KR102266153B1 - Artificial intelligence-based method of providing consumer preference through self-psychological analysis platform - Google Patents

Abstract

The present invention relates to a method for providing consumer preference through an artificial intelligence-based self-psychological analysis platform, which uses an artificial intelligence model which is learned based on personal information input by a user while using a self-psychological analysis platform, preferred advertisement content information, and preference information, so as to predict an advertising model with the highest advertising effectiveness when a company wants to advertise by targeting a specific group of consumers.

Description

Method of providing consumer preference through artificial intelligence-based self-psychoanalysis platform

The present invention relates to a method for providing consumer preference through an artificial intelligence-based self-psychoanalysis platform.

Recently, with the concept of mind therapy, self-psychoanalysis platforms that psychologically analyze users themselves and provide solutions to form a positive energy for users are in vogue.

On the other hand, artificial intelligence (AI) is a field of computer engineering and information technology that realizes human learning ability, reasoning ability, perceptual ability, and natural language understanding ability through computer programs.

Companies are trying to predict consumer preferences using artificial intelligence for effective marketing.

In particular, supervised learning, which is used in the most fields of artificial intelligence, is a method of predicting future values using training data including correct answers (labels). The purity of training data and test data in the data collection stage (for example, accuracy and truthfulness) determines the performance of an AI algorithm.

However, in the prior art, since training data and test data are generally obtained through public data, big data, and web crawling, the purity of the data is low and the performance of the artificial intelligence algorithm is lowered, so there is a problem that it is not possible to accurately identify consumer preferences. .

Furthermore, the prior art has a problem in that the diversity of training data and test data is low, so that consumer preferences cannot be grasped from various viewpoints.

The problem to be solved by the present invention is an artificial intelligence model learned based on personal information input by a user while using a self-psychological analysis platform, preferred advertisement content information, and preference information. It is to provide a method of providing consumer preference through an artificial intelligence-based self-psychological analysis platform that can predict the advertising model with the highest advertising effectiveness.

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

A method of providing consumer preferences through an artificial intelligence-based self-psychoanalysis platform of the present invention for solving the above-described problems comprises: transmitting personal data and preference data by a plurality of user terminals to a main server; transmitting, by the main server, mental state data and coin charging list data matching the personal data and the preference data based on a preset biorhythm list every day to the plurality of user terminals; transmitting, by the plurality of user terminals, selection data selected from among the coin charging list data to the main server; transmitting, by the main server, reward coins and solution data to the plurality of user terminals; and generating, by the main server, the personal data and the selection data as feature values and setting the preference data as label values to generate an artificial intelligence consumer preference algorithm through supervised learning.

The selection data may include at least one of product data and marketing content data.

The marketing content data may include at least one of image marketing content data, slideshow marketing content data, and program download method marketing content data.

The personal data may include at least one of gender data, age data, and residential area data.

The preference data may be data about a person preferred by the user.

In the method of providing consumer preference through the artificial intelligence-based self-psychoanalysis platform of the present invention, personal data and selection data input by the user to analyze and understand their psychology are used as feature values (independent variables). By learning artificial intelligence by setting training data and test data with the data as label values (dependent variables), there is an advantage in that it is possible to create an artificial intelligence algorithm that can accurately and accurately predict consumer preferences from various viewpoints.

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

1 is a conceptual diagram illustrating a method of providing consumer preference through the artificial intelligence-based self-psychological analysis platform of the present invention.
2 is a block diagram showing a system of a method for providing consumer preference through the artificial intelligence-based self-psychological analysis platform of the present invention.
3 is a flow chart showing the procedure of a method for providing consumer preference through the artificial intelligence-based self-psychological analysis platform of the present invention.
4 is a table showing feature values and label values for learning artificial intelligence of a method of providing consumer preference through the artificial intelligence-based self-psychoanalysis platform of the present invention.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully understand the scope of the present invention to those skilled in the art, and the present invention is only defined by the scope of the claims.

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

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless specifically defined explicitly.

Hereinafter, a method 1000 for providing consumer preference through the AI-based self-psychological analysis platform of the present invention will be described with reference to the drawings. 1 is a conceptual diagram illustrating a method of providing consumer preference through the artificial intelligence-based self-psychological analysis platform of the present invention, and FIG. 2 is a system of a method of providing consumer preference through the artificial intelligence-based self-psychological analysis platform of the present invention. 3 is a flowchart showing the sequence of a method for providing consumer preference through the artificial intelligence-based self-psychological analysis platform of the present invention, and FIG. 4 is a consumer through the artificial intelligence-based self-psychological analysis platform of the present invention. This is a table showing feature values and label values for training artificial intelligence of a method of providing preference.

The method 1000 for providing consumer preference through the artificial intelligence-based self-psychoanalysis platform of the present invention may be performed in a system including a plurality of user terminals 10 , a network 20 , and a main server 30 . .

The plurality of user terminals 10 is a method 1000 for providing consumer preferences through the artificial intelligence-based self-psychoanalysis platform of the present invention. Web pages, app pages (app list), programs or applications are provided to a plurality of users. It may be a terminal.

The plurality of user terminals 10 may be implemented as a computer that can access the main server 30 at a remote location through the network 20, for example, a laptop computer, a desktop (web browser) equipped with navigation, and a desktop ( Desktop), laptop (Laptop), etc. may be included.

In detail, the plurality of user terminals 10 are wireless communication devices that ensure portability and mobility, and include navigation, Personal Communication System (PCS), Global System for Mobile communications (GSM), Personal Digital Cellular (PDC), and Personal Communication System (PHS). Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), Wibro (Wireless Broadband Internet) terminal, All types of handheld-based wireless communication devices such as smartphones, smart pads, and tablet PCs may be included, but are not limited thereto.

The plurality of user terminals 10 may be connected to the main server 30 through the network 20 .

The network 20 means a connection structure capable of exchanging information between each node such as a plurality of user terminals 10 and the main server 30, and an example of such a network includes RF, 3rd Generation Partnership Project (3GPP). ) network, LTE (Long Term Evolution) network, 5GPP (5th Generation Partnership Project) network, WIMAX (World Interoperability for Microwave Access) network, Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network) , WAN (Wide Area Network), PAN (Personal Area Network), Bluetooth (Bluetooth) network, NFC network, satellite broadcasting network, analog broadcasting network, DMB (Digital Multimedia Broadcasting) network, and the like are included, but are not limited thereto.

The main server 30 may be a server that provides a web page, an app page, a program or an application related to the method 1000 for providing consumer preference through the artificial intelligence-based self-psychoanalysis platform of the present invention.

The method 1000 for providing consumer preference through the artificial intelligence-based self-psychoanalysis platform of the present invention comprises the steps of a plurality of user terminals 10 transmitting "personal data" and "preference data" to the main server 30 ( 100); The main server 30 transmits "mental state data" and "coin charging list data" matching "personal data" and "preference data" to the plurality of user terminals 10 every day based on a preset biorhythm list. step 200; A plurality of user terminals 10 transmitting the "select data" selected from the "coin charging list data" to the main server 30 (300); The main server 30 transmits the reward coin and "solution data" to the plurality of user terminals 10 (400); The main server 30 sets "personal data" and "select data" as feature values and sets "preference data" as label values to generate an artificial intelligence consumer preference algorithm by supervised learning (500); can

In the step 100 in which the plurality of user terminals 10 transmit "personal data" and "preference data" to the main server 30 , the user uses the user terminal 10 to provide "personal data" and "preference data" , and "personal data" and "preference data" input through the user terminal 10 may be transmitted to and stored in the main server 30 .

In this case, "personal data" includes "gender data (eg, female)" about the user's gender, "age data (eg, age 17)" about the user's age, and "residence area" about the user's residence data (for example, living in Gangnam-gu, Seoul)", but is not limited thereto.

"Personal data" is used as a feature value for learning artificial intelligence, and as described above, "personal data" includes a user's gender, age, residential area, etc., so the AI-based self-psychoanalysis platform of the present invention The method 1000 for providing consumer preference through the method 1000 may predict consumer preference from various viewpoints.

On the other hand, since the purity (accuracy) of “personal data” is important to ensure the accuracy of predicted consumer preferences, a self-authentication program may be set in the main server 30, and the user can provide accurate “personal data” through self-authentication. data" may be transmitted to the main server 30 through the user terminal 10 .

In addition, "preference data" can set various people, things, and ideas as objects, and for example, "preference data" is a list of candidate people transmitted from the main server 30 to a plurality of user terminals 10, among which It may be data about a preferred person (marketing model; entertainer) selected by the user, but is not limited thereto.

Since "preference data" is used as a label value for learning artificial intelligence, the purity (accuracy) of "preference data" can be guaranteed because it is data determined by the user's free will in order to understand and know his/her own psychology. .

The main server 30 transmits "mental state data" and "coin charging list data" matching "personal data" and "preference data" to the plurality of user terminals 10 every day based on a preset biorhythm list. In step 200, at least one of “personal data” and “preference data” transmitted through the plurality of user terminals 10 in the previous step in the viro rhythm list preset and stored in the database of the main server 30 is matched. "Psychological state data" may be transmitted to the plurality of user terminals 10 together with "coin charging list data" every day.

That is, a plurality of users may be provided with "mental state data" based on a biorhythm every day through the plurality of user terminals 10, and the "mental state data" is determined to be low in energy, and the psychological state is not good. On the day, you can receive coins by receiving a list of various coin charging methods that can recharge coins (as will be described later, you can improve your mental state by providing solution data on your psychological state with reward coins).

As an example, the coin charging means list is a list that provides various marketing contents to users, and provides users with specific products (eg, snacks, household goods, school supplies, etc.; furthermore, not only hardware but also software, that is, game applications. A list that provides specific marketing content (for example, content in which a user views a video advertisement, content in a manner in which a user views a slide advertisement, content in a manner in which a user downloads an application, etc.) may be, but is not limited thereto.

In the step 300 of the plurality of user terminals 10 transmitting the "selection data" selected from the "coin charging list data" to the main server 30, the user selects a specific means from the various coin charging means lists provided to him/her. can be exposed to marketing.

As an example, the user may be exposed to marketing by selecting a specific means from a list of various coin charging means and receiving specific marketing content for a specific product.

In this case, since the "select data" is data selected by the user among the "coin charging list data", it may naturally include "product data" and "marketing content data".

"Select data" is used as a feature value for learning artificial intelligence, and as described above, "select data" includes specific marketing content for a specific product provided by the user, so the AI-based self of the present invention The method 1000 for providing consumer preferences through a psychological analysis platform may predict consumer preferences from various viewpoints.

On the other hand, since "select data" is data selected by a user according to his or her own free will, the purity (accuracy) of "select data" can be guaranteed.

In the step 400 in which the main server 30 transmits the reward coin and "solution data" to the plurality of user terminals 10, the user consumes the coin provided as a reward (reward) exposed to marketing to create a "solution" data" may be provided.

The "solution data" may consist of psychological counseling or advice that can resolve a user's unfavorable psychological state, but is not limited thereto.

The main server 30 sets "personal data" and "select data" as feature values and sets "preference data" as label values to generate artificial intelligence consumer preference algorithms through supervised learning. In step 500, the main server 30 performs supervised learning by setting the big data having high purity and various viewpoints stored as much as the number of the plurality of user terminals 10 as a training/test dataset, and as a result, any By inputting "personal data" and "select data", an artificial intelligence consumer preference algorithm that predicts consumer preferences can be created.

For reference, machine learning can be defined as a methodology that gives a computer the ability to learn on its own without being explicitly programmed, and this is a method that allows a program to learn patterns of data by itself based on data. Machine learning is divided into supervised learning and unsupervised learning depending on whether the correct answer is specified in the data required for learning, and classification by dividing data into finite categories according to the purpose of use, continuous It is divided into regression, which maps to values, clustering, which groups similar data, and dimensional reduction, which maps multidimensional data to a representative lower dimension.

Deep learning, one of the machine learning techniques, has dramatically improved the performance of machine learning, which has been stagnant for a while. Deep learning is a methodology based on an artificial neural network (ANN) algorithm that mimics the overlap of synapses in the structure of the human brain. , as a deep learning structure, a deep neural network (DNN) in which a plurality of hidden layers exist between an input layer and an output layer, and a filter required for factor extraction in front of the hidden layer It can include a convolutional neural network (CNN) that learns filters together with , and a recurrent neural network (RNN) that can process time series data by stacking artificial neural networks at each time. Here, the high performance of the deep learning model is explained in two ways. First, the neural network is a Universal Approximator that can approximate all kinds of functions by superimposing the weighted sum of functions in each layer. Data can be simulated with high accuracy. Second, it is important to properly extract the factors representing the data in order to classify the data well. In addition, deep learning is a developed form of a model in the field of artificial intelligence called a neural network, in which the hidden layer of an artificial neural network consisting of a hierarchical structure consists of several stages. Recent deep learning models have many inner layers, so the number of weights (meaning the strength of the connection) connecting nodes can be up to billions.

The method 1000 for providing consumer preference through the artificial intelligence-based self-psychoanalysis platform of the present invention is a feature value of data about a user who has numerous genders, ages, and regions of residence and has been provided with marketing content for a specific product as a feature value. It is possible to generate an artificial intelligence consumer preference algorithm that is provided as a label value and learned by receiving data about an object that the user prefers (eg, advertisement model; celebrity) as a label value.

As a result, if "Female, 17 years old, living in Gangnam-gu, Seoul, snack, video marketing content" is input as arbitrary data into the AI consumer preference algorithm as a feature value (independent variable), The label value (dependent variable) with the highest preference is output (predicted).

If a company plans video marketing content for snacks for 17-year-old women living in Gangnam-gu, Seoul, BTS can be selected as an advertisement model using artificial intelligence consumer preference algorithm.

In other words, when a company plans specific marketing content for a specific target group, it is possible to enjoy high-value marketing effects by selecting a specific singer as an advertisement model by an artificial intelligence consumer preference algorithm verified by high-purity data learning. have.

The method 1000 for providing consumer preference through the artificial intelligence-based self-psychoanalysis platform of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

The above-described program is C, C++, JAVA, machine language, etc. that a processor (CPU) of the computer can read through a device interface of the computer in order for the computer to read the program and execute the methods implemented as a program It may include code (Code) coded in the computer language of Such code may include functional code related to a function defining functions necessary for executing the methods, etc., and includes an execution procedure related control code necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, the code may further include additional information necessary for the processor of the computer to execute the functions or code related to memory reference for which location (address address) in the internal or external memory of the computer to be referenced. have. In addition, when the processor of the computer needs to communicate with any other computer or server located remotely in order to execute the above functions, the code uses the communication module of the computer to determine how to communicate with any other computer or server remotely. It may further include a communication-related code for whether to communicate and what information or media to transmit and receive during communication.

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

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

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

Claims (5)

transmitting, by a plurality of user terminals, personal data including a user's gender, age, and residential area, and preference data for a person preferred by the user to a main server;
Based on the daily biorhythm list preset by the plurality of user terminals, so that the main server can know the psychological state based on the biorhythm for a plurality of users every day and, if necessary, receive solution data for their psychological state with a reward coin Thus, the mental state data matching the personal data and the preference data, the video marketing content and slideshow marketing content and the program download method marketing content for a specific product including snacks, daily necessities and school supplies for the plurality of users Transmitting a list of coin charging list data that provides marketing content including;
transmitting, by the plurality of user terminals, selection data selected from among the coin charging list data to the main server;
transmitting, by the main server, the reward coins to the plurality of user terminals in return for exposure of a plurality of users to marketing, and transmitting the solution data when the reward coins are consumed; and
The main server sets the personal data and the selection data as feature values and sets the preference data as label values to generate an artificial intelligence consumer preference algorithm through supervised learning,
The select data includes product data including snacks, daily necessities, and school supplies, and marketing content data including image marketing content, slideshow marketing content, and program download method marketing content,
The solution data is composed of data on psychological advice for the plurality of users,
A method of providing consumer preference through an artificial intelligence-based self-psychological analysis platform, characterized in that the main server is set with a user's identity authentication program for accuracy of the personal data. delete delete delete delete

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