the first server. and a terminal transmitting the behavior map to a second server;
a first server associated with a company providing the service to which the user has subscribed; and
a second server, which is related to a company that provides a financial service service that the user wants to use, and determines a financial service suitable for the user based on the behavior map;
Including, service model.
According to claim 1,
The four parts are classified based on 1) personal user information, 2) personal objective information, 3) collective cultural information, and 4) collective objective information, a service model.

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