KR102464836B1 - Service Model for Small Business Using Big Data - Google Patents

Abstract

According to the present specification, in a method for a management server to provide a financial service for a small business using big data, through a first terminal, the point information of the first terminal related to the financial business that the small business wants to use is registered. performing the procedure; setting a distribution order value related to the point of the first terminal based on the policy of the financial business; Receiving a first point information update request message requesting update of point information managed by the management server from a third terminal based on the receipt of a purchase request message using the points of the first terminal from the first terminal step; and counting the point distribution order value of the first terminal based on the first point information update request message, and updating the point information.

Description

Service Model for Small Business Using Big Data

The present specification relates to a service providing method for small businesses using big data.

Big data is a technology that extracts values 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 provide customized services to users using such big data 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.

In addition, an object of the present specification is to propose a method of providing customized financial products to small business owners using big data.

The technical problems to be achieved by this 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. will be able to understand

An aspect of the present specification, in a method for a management server to provide a financial service for a small business using big data, through a first terminal, the point information of the first terminal related to the financial business that the small business wants to use performing a registration procedure; setting a distribution order value related to the point of the first terminal based on the policy of the financial business; Receiving a first point information update request message requesting update of point information managed by the management server from a third terminal based on the receipt of a purchase request message using the points of the first terminal from the first terminal step; and counting the point distribution order value of the first terminal based on the first point information update request message, and updating the point information.

In addition, based on the policy of the financial business, setting a timer value for expiring the points of the first terminal; may further include.

In addition, the point information may include 1) a quantity value, 2) a timer, and 3) a residual distribution order value of points currently held by the user indicated by the index indicating the point information.

In addition, when the residual distribution order value of the point information of the first terminal becomes 0, generating a key for converting the points of the first terminal into cash; and transmitting the key to a terminal having a point of the first terminal based on the point information. may further include.

In addition, based on the receipt of the purchase request message using the points of the second terminal, receiving, from the first terminal, a second point information update request message requesting to update the point information managed by the management server; and counting the point distribution order value of the second terminal based on the second point information update request message, and updating the point information; may further include.

Another aspect of the present specification provides a management server for providing financial services for small business owners using big data, comprising: a transceiver for transmitting and receiving a signal; Memory; and a processor for controlling the transceiver and the memory, wherein the processor performs a procedure of registering point information of the first terminal related to the financial business that the small business wants to use in the memory through the first terminal, and , Based on the policy of the financial business, a distribution order value related to the point of the first terminal is set, and a purchase request message using the point of the first terminal is received from the first terminal through the transceiver Receives a first point information update request message requesting update of point information managed by the management server from a third terminal, and based on the first point information update request message, the first terminal It is possible to count the point distribution order value of , and update the point 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.

In addition, it is an object of the present specification to provide customized financial products to small business owners using big data.

The effects obtainable in the present specification are not limited to the above-mentioned effects, 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.
12 is an example of a management server to which this specification can be applied.
13 is an example of a point loan model to which this specification can be applied.
14 is an example of a point circulation model to which this specification can be applied.
15 is an example of a security guarantee method to which the present specification can be applied.
16 is an embodiment to which the present specification can be applied.
17 is an embodiment of a management server to which this 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 the present 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 ordinal numbers such as first, second, 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 a component is referred to as being “connected” or “connected” to another component, it may be directly connected or connected to the other component, but it is understood that other components 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 is to be understood that this does not preclude the possibility of the presence 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) communicating 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 that is 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 that implements 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.

Referring to FIG. 1 , a first communication device 910 and a second communication device 920 include a processor 911,921, a memory 914,924, and one or more Tx/Rx RF modules (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 . A 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 through 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 may 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 may be 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 training 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 trained, 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) in order 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 pre-processed 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 only data about an object included in the specific region as the learning data 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, may 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 value. 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 functionally divided into the AI processor 21, the memory 25, the communication unit 27, and the like, the above-described 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 features 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 .

The server 430 may include a service provider 520 , which will be described later, and a ^3rd party server associated with a service provided by the service provider 520 . For example, when the service provider 520 intends to provide a service related to a point of a company to which the user of the terminal 400 is subscribed, the service provider 520 uses a ^3rd party server operated by the company to which the user is subscribed. Through this, it is possible to receive the user's point information. Also, the service provider 520 may transmit the updated point information to a ^3rd 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 . This 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 the 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 uses 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 for providing 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 may be obtained secondary through a persona data interpretation algorithm.

The service provider 520 may recommend a service suitable for the user by using the generated user profile. For this purpose, AI technology may be used, and the terminal 400 may be connected to other service providers (eg, ^3rd party servers) through a network to request a recommended service, and the service provider 520 may be a terminal A service suitable for a 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 illustrates the service model shown in FIG. 5 in more detail in terms of code.

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 (inner) 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 and reputations with other people through relationship information, contact frequency, contact details, etc. 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 about 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. This may be obtained by direct input by the user, or may be obtained through a ^3rd party server related to a bank, school, or workplace.

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 can 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 he 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. In more detail, the terminal may receive point information held by the user from a ^3rd party server that provides a service to which the user has subscribed, and may provide additional points when the user participates in a survey.

The terminal acquires persona data through an input value for a questionnaire received from a user (S710). Also, the terminal may request additional information related to the questionnaire. For example, when a user requests an answer from a specific person related to a questionnaire, the terminal may request a specific person's answer from a ^3rd party server. For example, the ^3rd party server may include a server that provides SNS used by the user. In addition, when the user requests additional information from the terminal to check the result of the questionnaire, the terminal may request the ^3rd party server to obtain the additional information. 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 the user, 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 mounted on a 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 received 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 that the user wants to provide 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 service 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 set 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 providing a specific financial business service. The second questionnaire includes the aforementioned integrated questionnaire. Therefore, 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 input values for the first persona data and 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 above-described first and second questionnaires.

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 from the user the right 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 a questionnaire related to the marketing mission, the terminal may additionally reward the user with user points.

In order to analyze the input value of such a 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 me (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. Referring to FIG. 11 , the operation of FIG. 9 is exemplified in more detail from the viewpoint of the terminal.

1. The terminal displays a questionnaire.

2. The terminal sends the user point information to the first server based on the input value of the questionnaire.

Send a request message to update.

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 the user's behavior map based on each cluster, and transmits the user's behavior map 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.

12 is an example of a management server to which this specification can be applied.

Referring to FIG. 12 , the management server may include the aforementioned second server (eg, a server related to a company providing financial business services).

9 to 11 , the generated user profile may include the user's core desires and Pain Points. The user profile may be provided to the management server through the management server providing application installed in the user's terminal. Also, through the received user profile, the management server may infer that the user "needs a loan, but it is difficult to obtain a loan through the first financial institution".

The management server may transmit a public relation (PR) message suitable for the user by using the inferred result. To this end, the management server providing application installed in the user's terminal should be set to allow the reception of such a PR message.

point loan model

13 is an example of a point loan model to which this specification can be applied.

The point in this specification can be implemented through blockchain technology, and a small business can be defined as a business with less than 10 full-time workers among small businesses.

Referring to FIG. 13 , the point loan model may include a small business terminal, a consumer terminal, a provider terminal, a management server, and a financial institution server.

Through the PR message of FIG. 12 , a small business owner (ie, a user) may select a financial product of the management server. In more detail, small business owners may use products that lend points instead of cash.

1. A small business owner can apply for a point loan through the terminal and to the management server. For example, the small business owner may touch the loan service icon 1200 in the management server providing application installed in the terminal, and apply for a point loan to the management server.

2. The management server examines the user profile of the small business owner and decides whether to issue points based on the asset status of the small business owner.

3. According to the number of points issued to the small business owner, the management server requests the deposit of the deposit to the financial institution server where the small business assets are stored.

4. Small business owners can sell products to consumers at a pre-discounted price. In addition, the small business owner can purchase the product by using the points from the supplier who supplies the product to the small business owner.

5. A consumer can purchase a product from a small business using points through a consumer terminal. In addition, the small business may receive goods/services corresponding to the value of points paid from the supplier.

6. The small business owner can request the management server to update the points received or paid through the terminal.

7. The provider may request to refund the points received to the management server in cash through the terminal.

8. When the initially issued points are redeemed, the management server may request the financial institution server to return a deposit corresponding to the value of the redeemed points.

point cycle model

14 is an example of a point circulation model to which this specification can be applied.

Referring to FIG. 14 , the management server issues points ( S1410 ). The management server can set and execute a timer for expiring points. In addition, the management server may limit points at which points can be used. For example, the management server may set a timer for expiring the points to 3 months, and limit the points at which the points can be used to a specific small space.

When the points are distributed n times, the management server generates a key for cash conversion (S1420). To this end, the management server must count the number of points distributed. The value of n may be set by the management server when issuing points for the first time.

The management server may increase the value when the VIP holder converts points into cash (S1430). For example, the VIP holder may be set by the amount of points issued through the management server, the number of times of use, and the like.

The management server generally devalues the value when the points are converted into cash (S1440).

Through this, the management server can induce continuous consumption in the market for small businesses, and can be converted into cash only after the nth circulation, thereby maximizing the multiplier effect of point payment.

Collateral Guarantee Method

15 is an example of a security guarantee method to which the present specification can be applied.

Referring to FIG. 15 , the management server may provide a loan service to a small business owner (ie, a user) using the user profile generated through the embodiments of FIGS. 9 to 11 .

To this end, the management server may create a fund by using the above-described points. For example, the management server may create a fund by integrating dormant points. In more detail, the management server may set the point as a dormant point when the circulation order of the point is not counted for a certain period of time.

In addition, the management server may set the created fund to be used only for small business owners.

0. The management server receives the user profile of the small business from the small business terminal. For example, the user profile may include persona data/transaction performance information of a small business owner.

1. A small business owner can apply for a cash loan to a financial institution server through the terminal. For example, the small business owner may apply for a cash loan to a financial institution server by touching the loan service icon 1200 in the management server providing application installed in the terminal.

2. The financial institution server may inquire about the fund collateral for the loan service to the management server, and the management server may transmit the fund collateral agreement to the financial institution server in response thereto.

3. In addition, if necessary, the management server can deliver the non-financial credit information of small business owners to the server of the financial institution. Such non-financial credit information may be generated by the management server through the received user profile.

4. The financial institution server determines whether the small business is eligible to use the loan service, and transmits a loan approval/rejection message to the small business terminal.

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

Referring to FIG. 16 , the first terminal may include a small business terminal, the second terminal may include a consumer terminal, and the third terminal may include a provider terminal.

The management server performs a procedure for registering point information of the first terminal (S1600). For example, when the small business selects the loan service icon 1200 displayed on the first terminal, the first terminal may transmit a registration request message including the quantity value of the points to be loaned received from the small business to the management server. have. The management server may use the user profile of the small business received from the first terminal to register the point information that the small business can borrow, and transmit a registration acceptance message to the first terminal. Such point information may be managed for each user of each terminal by the management server. To this end, the management server may manage a table having users of each terminal as an index, and each table may have point information as a member of each user index. In addition, such point information may include 1) a quantity value, 2) a timer, and 3) a remaining distribution order value of points currently held by the user indicated by the user index.

The management server sets a timer related to the point of the first terminal (S1610). The value of the timer may be set differently according to the policy of the point. For example, when one month has elapsed from the point registration date of the first terminal, the management server may set the timer to one month so that the points of the first terminal expire, and operate the timer. Before the timer expires, the management server may transmit a notification message for notifying the expiration of the timer to the terminal of the user possessing the point related to the timer.

The management server sets the distribution order related to the point of the first terminal (S1620). The value of the timer may be set differently according to the policy of the point. For example, when the points of the first terminal are circulated 10 times, the management server may set the distribution order to 10 so that it can be converted into cash. When the distribution order reaches the set value, the management server may transmit a notification message for notifying that the distribution order has reached the set value to the terminal of the user having the points related to the distribution order. To this end, the management server may manage the residual distribution order value of points owned by each user for each user.

The second terminal transmits a first point purchase request message to the first terminal (S1630). In more detail, the consumer may transmit a first point purchase request message to the first terminal through the second terminal in order to purchase the product/service provided by the small business with the consumer's points.

When it is determined that the purchase request using the points of the second terminal is justified as a response to the first point purchase request message, the first terminal performs a point information update procedure with the management server (S1640). For example, the first terminal may transmit a point information update request message to the management server.

The management server counts the point distribution order of the second terminal and updates the point information (S1641). For example, the management server may receive the point quantity change value of the small business owner and the consumer through the point information update request message. The management server may increase the distribution order of the consumer's points that the second terminal intends to use for purchase by one. In addition, the management server updates the point information according to the change in the number of points of small business owners and consumers.

The first terminal transmits a first point purchase response message indicating that the purchase has been completed to the second terminal (S1650).

The first terminal transmits a second point purchase request message to the third terminal (S1660). for example. The small business owner may use the points of the first terminal to purchase goods/services from the supplier.

When it is determined that the purchase request using the points of the first terminal is justified as a response to the second point purchase request message, the third terminal performs a point information update procedure with the management server (S1670). For example, the third terminal may transmit a point information update request message to the management server.

The management server counts the point distribution order of the first terminal and updates the point information (S1680). For example, the management server may receive the point quantity change value of the small business owner and the provider through the point information update request message. The management server may increase the distribution order of the points of the small business owner that the first terminal intends to use for purchase by one. In addition, the management server updates the point information according to the change in the number of points of small business owners and suppliers.

The third terminal transmits a second point purchase response message indicating that the purchase is complete to the first terminal (S1690).

Each of the steps of S1610 to S1690 described above may be performed together or separately, and if necessary, may be repeatedly performed.

The management server generates a point cash conversion key of the first terminal (S1691). For example, when the residual circulation order value of the first terminal becomes 0, the management server may generate a key for converting points of the first terminal into cash. Such a key may be managed through the management server, and may be shared with terminals holding the points of the first terminal. For this, the above-described point information may be used. In addition, the management server may transmit a notification message notifying that the points of the first terminal can be converted into cash to the terminals holding the points of the first terminal.

The first terminal may transmit a request message requesting a point refund of the first terminal to the management server (S1692). The management server compares the key included in the refund request message and, if justified, may refund the points of the first terminal in cash.

The third terminal may transmit a request message for requesting a point refund of the first terminal that the third terminal possesses to the management server (S1693). If the provider satisfies the VIP holder condition, the management server may refund the point cash value of the first terminal held by the third terminal with a premium.

17 is an embodiment of a management server to which this specification can be applied.

Referring to FIG. 17 , an embodiment of FIG. 16 is shown from the perspective of a management server.

1. The management server performs a procedure of registering the point information of the small business related to the financial business that the small business wants to use through the small business terminal. For example, the point information may include 1) a quantity value, 2) a timer, and 3) a residual distribution order value of points currently held by the user indicated by an index indicating the point information.

2. The management server sets the distribution order value related to the points of the small business owner based on the policy of the financial business that the small business owner wants to use. Such a policy may be preset in the management server or may be set through a financial institution server connected to the management server.

3. The management server receives a message requesting update of point information managed by the management server from the provider terminal based on the receipt of a purchase request message using the points of the small business owner from the small business terminal.

4. Based on the message requesting the update of the point information, the management server counts the value of the point distribution order of the small business and updates the point information.

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 kinds 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 includes implementation 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 equivalent scope 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 will 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 the present 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)

When the management server receives the registration request message including the quantity value of the points to be loaned from the small business terminal, it uses the user profile of the small business to register the point information that the small business can borrow, and the registration acceptance message to the small business terminal sending a;
setting, by the management server, a value of a timer value for erasing the point of the small business terminal;
setting, by the management server, a distribution order related to the point of the small business terminal;
providing, by the management server, a notification message informing that the circulation order has reached the set value to the terminal of the user having points related to the distribution order when the distribution order reaches the set distribution order;
transmitting, by the consumer terminal, a first point purchase request message to the small business terminal;
providing, by the small business terminal, a point information update request message to a management server when it is determined that the purchase request using the points of the consumer terminal is justified as a response to the first point purchase request message;
The management server receives the point quantity change value of the small business owner and the consumer through the point information update request message, the consumer terminal increases the distribution order of the consumer's points that the consumer terminal wants to use for purchase, and the point information according to the change in the consumer's point quantity updating the ;
transmitting, by the small business terminal, a first point purchase response message informing that the purchase has been completed to the consumer terminal;
providing, by the small business terminal, a second point purchase request message for using the points of the small business terminal for product purchase to the provider terminal;
transmitting, by the provider terminal, a point information update request message to a management server when it is determined that the purchase request using the points of the small business terminal is justified;
The management server receives the point quantity change value of the small business owner and the provider through the point information update request message, increases the distribution order of the points of the small business owner that the small business terminal wants to use for purchase, and according to the change in the number of points of the consumer updating point information;
The provider terminal includes the step of transmitting a second point purchase response message informing that the purchase has been completed to the small business terminal,
generating, by the management server, a key for cash conversion when the circulation order reaches the set circulation order, and transmitting the key to a terminal holding the point based on the point information; and
Further comprising the step of the management server increasing the value of the points when the VIP holder converts the points into cash, and devaluing the value of the points when the general holder converts the points into cash,
The distribution order related to the above points is
The number of times the points can be distributed to other users before the timer expires, and the points can be converted into cash when the number of times the points can be distributed ends.
A service provision method for small business owners using big data.


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