KR102238662B1 - System and method for evaluating future value of resell products using big data - Google Patents

Abstract

A system and method for evaluating the future value of resell products based on big data are provided. The system for evaluating the future value of resell products comprises: a big data collection unit that collects data on the resell products; And a future value evaluation unit that generates future value information for the resell product based on the collected data on the resell product, wherein the data on the resell product includes at least preference information for the resell product and the resell product. Information, and the future value evaluation unit generates the future value information based on preference information for the resell product and inquiry information for the resell product.

Description

System and method for evaluating future value of resell products using big data}

The present invention relates to a system and method for evaluating the future value of a resell product, and in particular, to a system and method for evaluating the future value of a resell product based on big data collected by various methods and providing it to users. .

With the advent of the 4th industrial revolution, with the development of online transactions and the revitalization of overseas direct search, the consumption pattern of consumers is changing from the conventional passive consumption form to the active and independent consumption form. In particular, consumers are showing a psychological consumption trend that they want to differentiate from others, and companies are releasing limited edition products in various areas, taking into consideration the changed consumption trend of these holders.

Limited edition products limit the sale quantity or sale timing of certain products. In the fashion industry, such limited edition releases are utilized as a marketing strategy and are becoming a major consumption pattern among younger generations. Limited products are considered art and value by consumers now as the invisible value part such as the design and meaning of the product is emphasized rather than the functional aspect provided by the original product. Therefore, there were also consumers who tried to purchase limited edition products in the order of waiting by staying up all night in front of the store (camping) from the day before the release to purchase limited edition products.

The popularity of these limited edition products and an increase in demand for them is leading to the formation of a secondary market where limited editions are purchased at a certain premium. A re-sell market of limited edition products is being formed centering on the fashion (sneakers, clothes, accessories, etc.) market, and this resale market in Korea is gradually increasing, centering on online. In addition, in addition to users who purchase and directly use limited edition products, resellers based on the concept of investors who set a certain amount of premium on the purchased products and resell them are gradually increasing.

However, the secondary market of these limited edition products is a market for which the future value is not determined, and the criteria for determining the premium are not clear. In the case of limited edition products, the value of the limited edition product is rapidly increased due to factors such as new releases and reproductions or certain social issues. Although it changes, it is difficult to accurately predict the value because all of the changes cannot be confirmed/predicted every time and the process of collecting key information is difficult. Accordingly, there is a situation in which the premium is somewhat excessively determined without a certain standard by the reseller described above, and the damage to the excessive premium is passed on to the actual user or the secondary purchaser. In addition, resell products have a problem in which it is difficult to verify the authenticity of online transactions. In other words, the risks and disadvantages arising from online transactions of such resell products are borne by buyers.

Therefore, there is a need for a system and method for evaluating the future value of resell products that can resolve information imbalances and establish a correct secondary market economy for resell products through judgment of the fair value of resell products.

Republic of Korea Patent Publication No. 10-2011-0094712 (August 24, 2011)

The present invention was conceived to solve the above-described problem, and the future value evaluation of resell products capable of resolving information imbalance and establishing a correct secondary market economy of resell products through judgment of the fair value of resell products. Systems and methods are provided.

A system for evaluating the future value of a resell product according to an embodiment of the present invention is a system for evaluating the future value of a resell product based on big data, comprising: a big data collection unit collecting data on the resell product; A future value evaluation unit that generates future value information for the resell product based on the collected data on the resell product; And a user interface unit that provides information on future value of the resell product to a user.

A method for evaluating the future value of a resell product according to another embodiment of the present invention is a method of evaluating the future value of a resell product in a system that evaluates the future value of a resell product based on big data, and collects data on the resell product. The step of doing; Generating future value information for the resell product based on the collected data on the resell product; And providing future value information on the resell product to a user.

The system and method for evaluating the future value of a reseller product according to an embodiment of the present invention are to consider the reaction and preference of general consumers to the reseller product, not the arbitrary price set by the reseller or the current market price. By setting a bar, it is possible to protect buyers of resell products, and to further activate the resell product trading market by allowing resell products to be traded at a reasonable price.

In addition, the system and method for evaluating the future value of the reseller product according to the present embodiment determine the future value of the reseller product by further considering the current state and authenticity of the reseller product, and can further protect the purchaser of the resell product, It can help form a secure e-commerce market.

1 is a block diagram showing the configuration of a system for evaluating a future value of a resell product according to an embodiment of the present invention.
2 is a block diagram showing a detailed configuration of a big data collection unit according to an embodiment of the present invention.
3 is a block diagram showing a detailed configuration of a future value evaluation unit according to an embodiment of the present invention.
4 is a flowchart of a method for evaluating the future value of a resell product according to another embodiment of the present invention.
5 is a flowchart illustrating detailed steps of collecting data on resell products according to another embodiment of the present invention.
6 is a flowchart illustrating detailed steps of generating future value information for the resell product according to another embodiment of the present invention.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those of ordinary skill in the art can easily implement the present invention. However, the present invention may be implemented in various different forms and is not limited to the embodiments described herein. Throughout the specification, when a part "includes" a certain component, it means that other components may be further included rather than excluding other components unless specifically stated to the contrary.

The terms used in the present specification have selected general terms that are currently widely used as possible while considering functions, but this may vary according to the intention or custom of a technician working in the field, or the emergence of new technologies. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in the description of the corresponding specification. Therefore, terms used in the present specification should be interpreted based on the actual meaning of the term and the entire contents of the present specification, not a simple name of the term.

In this specification, the term "re-sell products" refers to a product that an individual or a corporation intends to sell to others at a higher price than the original purchase price. That is, the resell product may refer to a used product that is not a used product, but an unused, unopened, or sealed product, but is not limited thereto, and refers to a used product that can be resold at a high price even though it is a used product. A limited edition or limited edition product manufactured and sold only for a specific period or a specific quantity may be included in the resell product.

1 is a block diagram showing the configuration of a system for evaluating a future value of a resell product according to an embodiment of the present invention. 2 is a block diagram showing a detailed configuration of a big data collection unit according to an embodiment of the present invention. 3 is a block diagram showing a detailed configuration of a future value evaluation unit according to an embodiment of the present invention.

1 to 3, the system 10 for evaluating the future value of a resell product may collect big data and evaluate the future value of the resell product based on the collected big data. The future value evaluation system 10 of resell products may be included or connected to the online transaction system of the resell product in which the above-described transaction between the consumer and the seller is performed to provide evaluation information to at least one of the consumer and the seller, but is limited thereto. It is not. In some embodiments, the future valuation system 10 of resell products may be implemented separately from the online transaction system. In addition, the resell product evaluated by the future valuation system 10 of the resell product may be a product scheduled to be released, but is not limited thereto and may include a product currently being traded. In the following description, the process of evaluating the future value of one resell product is described, but the future value evaluation system 10 of the resell product evaluates the values of a plurality of reseller products, respectively, and evaluates each value to the user. Can provide. In addition, the future value evaluation system 10 of a resell product may provide an evaluation of a future value in consideration of the authenticity of a resell product requested by a specific consumer. That is, the future value evaluation system 10 of a resell product may determine and provide the future value of the resell product at the present time to assist in performing a reasonable transaction of the resell product between the consumer and the seller.

1, the future value evaluation system 10 of a resell product includes a big data collection unit 100, a future value evaluation unit 110, a database unit 120, and a user interface unit 130 do.

The future valuation system 10 of a resell product according to the embodiments and each device or unit constituting the same may have an aspect that is entirely hardware, or partially hardware and partially software. For example, each component of the system 10 for evaluating the future value of a resell product according to an embodiment of the present invention refers to a combination of hardware and software driven by the hardware. The hardware may be a data processing device including a central processing unit (CPU) or another processor. In addition, software driven by hardware may refer to an executing process, an object, an executable file, a thread of execution, a program, and the like. For example, the big data collection unit 100 may refer to a combination of hardware and software for collecting data distributed on the Internet web.

In addition, in this specification, each module constituting the future valuation system 10 of a resell product may be communicatively connected to each other in a distributed computing environment, and is not necessarily intended to refer to separate components that are physically separated. .

The big data collection unit 100 may collect data on resell products. The future value evaluation unit 110 generates future value information on the resell product based on the collected data on the resell product. The database unit 120 may store the collected data on the resell product and the future value information. The user interface unit 130 may provide the generated future value information to the user.

The data on the resell product may include at least preference information on the resell product and inquiry information on the resell product. In addition, the future value evaluation unit 110 may generate the future value information based on preference information for the resell product and inquiry information for the resell product. The user may check the future value of the resell product through future value information provided by the user interface unit 130. The user interface unit 130 is an interface for inputting/outputting data information between a system 10 for evaluating the future value of a resell product and a user using the same, and an interface for inputting specific data of the user and evaluating the future value of the resell product It may be configured to include an output interface or the like for providing the data generated by the system 10 to a user.

Referring to FIG. 2, the big data collection unit 100 may collect data on resell products from the server S. The big data collection unit 100 includes a resell product inquiry information collection unit 101 that collects inquiry information on resell products, and a resell product preference information collection unit 102 that collects preference information on resell products.

The resell product inquiry information collection unit 101 may search for a resell product from the server S and collect related text data. That is, the server S refers to a server through which general users can obtain information on resell products. For example, the server S may include a portal site server such as Naver, Google, or Daum, or an SNS server such as Facebook and Instagram. In addition, the server S may include a video streaming server such as YouTube.

The resell product inquiry information collection unit 101 may set a resell product corresponding to an evaluation target as an input keyword, and collect text data related to the input keyword from the server S. Here, the resell product inquiry information collection unit 101 may receive information on input keywords and period information of a resell product corresponding to an evaluation target. The input keyword and period information of the resell product may be provided from the user through the user interface unit 130. That is, the user may input a target product for evaluation through the user interface unit 130 and check the result thereof. However, the present invention is not limited thereto, and the resell product inquiry information collecting unit 101 may search for a preset keyword in the server S according to a preset period and collect related text data. For example, the resell product inquiry information collection unit 101 stores news, cafe posts, posts, blogs, etc. related to the input keyword based on the input keyword and period information in order to extract the data set required for data analysis. Search in. Before the resell product is released, not only basic information about the resell product, but also a number of related texts reflecting the expectations of consumers about the resell product may be posted on the portal site or SNS.After the resell product is released, the resell product Relevant text such as evaluation, review, etc. may be posted on the portal site or SNS. Accordingly, the reseller product inquiry information collection unit 101 stores the text data related to the expectations of consumers before the release of the resell product and the evaluation after the release of the resell product by posting news, cafe posts, posts, etc. to portal sites or faces such as Naver, Daum, etc. It can be collected by searching on social media such as books and Instagram. The resell product inquiry information collection unit 101 fetches an HTML file including a list, title, text, and comments of all the searched text data from the server S. In addition, the resell product inquiry information collection unit 101 may perform encoding for Korean language support, extract text data from an HTML file, generate the extracted text data in a txt format, and store the extracted text data in the database unit 120. .

The resell product preference information collection unit 102 collects preference information for resell products. The resell product preference information collection unit 102 may perform a preference survey for a specific resell product targeting consumers. The resell product preference information collection unit 102 may provide consumers with a user interface capable of displaying the consumer's preference for the resell product together with basic information of the resell product. For example, the resell product preference information collection unit 102 may examine the preference for a resell product by providing a user interface that allows the consumer to press a "Like" button or to set a rating. The preference survey performed by the resell product preference information collection unit 102 may be performed to investigate expectations for resell products scheduled to be released, but is not limited thereto, and is performed for consumers' evaluation of resell products after release. It could be. The resell product preference information collection unit 102 may collect quantitative data on the result of the preference survey conducted within a specific period as resell product preference information, and store the collected resell product preference information in the database unit 120. I can.

As described above, the database unit 120 may store data on resell products collected by the big data collection unit 100. In addition, the database unit 120 may include related information on resell products. For example, it may further include related information for evaluating the future value of the resell product, such as the initial release price of the resell product and the recent transaction amount of the resell product, and such related information is also provided by the big data collection unit 100. It may be collected and stored in the database unit 120. That is, the big data collection unit 100 may further collect information related to the recent transaction amount of the resell product from the server on which the online transaction system is implemented, and store this in the database unit 120.

The future value evaluation unit 110 may generate future value information on the resell product based on data on the resell product collected by the big data collection unit 100 and stored in the database unit 120.

As shown in FIG. 3, the future value evaluation unit 110 includes a text analysis unit 111, a future value calculation unit 112, and a future price estimation unit 113.

The text analysis unit 111 may analyze text data related to the resell product collected by the resell product inquiry information collection unit 101 to calculate an independent variable required for future value calculation. Specifically, the text analysis unit 111 may analyze text data related to the resell product and perform language processing to calculate the frequency of positive expressions for the resell product and the frequency of negative expressions for the resell product. The text analysis unit 111 may include a positive topic dictionary and a negative topic dictionary registered in advance, and calculate the frequency of positive expressions for resell products by matching text data and a positive topic dictionary, and calculate the frequency of positive expressions for resell products, and text data and negative topics By matching dictionaries, the frequency of negative expressions for resell products can be calculated. The frequency of the positive expression and the frequency of the negative expression analyzed and calculated by the text analysis unit 111 are independent variables (positive independent variables, negative independent variables) for determining the future value of the resell product, and the future value calculation unit 112 ).

The future value calculation unit 112 calculates the future value of the resell product based on the independent variable calculated by the text analysis unit 111 and the resell product preference information collected by the resell product preference information collection unit 102. That is, the resell product preference information collected by the product preference information collection unit 102 may also be converted into numerical data and used as an independent variable for calculating future value.

The future value calculation unit 112 may model the future value y(n) of the resell product through a linear regression equation such as Equation 1 below.

[Equation 1]

: 리셀 상품의 미래 가치,

은 독립 변수로 각각 긍정 독립 변수, 부정 독립 변수, 리셀 상품 선호도 정보에 기초한 선호도 변수에 해당하며,

은 상기 독립 변수의 회귀 계수로서 통계적으로 산출되어 설정되는 상수 값에 해당하며,

은 초기 상수 값에 해당한다)(here,

: Future value of resell products,

Is an independent variable and corresponds to a positive independent variable, a negative independent variable, and a preference variable based on resell product preference information, respectively.

Corresponds to a constant value that is statistically calculated and set as a regression coefficient of the independent variable,

Corresponds to the initial constant value)

The future value of the reseller product calculated by the future value calculation unit 112 is calculated based on not only the positive reactions and negative reactions of general consumers to the reseller product, but also the preference survey for the reseller product, and is random by a specific reseller. It may be a value calculated through fair value determination, not a price determined as. That is, if positive responses and preference survey results are high, the future value of the resell product can be calculated high, and if negative reaction is high, the future value of the resell product can be calculated low.

)에 반영되어 산출되는 미래 가치(y(n))에 영향을 미칠 수 있다.In some embodiments, a modified model product in which the design or color of the resell product is changed based on a specific model product may be released. In other words, when the A model product is popular, the modified model product of the A model may be released as a limited edition through collaboration with other famous designers. When determining the future value of the modified model product of the A model, the present value of the previously released A model product may be considered. That is, if the previously released A model product is currently popular, the future value of the modified model product of the A model is also expected to be high. Here, when the resell product is a modified model product of the first model product, the big data collection unit 100 may further collect the present value of the first model product, and the future value calculation unit 112 is a first model product. The future value of the resell product can also be determined by reflecting the present value of the product. Exemplarily, the present value of the product of the same kind is the initial constant value of Equation 1 (

) Can affect the calculated future value (y(n)).

The future value y(n) calculated by the future value calculation unit 112 is calculated as a constant value, and the corresponding value itself may be provided to the user through the user interface unit 130 as future value information. However, the future value evaluation system 10 of a resell product according to an embodiment of the present invention predicts the future price of the resell product, and uses the user interface unit 130 as the future value information of the expected future price of the resell product. It can be provided to the user.

The future price estimation unit 113 may predict the future price of the resell product described above by using the future value y(n) calculated by the future value calculation unit 112. Here, when the resell product is not released, the future price estimation unit 113 may predict the future price by considering the calculated future value y(n) and the initial sale price of the resell product. In addition, when a resell product is released and a transaction is being performed in the online secondary market, the future price prediction unit 113 considers the calculated future value (y(n)), the initial selling price of the resell product, and the current transaction price. Thus, the future price of the resell product can be predicted. The future price of the resell product predicted by the future price prediction unit 113 may be provided to the user through the user interface unit 130 as future value information.

In addition, the future value evaluation system 10 of a resell product may perform a future value evaluation of a resell product owned by a user that a specific user requests through the user interface unit 130. That is, a specific user may request an individual future value evaluation of a resell product owned by the user through the user interface unit 130. The user may be a reseller who intends to sell his or her own resell product to other users, or a user who wants to be judged of the future value of the own resell product.

The future value evaluation unit 110 of the future value evaluation system 10 of the resell product checks the authenticity of the user-owned resell product requested through the user interface unit 130, and based on the confirmed authenticity result. Individual future valuation of the resell product to be evaluated can be further performed.

The system 10 for evaluating the future value of a resell product according to an embodiment of the present invention may further include a resell product authenticity determination unit 140 that checks whether the resell product to be evaluated is authentic.

First, the resell product authenticity determination unit 140 may determine a current state of an individual resell product for which evaluation is requested. The user interface unit 130 may be configured to obtain an image of an individual resell product for which evaluation is requested. The resell product authenticity determination unit 140 may receive an image of an individual resell product from which the current state of the individual resell product can be checked from the user interface unit 130. The resell product authenticity determination unit 140 may determine a current state (whether it is damaged or used) of the provided resell product. The current state of the resell product may also act as an independent variable, and the determined result value is provided to the future value evaluation unit 110.

Here, the big data collection unit 100 further includes a resell product image information collection unit 103. The resell product image information collection unit 103 may collect an image of a resell product requested for evaluation from the server S. Resell product image information may be collected from a portal site server such as Naver, Google, or Daum, an SNS server such as Facebook, Instagram, or a video streaming server such as YouTube.

The resell product authenticity determination unit 140 may compare the image data collected by the resell product image information collection unit 103 with the image of the evaluation target resell product to determine whether the evaluation target resell product is authentic. The resell product authenticity determination unit 140 extracts each unique feature (feature, feature) from the collected image of the resell product and the image of the resell product to be evaluated, and compares the extracted unique features to determine the authenticity. have. In addition, the resell product authenticity determination unit 140 further uses a histogram similarity comparison method that calculates and compares the similarity by dividing the image into cells and converting it into color features including location information. It is possible to determine whether or not to be similar. In addition, the resell product authenticity determination unit 140 includes unique template features such as landmark points, curves, curved surfaces, and volumes for the resell product, and matches the feature with the image of the resell product to be evaluated to resell the evaluation target. It is also possible to determine whether the image of the product is authentic or not.

The authenticity result determined by the resell product authenticity determination unit 140 may also act as an independent variable, and the determined result value is provided to the future value evaluation unit 110.

The future value evaluation unit 110 may evaluate the future value of the resell product to be evaluated by using the result values (current state variable of the resell product, the authenticity variable) provided from the resell product authenticity determination unit 140 as independent variables. have. Specifically, the future value calculation unit 112 of the future value evaluation unit 110 is based on the independent variable calculated by the text analysis unit 111 and the resell product preference information collected by the resell product preference information collection unit 102. The future value of the resell product to be evaluated can be calculated by further considering the current state variable and the authenticity variable. The future value calculation unit 112 may model the future value y(n) of the resell product through a linear regression equation such as Equation 2 below.

[Equation 2]

: 리셀 상품의 미래 가치,

은 독립 변수로 각각 긍정 독립 변수, 부정 독립 변수, 리셀 상품 선호도 정보에 기초한 선호도 변수, 리셀 상품의 현재 상태 변수, 리셀 상품의 진위 여부 변수에 해당하며,

은 상기 독립 변수의 회귀 계수로서 통계적으로 산출되어 설정되는 상수 값에 해당하며,

은 초기 상수 값에 해당한다)(here,

: Future value of resell products,

Is an independent variable and corresponds to a positive independent variable, a negative independent variable, a preference variable based on resell product preference information, a current state variable of a resell product, and a variable whether the resell product is authentic or not.

Corresponds to a constant value that is statistically calculated and set as a regression coefficient of the independent variable,

Corresponds to the initial constant value)

The future value of the resell product calculated by the future value calculation unit 112 may be determined in consideration of the current state and authenticity of the resell product to be evaluated. In addition, the future value of the resell product calculated by the future value calculation unit 112 is calculated based on not only the positive reactions and negative reactions of general consumers to the reseller product, but also the preference survey for the reseller product. It may be a value calculated through fair value determination, not a price arbitrarily determined by.

That is, the future value evaluation system 10 of the reseller product according to the present embodiment considers the reaction and preference of general consumers to the reseller product, not the arbitrary price set by the reseller or the current market price. By setting the value, it is possible to protect buyers of resell products, and promote the resell product transaction market by allowing resell products to be traded at a reasonable price.

In addition, the future value evaluation system 10 of the reseller product according to the present embodiment determines the future value of the reseller product by further considering the current state and authenticity of the reseller product, thereby further protecting the purchaser of the resell product. , Can help form a secure e-commerce market.

Hereinafter, a method for evaluating the future value of a resell product according to another embodiment of the present invention will be described.

4 is a flowchart of a method for evaluating the future value of a resell product according to another embodiment of the present invention. 5 is a flowchart illustrating detailed steps of collecting data on resell products according to another embodiment of the present invention. 6 is a flowchart illustrating detailed steps of generating future value information for the resell product according to another embodiment of the present invention.

A method for evaluating the future value of a resell product according to another embodiment of the present invention is an evaluation method performed in the system for evaluating the future value of a resell product of FIGS. 1 to 3 described above, and FIGS. 1 to 3 are Can be referenced.

4 to 5, the method for evaluating the future value of the resell product is a method of evaluating the future value of the resell product in a system that evaluates the future value of the resell product based on big data. Collecting (S100); Generating future value information for the resell product based on the collected data on the resell product (S200); And providing (S300) future value information on the resell product to a user, wherein the data on the resell product includes at least preference information for the resell product and inquiry information on the resell product, and the In the step of generating future value information for a resell product, the future value information is generated based on preference information for the resell product and inquiry information for the resell product.

First, data on resell products is collected (S100).

The big data collection unit 100 may collect data on resell products from the server S. The big data collection unit 100 includes a resell product inquiry information collection unit 101 that collects inquiry information on resell products, and a resell product preference information collection unit 102 that collects preference information on resell products.

In the step of collecting data on the resell product (S100), performing a search in a server by setting the resell product as an input keyword, and collecting text data related to the input keyword as inquiry information on the resell product. (S110); And collecting preference information for the resell product by performing a preference survey on the resell product for consumers (S120).

The resell product inquiry information collection unit 101 may set a resell product corresponding to an evaluation target as an input keyword, and collect text data related to the input keyword from the server S. The resell product inquiry information collection unit 101 stores text data related to the expectations of consumers before the release of the resell product and evaluation after the release of the resell product by posting news, cafe posts, posts, etc. to portal sites such as Naver, Daum, or Facebook, It can be collected by searching on social media such as Instagram. The resell product inquiry information collection unit 101 fetches an HTML file including a list, title, text, and comments of all the searched text data from the server S. In addition, the resell product inquiry information collection unit 101 may perform encoding for Korean language support, extract text data from an HTML file, generate the extracted text data in a txt format, and store the extracted text data in the database unit 120. .

The resell product preference information collection unit 102 collects preference information for resell products. The resell product preference information collection unit 102 may perform a preference survey for a specific resell product targeting consumers. The preference survey performed by the resell product preference information collection unit 102 may be performed to investigate expectations for resell products scheduled to be released, but is not limited thereto, and is performed for consumers' evaluation of resell products after release. It could be. The resell product preference information collection unit 102 may collect quantitative data on the result of the preference survey conducted within a specific period as resell product preference information, and store the collected resell product preference information in the database unit 120. I can.

Next, future value information for the resell product is generated based on the collected data on the resell product (S200).

The future value evaluation unit 110 may generate future value information on the resell product based on data on the resell product collected by the big data collection unit 100 and stored in the database unit 120. The future value evaluation unit 110 includes a text analysis unit 111, a future value calculation unit 112, and a future price estimation unit 113.

The step of generating future value information for the resell product (S200) may include calculating an independent variable necessary for future value calculation by performing analysis on the collected text data related to the resell product (S210); Calculating a future value of the resell product based on the calculated independent variable and preference information for the resell product (S220); And predicting a future price of the resell product by using the future value calculated by the future value calculation unit (S230).

The text analysis unit 111 may analyze text data related to the resell product collected by the resell product inquiry information collection unit 101 to calculate an independent variable required for future value calculation. The calculated independent variable may include a positive independent variable corresponding to a frequency of a positive expression for the resell product and a negative independent variable corresponding to a frequency of a negative expression for the resell product.

The future value calculation unit 112 calculates the future value of the resell product based on the independent variable calculated by the text analysis unit 111 and the resell product preference information collected by the resell product preference information collection unit 102. That is, the resell product preference information collected by the product preference information collection unit 102 may also be converted into numerical data and used as an independent variable for calculating future value. The future value of the reseller product calculated by the future value calculation unit 112 is calculated based on not only the positive reactions and negative reactions of general consumers to the reseller product, but also the preference survey for the reseller product, and is random by a specific reseller. It may be a value calculated through fair value determination, not a price determined as. The future value of the resell product may be modeled as shown in Equation 1 below.

[Equation 1]

: 리셀 상품의 미래 가치,

은 독립 변수로 각각 긍정 독립 변수, 부정 독립 변수, 리셀 상품 선호도 정보에 기초한 선호도 변수에 해당하며,

은 상기 독립 변수의 회귀 계수로서 통계적으로 산출되어 설정되는 상수 값에 해당하며,

은 초기 상수 값에 해당한다)(here,

: Future value of resell products,

Is an independent variable and corresponds to a positive independent variable, a negative independent variable, and a preference variable based on resell product preference information, respectively.

Corresponds to a constant value that is statistically calculated and set as a regression coefficient of the independent variable,

Corresponds to the initial constant value)

The future value of the reseller product calculated by the future value calculation unit 112 is calculated based on not only the positive reactions and negative reactions of general consumers to the reseller product, but also the preference survey for the reseller product, and is random by a specific reseller. It may be a value calculated through fair value determination, not a price determined as. That is, if positive responses and preference survey results are high, the future value of the resell product can be calculated high, and if negative reaction is high, the future value of the resell product can be calculated low.

)에 반영되어 산출되는 미래 가치(y(n))에 영향을 미칠 수 있다.When the resell product is a modified model product of the first model product, the big data collection unit 100 may further collect the present value of the first model product, and the future value calculation unit 112 The present value can also be reflected to determine the future value of the resell product. Exemplarily, the present value of the product of the same kind is the initial constant value of Equation 1 (

) Can affect the calculated future value (y(n)).

Future value information on the resell product is provided to the user (S130).

The calculated future value y(n) is calculated as a constant value, and the corresponding value itself may be provided to the user through the user interface unit 130 as future value information. However, the method for evaluating the future value of a resell product according to an embodiment of the present invention predicts the future price of the resell product and provides the predicted future price of the resell product to the user through the user interface unit 130 as future value information. can do.

The future price estimation unit 113 may predict the future price of the resell product described above by using the future value y(n) calculated by the future value calculation unit 112. Here, when the resell product is not released, the future price estimation unit 113 may predict the future price by considering the calculated future value y(n) and the initial sale price of the resell product. In addition, when a resell product is released and a transaction is being performed in the online secondary market, the future price prediction unit 113 considers the calculated future value (y(n)), the initial selling price of the resell product, and the current transaction price. Thus, the future price of the resell product can be predicted.

In other words, the method for evaluating the future value of the reseller product according to the present embodiment determines the future value of the reseller product in consideration of the general consumer's reaction and preference to the reseller product, not the arbitrary price set by the reseller or the current market price. As such, it is possible to protect buyers of resell products, and to further revitalize the resell product trading market by allowing resell products to be traded at a reasonable price.

In addition, in some embodiments, the resell product is an evaluation target resell product requested by a user through the user interface, and the step of evaluating the future value of the requested evaluation target resell product confirms the authenticity of the evaluation target resell product. In addition, it is possible to evaluate the future value of the evaluation target resell product in consideration of the authenticity of the evaluation target resell product. Specifically, the method for evaluating the future value of the resell product according to the present embodiment includes the steps of: determining a current state of the resell product to be evaluated and generating a current state variable of the resell product; And generating an authenticity variable of the resell product by determining whether the resell product to be evaluated is authentic. The above-described steps may be performed by the resell product authenticity determination unit 140. The authenticity result determined by the resell product authenticity determination unit 140 may also act as an independent variable, and the determined result value is provided to the future value evaluation unit 110. The future value evaluation unit 110 may evaluate the future value of the resell product to be evaluated by using the result values (current state variable of the resell product, the authenticity variable) provided from the resell product authenticity determination unit 140 as independent variables. have. Specifically, the future value calculation unit 112 of the future value evaluation unit 110 is based on the independent variable calculated by the text analysis unit 111 and the resell product preference information collected by the resell product preference information collection unit 102. The future value of the resell product to be evaluated can be calculated by further considering the current state variable and the authenticity variable. The future value calculation unit 112 may model the future value y(n) of the resell product through a linear regression equation such as Equation 2 below.

[Equation 2]

: 리셀 상품의 미래 가치,

은 독립 변수로 각각 긍정 독립 변수, 부정 독립 변수, 리셀 상품 선호도 정보에 기초한 선호도 변수, 리셀 상품의 현재 상태 변수, 리셀 상품의 진위 여부 변수에 해당하며,

은 상기 독립 변수의 회귀 계수로서 통계적으로 산출되어 설정되는 상수 값에 해당하며,

은 초기 상수 값에 해당한다)(here,

: Future value of resell products,

Is an independent variable and corresponds to a positive independent variable, a negative independent variable, a preference variable based on resell product preference information, a current state variable of a resell product, and a variable whether the resell product is authentic or not.

Corresponds to a constant value that is statistically calculated and set as a regression coefficient of the independent variable,

Corresponds to the initial constant value)

The future value of the resell product calculated by the future value calculation unit 112 may be determined in consideration of the current state and authenticity of the resell product to be evaluated. In addition, the future value of the resell product calculated by the future value calculation unit 112 is calculated based on not only the positive reactions and negative reactions of general consumers to the reseller product, but also the preference survey for the reseller product. It may be a value calculated through fair value determination, not a price arbitrarily determined by.

In other words, the method for evaluating the future value of the reseller product according to the present embodiment determines the future value of the reseller product in consideration of the general consumer's reaction and preference to the reseller product, not the arbitrary price set by the reseller or the current market price. As such, it is possible to protect buyers of resell products, and to further revitalize the resell product trading market by allowing resell products to be traded at a reasonable price.

In addition, the method for evaluating the future value of the reseller product according to the present embodiment further considers the current state and authenticity of the reseller product to determine the future value of the reseller product. It can help shape the commerce market.

The operation by the method for evaluating the future value of the resell product according to the embodiments described above may be implemented at least partially as a computer program and recorded in a computer-readable recording medium. A program for implementing an operation according to the method for evaluating the future value of a resell product according to the embodiments is recorded, and the computer-readable recording medium includes all types of recording devices in which data that can be read by a computer is stored. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tapes, floppy disks, and optical data storage devices. In addition, the computer-readable recording medium may be distributed over a computer system connected through a network, and computer-readable codes may be stored and executed in a distributed manner. In addition, functional programs, codes, and code segments for implementing this embodiment may be easily understood by those of ordinary skill in the art to which this embodiment belongs.

Although described above with reference to examples, the present invention should not be construed as being limited by these examples or drawings, and those skilled in the art will be able to understand the spirit and scope of the present invention described in the following claims. It will be appreciated that various modifications and changes can be made to the present invention without departing from.

10: Future valuation system for resell products
100: Big data collection unit
101: Resell product inquiry information collection unit
102: Resell product preference information collection unit
103: Resell product image information collection unit
110: Future Valuation Department
111: text analysis unit
112: future value calculation unit
113: future price prediction
120: database unit
130: user interface unit
140: Resell product authenticity determination unit

Claims (10)

A system that evaluates the future value of resell products based on big data,
A big data collection unit for collecting preference information for the resell product and inquiry information for the resell product;
A reseller product authenticity determination unit configured to determine a current state of the resell product to generate a current state variable of the resell product, and to determine whether the resell product is authentic or not to generate an authenticity variable of the resell product;
A future value evaluation unit that generates future value information for the resell product based on the collected data on the resell product; And
Including a user interface unit for providing the future value information of the resell product to the user,
The big data collection unit sets the resell product as an input keyword, performs a search on the server, and collects text data related to the input keyword as inquiry information for the resell product, and the resell product Including a resell product preference information collection unit for collecting preference information for the resell product by performing a preference survey on the consumer,
The future value evaluation unit may perform analysis on text data related to the collected resell product to calculate an independent variable required for future value calculation, an independent variable calculated by the text analysis unit, and the resell product. A system for evaluating a future value of a resell product, including a future value calculation unit that calculates a future value of the resell product based on preference information, a current state variable of the resell product, and a variable whether the resell product is authentic. delete delete The method of claim 1,
The future value evaluation unit,
Further comprising a future price prediction unit that predicts the future price of the resell product by using the future value calculated by the future value calculation unit,
The user interface unit provides a future price of the resell product to the user as the future value information. delete The method of claim 1,
The resell product is a future value evaluation system of a resell product, characterized in that the resell product to be evaluated requested by a user through the user interface. delete The method of claim 1,
The independent variable calculated by the text analysis unit includes a positive independent variable corresponding to a frequency of positive expressions for the resell product and a negative independent variable corresponding to a frequency of negative expression for the resell product,
The future value calculation unit,
A system for evaluating the future value of a resell product, characterized in that the future value of the evaluation target resell product is modeled as shown in Equation 2 below.

[Equation 2]

(here,

: Future value of resell products,

Is an independent variable and corresponds to a positive independent variable, a negative independent variable, a preference variable based on resell product preference information, a current state variable of a resell product, and a variable whether the resell product is authentic or not.

Corresponds to a constant value that is statistically calculated and set as a regression coefficient of the independent variable,

Corresponds to the initial constant value) The method of claim 6,
The big data collection unit further includes a resell product image information collection unit for collecting the image of the resell product for which the evaluation is requested from the server,
The reseller product authenticity determination unit compares the image of the evaluation target resell product with image data collected by the resell product image information collection unit to determine whether the resell product to be evaluated is authentic or not. It is a method of evaluating the future value of reseller products in a system that evaluates the future value of reseller products based on big data.
A first step of collecting preference information for the resell product and inquiry information for the resell product;
A second step of determining a current state of the resell product to generate a current state variable of the resell product, determining whether the resell product is authentic, and generating a variable whether the resell product is authentic;
A third step of generating future value information for the resell product based on the collected data on the resell product; And
Including a fourth step of providing future value information on the resell product to a user,
The first step includes setting the resell product as an input keyword and performing a search on the server, collecting text data related to the input keyword as inquiry information for the resell product, and surveying preferences for the resell product. Comprising the step of performing for consumers to collect preference information for the resell product,
In the third step, an independent variable required for future value calculation by performing analysis on text data related to the collected resell product and an independent variable calculated by analyzing the text data, the resell product A method for evaluating a future value of a resell product, comprising: calculating a future value of the resell product based on preference information for, a current state variable of the resell product, and an authenticity variable of the resell product.

Images