KR20130018381A - Electronic commerce system with inclination analysis displaying techniques based on user input data - Google Patents

Abstract

PURPOSE: A condition analysis electronic commercial transaction system is provided to display information similar with the request data of an information requester. CONSTITUTION: A tendency analysis proximity display server(20) accesses an information request terminal(110) and an information providing terminal. The information request data of an information requester and the information providing data of an information provider are inputted to the tendency analysis proximity display server. A data server(30) stores the information request data of the information requester, the information providing data of the information provider, and classification and analysis data. [Reference numerals] (101) Tendency analysis proximity display system; (110) Information request terminal; (120) Information providing terminal; (13) Network; (20) Tendency analysis proximity display server; (22) Data analysis classification unit; (23) Item similar degree calculation unit; (24) Display distance data calculation unit; (30) Data server; (31) Input data storage unit; (32) Classification analysis data storage unit; (33) Similar degree data storage unit; (34) Display distance storage unit

Description

Condition analysis electronic commerce system applied with propensity analysis proximity display technology {ELECTRONIC COMMERCE SYSTEM WITH INCLINATION ANALYSIS DISPLAYING TECHNIQUES BASED ON USER INPUT DATA}

The present invention relates to an electronic commerce, and more particularly, the sales information most similar to the request data of an information requester who is a purchaser who wants to purchase information is displayed on the terminal screen of the information requester so as to look at it, but provides the nearest sales information. The seller's icon is displayed closest to the buyer's icon, and the condition analysis electronic commerce system is applied to the propensity analysis proximity display technology provided so that the buyer can easily find and purchase the desired product by easily searching the desired sales information.

In general, the traditional commerce of buying and selling goods is paying by visually checking the goods. However, in recent years, information and communication have been developed, and the electronic commerce method of trading by using a network communication means is being used without seeing or selecting things directly by activating Twitter or smart phones.

Such an e-commerce method has become an important commerce method for small businesses because it is easily accessible to beginners in small businesses and commerce. In particular, the initial start-up fund is low because there is no need to have the facilities of the store, and it is more activated because there are no unnecessary elements or obstacles other than commerce.

In regard to such electronic commerce, Korean Patent Application No. 10-2007-0092704 (Efficient e-commerce method and system through open quotation in online market) allows the buyer's quotation to be presented in various categories and the seller participates in response. In general, the seller does not select the information posted by the seller, but the seller responds to the buyer's estimate first.

In addition, the Republic of Korea Patent Application No. 10-2008-0001054 (reverse auction system and method using a communication network) is configured to proceed with the bidding on the basis of the buyer and seller local information by analyzing the information posted by the buyer and seller.

As described above, in addition to the general electronic commerce, various types of electronic commerce are performed. In fact, on the screen of the buyer or the screen of the seller, the product information of the numerous purchases or sales of the other party is displayed. And they participate in buying or selling decisions by looking at each other's conditions.

However, due to the activation of e-commerce, too many sellers and buyers' information is displayed, which is a problem of e-commerce.

In addition, even if various e-commerce methods are proposed, it is difficult for a buyer or a seller to find a counterpart and proceed with a sale.

And not only in e-commerce, but also in general information search of general portals, it is difficult to find the searched contents in the optimal state that the user wants, and it is not easy for the searcher to find the desired material, and sometimes the abandon the search. will be.

[Document 1] Republic of Korea Patent Application No. 10-2007-0092704 [Document 2] Republic of Korea Patent Application No. 10-2008-0001054

The present invention for solving the above problems is to be displayed on the terminal screen of the information requestor so that the information most similar to the request data of the information requester wants the information, the icon of the information provider providing the nearest information is the requester Or it is displayed closest to the reference point for the purpose of making information retrieval easy.

The requestor is preferred by classifying the requestor data and the provided data by propensity items, comparing the actual data of the classified items, extracting the most similar data, and calculating the data on the screen based on the similar data. The information closest to the disposition condition is displayed close to each other.

That is, in the case of commodity trading, the product is analyzed and classified and displayed according to the degree of similarity so that the buyer can easily look at the seller suitable for the condition of the product or the propensity, so that the product can be easily found and purchased.

In addition, when there are many sellers displayed with similar similarity, each item of similarity is displayed in a display group, and buyers can search for sellers belonging to their desired purchase items in an effort to activate e-commerce more. will be.

The present invention for achieving the above object, the information requesting terminal used by the information requester, the information providing terminal used by the information provider, the information requesting terminal of the information requester, the information requesting terminal of the information provider connected to the network and the information requestor And a propensity analysis proximity display server for inputting the information request data of the information provider and the information provision data of the information provider to be stored in the data server, and a data server for storing the data. The propensity analysis proximity display server includes the information request data. By comparing a plurality of information providing data as a reference, information providing data similar to the information request data are discriminated, and the information provider's icon related to the information providing data similar to the information request data is close to the information requestor's icon on the screen of the information requesting terminal. In order to be displayed, Classify the plurality of information providing data according to the company degree, and the UI display data to be displayed on the information requesting terminal 110 is calculated, and the icons of the plurality of information providers based on the icon of the information requestor by the UI display data Although the data of the display distance for displaying the icon of the individual information provider and the icon of the information requester are calculated and calculated from the data of the degree of similarity between the individual information providing data and the information request data, the propensity analysis proximity display server displays the information. UI display data including request data, a plurality of information providing data and data of the display distance are provided to be transmitted to the information requesting terminal of the buyer, and the information requesting terminal receives the information request data, the plurality of information providing data and the UI display data. Icon of multiple information providers centering on the icon of the information requestor on the UI screen Is displayed, but the propensity analysis proximity display system to which the propensity analysis proximity display technology is applied, characterized in that the icons of a plurality of individual information providers are displayed so as to be spaced apart from the icon of the information requestor by the data of the display distance of the UI display data. And condition analysis electronic commerce system.

In addition, the present invention, the purchase request terminal 11 used by the buyer, the sales providing terminal 12 used by the seller, the purchase request terminal 11 of the buyer, the sales providing terminal 12 of the seller and the network 13 A propensity analysis proximity display server 20 which is connected and stores the purchase request data of the purchaser and the sale offer data of the seller and is stored in the data server 30, the purchase request data of the buyer, the sale offer data of the seller, and the purchase request data. And a data server 30 for storing analysis and classified analysis classification data for the sales providing data, the tendency analysis proximity display server 20 includes a plurality of sales providing data based on the purchase request data. And compares the sales offer data similar to the purchase request data, and the icon of the sales provider associated with the sales offer data similar to the purchase request data. In order to be displayed close to the icon of the purchase requester on the screen of the terminal 11, a plurality of sales providing data are classified according to the degree of similarity with the purchase request data, and in the tendency analysis proximity display server 20, the purchase is performed. The UI display data to be displayed on the request terminal 11 is calculated, and the icons of a plurality of sellers are displayed based on the icons of the buyers by the UI display data, but the icons of the individual sellers and the icons of the buyers are displayed. The display distance data is calculated and calculated from the data of the degree of similarity between the individual sales offer data and the purchase request data. In the tendency analysis proximity display server 20, the purchase request data, the plurality of sales offer data, and the display distance UI display data including the data is provided to be transmitted to the purchase request terminal 11 of the buyer, the purchase request terminal ( In 11), the purchase request data, a plurality of sales offer data and UI display data is received from the propensity analysis proximity display server 20, and the icon of the buyer is centered on the UI screen 111 of the purchase request terminal 11. In order to display the icons of a plurality of sellers, the icons of a plurality of individual sellers are displayed so as to be spaced apart from the icons of the buyers by the data of the display distance of the UI display data, and the seller wants the transaction displayed on the purchase request terminal 11. The condition analysis electronic commerce system to which the propensity analysis proximity display technology is applied is characterized in that the purchase selection information selected by the buyer is transmitted to the propensity analysis proximity display server 20 from the purchase request terminal 11.

In addition, the present invention, the tendency analysis proximity display server 20, the purchase request data transmitted from the purchase request terminal 11 and the sales providing terminal 12, and analyzes the input purchase request data, sales providing data, and classified by item It is provided with a data analysis classification unit 22 for analyzing the actual data divided by items and to store the analyzed and classified data in the data server 30, and reads the analyzed and classified purchase request data, sales offer data, Based on the actual data for each item of the purchase request data, the degree of similarity is analyzed for the individual actual data of the corresponding item of the multiple sales offer data, and the similarity to the purchase request data that is the reference for the multiple individual sales offer data. A similarity degree calculation unit 23 for each item is provided so that the degree of data is stored in the data server 30 together with the information of the corresponding sales providing data. The data of the similarity level for each item of the individual offer data is read with respect to the request data, and the data of the display distance calculated from the similarity data for each item of the individual offer data is calculated and stored in the data server 30. Provided is a condition analysis electronic commerce system to which the propensity analysis proximity display technology is applied, characterized in that the display distance data calculation unit 24 is included.

In addition, the data server 30, the data server 30, through the propensity analysis proximity display server 20 input data storage unit for storing the data input from the purchase request terminal 11, the sales providing terminal 12 ( 31), the purchase request data and sales offer data stored in the input data storage unit 31 are analyzed by the propensity analysis proximity display server 20, classified by item, and analyzed, analyzed and classified by actual data of each item. Purchase by the classification analysis data storage unit 32 storing the data, and the tendency analysis proximity display server 20 reading the analysis and classified purchase request data and sales providing data stored in the classification analysis data storage unit 32. Based on the actual data for each item of the requested data, the degree of similarity is analyzed for the individual actual data of the corresponding item of the multiple sales offer data, and the reference is made for the multiple individual sales offer data. The similarity data storage unit 33 stores similarity data about the purchase request data and information of the corresponding sales offer data, and purchase request data stored in the similarity data storage unit 33 of the individual sales offer data. Display distance data storage unit for storing the data of the display distance calculated and calculated from the data of similarity for each item of the respective sales offer data by the propensity analysis proximity display server 20 reading the data of similarity for each item ( Provided is a condition analysis electronic commerce system to which the propensity analysis proximity display technology is applied.

In addition, the present invention provides the UI of the purchase request terminal 11 to the purchase request terminal 11 that receives the data of the display distance of the plurality of sale providing data and the individual sale providing data from the propensity analysis proximity display server 20. The purchase selection information of one seller's icon selected for purchase from among the seller's icons displayed on the screen 111 is transmitted through the propensity analysis proximity display server 20 to receive and receive the purchase request terminal 11 and the sales providing terminal 12. There is provided a commerce processing unit 14 for the sales transaction proceeds between the buyer and the seller of the user, the payment system to perform the payment step during the data processing process between the commerce processing unit 14 and the purchase request terminal (11) (15) is provided, the buyer is provided with a delivery system (16) is provided so that the goods sold through the purchase request terminal 11 is delivered to the buyer request The analysis of the trend analysis-up display technology, characterized in that the conditions applied e-commerce system is provided.

In addition, in the present invention, the input data including information of the purchase request data or the sale providing data input by the buyer or the seller is the tendency analysis proximity display server 20 in the purchase request terminal 11 or the sales providing terminal 12. Purchase sales information input step (S01) to be transmitted to and stored in the data server 30; The propensity analysis proximity display server 20 analyzes the purchase request data or the sales offer data stored in the data server 30, and the purchase request data or the sales request data as detailed data of the classification data stored in the data server 30. Purchasing sales information analysis classification step (S02); In the propensity analysis proximity display server 20, the purchase and purchase analysis data classified and stored in the data server 30 and the sale providing data are read by the purchase and sale information analysis classification step, and actual data for each item of the purchase request data is read. Based on the analysis, the degree of similarity is analyzed for the individual actual data of the corresponding item of the multiple sales offer data, and the similarity data for the purchase request data that is the reference for the multiple individual sales offer data is included in the relevant sales offer data. Item-specific similarity calculation step (S03) to be stored in the data server 30 together with the information; The propensity analysis proximity display server 20 reads the data of similarity for each item of the individual sales providing data with respect to the purchase request data stored in the data server 30, and the data of similarity for each item of the individual sales providing data. A display distance data calculation step (S04) for storing the data of the display distance calculated and calculated from the data server 30; In the propensity analysis proximity display server 20, UI display data displayed on the UI screen of the purchase request terminal 11 is calculated, and is individually based on the buyer's icon according to the display distance data included in the UI display data. The seller's icon is displayed, and UI display data including purchase request data, a plurality of sales providing data, and data of the display distance are transmitted from the tendency analysis proximity display server 20 to the purchase request terminal 11 of the buyer. Display data transmission step (S05); In the purchase request terminal 11, the purchase request data, a plurality of sales providing data and the UI display data from the propensity analysis proximity display server 20, received on the UI screen 111 of the purchase request terminal 11 A propensity display step (S06) in which a plurality of seller's icons are displayed based on a buyer's icon, and the icons of a plurality of individual sellers are displayed to be spaced apart from the buyer's icon by data of the display distance of the UI display data; The buyer selects an icon of a seller who wants a transaction displayed on the purchase request terminal 11, and a tendency analysis proximity display technology is applied, characterized in that a purchase step (S07) including a purchase procedure and a commerce is included is provided. Condition analysis An electronic commerce method is provided.

Accordingly, in the present invention, each item of the request data or the provided data is characterized in that the item consists of at least one item of the period of use, price, location, manufacturer, product model name, sales period, trade-off price, transaction method A condition analysis electronic commerce method using proximity display technology is provided.

Furthermore, the present invention provides the UI of the purchase request terminal 11 with respect to the offer data for which the data value of the display distance is calculated equally or similarly among the data of the display distance calculated by the display distance data calculation step S04. The distance between the icon of the buyer and the icon of the seller displayed on the screen 111 is processed according to the data of the display distance, and the position at which the icon of the seller is displayed is a radius of the data value of the display distance and is represented by the rectangular coordinate value of the position. It is processed to be calculated by the rectangular coordinate calculation method or the group distribution method, the rectangular coordinate calculation method is a data value of the display distance to the radius (r), and the one-sided coordinate value (x) of the displayed rectangular coordinates is determined randomly. , Another coordinate value (y)

y ^ 2 = r ^ 2-x ^ 2

And the group distribution method is configured to display a plurality of sales providing data in which the data values of the display distances are calculated with the same or similar data values, and are displayed in groups for each item. A condition analysis electronic commerce method is applied.

According to the present invention configured as described above, the information most similar to the request data of the information requester who wants the information is displayed to look good, and the icon of the information provider providing the closest information is displayed closest to the requester or the reference point to search for information. There is an excellent effect that makes this easy.

In particular, the requestor prefers the data by providing the requestor's data and the provided data by categorizing the propensity items, comparing the actual data of the classified items, extracting the most similar data, and calculating the data on the screen based on the similar data. The information closest to the propensity condition to be displayed is displayed close.

In addition, when applied to the sale of goods, the buyers can easily find the desired product when purchasing a product by analyzing and classifying and displaying it according to the degree of similarity so that the buyer can easily look for the seller that meets the requirements of the product or propensity. .

In addition, when there are many sellers displayed with similar similarity, each item of similarity is displayed in a display group, and buyers can search for sellers belonging to their desired purchase items in an effort to activate e-commerce more. will be.

1 is a schematic control block diagram of a condition analysis electronic commerce system according to the present invention.
2 is a schematic control block diagram of an example of information request and information provision in the condition analysis electronic commerce system according to the present invention.
3 is a flow chart of a schematic commerce method for the condition analysis electronic commerce system according to the present invention.
4 is an example of a reference index table for calculating a degree of similarity for real data of each item in the condition analysis electronic commerce system according to the present invention.
5 is an example table for purchase request data in the condition analysis electronic commerce system according to the present invention.
6 illustrates index calculation data of an example purchase request data according to a reference index table in the condition analysis electronic commerce system according to the present invention.
7 is an exemplary flowchart for input processing of purchase request data in the condition analysis electronic commerce system according to the present invention.
8 is an exemplary view illustrating a comparison process between a reference index value and an input value in the condition analysis electronic commerce system according to the present invention.
9 is an example table for sales offer data in the condition analysis electronic commerce system according to the present invention.
FIG. 10 illustrates index calculation data of exemplary sales providing data according to a reference index table in the condition analysis electronic commerce system according to the present invention.
11 is a diagram illustrating an index value comparison between purchase request data and sales offer data calculated through an exemplary reference index in the condition analysis electronic commerce system according to the present invention.
12 is an example table for data values of similarity in sales offer data calculated through an example reference index in the condition analysis e-commerce system according to the present invention.
FIG. 13 is a table illustrating a calculated data of display distances for respective sellers through similarity data in sales offer data calculated through an exemplary reference index in the condition analysis electronic commerce system according to the present invention.
14 is a diagram illustrating a UI screen of a purchase request terminal according to an embodiment in which an icon of a plurality of sellers calculated through an example reference index is displayed in the condition analysis electronic commerce system according to the present invention.
15 is an exemplary diagram of a reference coordinate of the propensity display displayed on the UI screen of the purchase request terminal in the condition analysis electronic commerce system according to the present invention.
16 is an exemplary view for explaining the calculation of the coordinate value of the tendency indication in the condition analysis electronic commerce system according to the present invention.
17 is an exemplary diagram of coordinates displayed by the coordinate value calculation method of the propensity indication in the condition analysis electronic commerce system according to the present invention.
18 is a table showing an example of the display level according to the display distance of the tendency indication in the condition analysis electronic commerce system according to the present invention.
FIG. 19 is an exemplary table for sellers in an embodiment divided into an exemplary table of level indications of propensity in the condition analysis electronic commerce system according to the present invention.
20 is an exemplary view in which the coordinate region is divided and shown as an example of the level of inclination indication in the condition analysis electronic commerce system according to the present invention.
FIG. 21 is an exemplary view of coordinates in which icons of a plurality of sellers are displayed in an exemplary manner of level display in the condition analysis electronic commerce system according to the present invention.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

That is, the condition analysis electronic commerce system 100 and the propensity analysis proximity display system 101 to which the propensity analysis proximity display technology according to the present invention is applied are as shown in FIGS. 1 to 21, the information requesting terminal 110. Alternatively, with the purchase request terminal 11, an input terminal is provided as the information providing terminal 120 or the sales providing terminal 12, etc., and the propensity analysis proximity connected to the input terminal and the network 13 or the wired / wireless internet or the like. The display server 20 is included.

In addition, the data server 30, the transaction processing unit 14, the payment system 15, the delivery system 16, etc., which are connected thereto are provided together, and the information requester requests the information through the information requesting terminal 110. Information provided from the information providing terminal 120 connected to the analysis proximity display server 20 and the propensity analysis proximity display system 101 is transmitted to the information requesting terminal 110 through the information analysis proximity display server 20 to request information. One gets the information he wants.

In more detail, when such information corresponds to purchase and sale, the information request terminal 110 may be a purchase request terminal 11, and the information providing terminal 120 may be a sales providing terminal 12. Therefore, when the buyer transmits the request information for purchase through the purchase request terminal 11 to the propensity analysis proximity display server 20, the sales offer data of the appropriate seller is provided to the purchase request terminal 11, so Condition analysis is made of the electronic commerce system 100 is operated.

Among the data of the seller (information provider) desired by the buyer (information requester), the information of the seller (information provider) extracted on the basis of the data entered by the buyer (information requester) is given priority. Condition analysis e-commerce to be seen by the user, which extracts only the information of the seller (information provider) analyzed according to the propensity of the buyer (information requester) and makes it visible to the buyer (information requester). It is configured to increase the efficiency (or the efficiency of information retrieval or information provision).

That is, in the present invention, the information requesting terminal 110, the information providing terminal 120, the propensity analysis proximity display server 20, the data server 30, and the like, the information requesting terminal 110 by these configurations Analyze the data requested or provided by the client, such as the information requester, the information provider using the information providing terminal 120, and categorized by propensity (by item), and classified by these items (by propensity) After extracting similar items by item (propensity) from the information providers based on the propensity data of the information requester, the similar information providing data by propensity (item by item) are transmitted to the information requesting terminal 110. By doing so, the information requester is configured to easily check the information providing data closest to the desired information.

Examples of such information types or items (propensity) include various kinds of items such as occupational propensity, regional propensity, hobby propensity, sports propensity, special propensity, interest propensity, alumni propensity, hometown propensity, social club propensity, purchase or sales propensity, etc. The propensity of the data may be the target.

In addition, each of the propensity (item) classifications may be divided into more subdivided categories of inclinations, for example, in the case of occupational classification, it may be referred to as 'vocational interest classification' as an alternative concern. ',' Public Service Classification ',' Information & Communication Classification ',' IT Occupational Classification ',' Sales Business Classification ',' Legal Legal Classification ',' Academic Classification ',' Manufacturing Industry Classification ',' Trade Classification ',' Telecommunications Sales Classification ' It may be divided into such regions, and in the case of regions, they may be categorized by each country, or may be divided into a tendency classification classified by individual regions in one country.

In the present invention, members named as '~ part', '~ processing part', '~ server', or '~ terminal', '~ system', '~ calculation part', etc. are physically handled for data processing. It refers to a unit that processes a specific function or operation, such as a component configuration, a configuration of a processing apparatus, a configuration of logical processing, a configuration of a processor, a configuration of a control flow, a single server, a plurality of servers, a partition server, and the like. It may be provided and implemented in a variety of configurations, such as configuration, software configuration, or a combination of hardware and software configuration, and is not limited to any one form, which is a common electronic information and communication It can be seen that it is a configuration that can be applied, operated and implemented within general technical details.

Accordingly, in the condition analysis electronic commerce system 100 to which the propensity analysis proximity display technology according to the present invention is applied, a propensity analysis proximity display system 101 for implementing the propensity analysis proximity display technology is configured. The transaction analysis unit 14, the payment system 15, the delivery system 16, etc. are further added to the 101 to sell, deliver, and settle the goods, thereby forming the condition analysis e-commerce system 100 for commerce. It is done.

That is, the propensity analysis proximity display technology configured in the present invention analyzes the input information requestor's information request data, the information provider's information providing data, etc., and classifies them into items for each propensity, and then displays them so that the information requester can easily distinguish them. The main technique is to make it possible. Thus, it is called 'propensity analysis proximity display technology' by applying the technology that analyzes and classifies the input data into items classified by propensities, and displays the data classified by the analyzed propensities so that they can be easily viewed. And the propensity analysis proximity display technology is carried out through the propensity analysis proximity display system 101, the configuration of the propensity analysis proximity display system 101, the information request terminal 101 (or purchase request terminal 11). And a propensity analysis proximity display server 20, a data server 30, etc., which are provided to transmit and receive data with the information providing terminal 120 (or the sales providing terminal 12).

As an example, as shown in Figure 1, 2, etc., the information requesting terminal 110 used by the information requester, the information providing terminal 120 used by the information provider, the information requesting terminal 110 of the information requester, Propensity analysis proximity display server 20 connected to the information provider terminal 120 and the network 13 of the information provider and allowing the information request data of the information requester and the information provider data of the information provider to be input and stored in the data server 30. And a data server 30 for storing the analysis classification data analyzed and classified for the information request data of the information requester, the information provision data of the information provider, the information request data and the information provision data, and the like.

In this regard, the information requesting terminal 110 and the information providing terminal 120, etc. are connected to the operating system or the operation server in the present invention, or a network 13 (generally composed of an Internet connection with a wired or wireless network). It may be configured as a terminal.

In addition, the information requester using the information request terminal 110 may be a user who wants specific information and a user who wants to obtain information of various tendencies described above. In addition, the information provider using the information providing terminal 120 is also to provide a specific information, a person who can provide the information of the various tendencies described above, may be a general individual user, or specialize in providing information This could be an individual business, a general information provider, a library, a patent information source, or an individual or user organization that uses client terminals such as public and government agencies.

In particular, the technology according to the present invention can be applied as an electronic commerce system for the sale of goods, when applied to such an electronic commerce system, the information requestor, information request terminal 110, etc., the buyer wants to purchase the goods, the purchase request terminal ( 11) and so on. In addition, the information provider, the information providing terminal 120, etc. may be composed of a seller selling the goods, a sales providing terminal 12 and the like.

However, the present invention is not limited thereto, and when the information requester is designated as a specific single buyer, the information provider may also include a plurality of other buyers who are similar in purchase classification to the buyer. Naturally, many other sellers could also be informants.

In addition to this, the sellers may devise a sales strategy to directly find and request a purchaser having a proximity tendency for their own goods. Such a seller's propensity to sell when a particular seller is requested as an information requester The sellers may want to see the same category and other sellers with similar data in the category, and the seller may be an informant. And of course, if one particular seller to be an information requester, it is natural that a plurality of buyers can be implemented as an information provider.

As described above, the information provider, the information requestor, etc. in the propensity analysis proximity display system 101 to which the propensity analysis proximity display technology according to the present invention is applied and the condition analysis electronic commerce system 100 to which the propensity analysis proximity display technology is applied must be identified. The present invention is not limited to those who provide related information, but the present invention can be practiced so that a person who provides information similar to his or her own can be analyzed and displayed on his or her monitor screen in close proximity. Various applications may be applied to the classification scheme, and all user clients accessing the configuration server of the present invention may be information requesters, and other information requestors may be information providers themselves.

For example, when a specific user 1 searches the information of the 'national library location' through the Internet, another general individual user 2 may provide the information of the national library location. In addition, a user3 who is a management server of the National Library may be an information provider. If other public institutions or general corporate data reveal information about the location of the National Library, they may also be users4.

In addition to user 1, there may be other individual users 5 who want to search the National Library location.

When the contents of finding the 'national library location' are conducted as information request data, user 1 will correspond to the information requester, and the terminal used by the user 1 will be the information request terminal.

The information provider may be an information provider such as another general individual user 2, a user 3 serving as a management server of the National Library, a user 4 of another public institution, a general enterprise, or the like. Could be.

Furthermore, in addition to user 1, another individual user 5 requesting the information may be another information provider using the information of the 'information request' of the 'national library location' as the information providing data. The terminal may also be applied to the information providing terminal.

Therefore, according to the information requester's information request, a plurality of information providers' icons will be displayed on the terminal screen of the information requester, and after selecting the icons of the plurality of information providers and looking at the brief contents, the icons of the information requesters select the desired information providers. If the data provided by the information provider will be shown on the screen of the information requester's terminal.

As such, when such propensity analysis proximity display technology is applied to the field of electronic commerce, when a particular buyer is requested as an information requester, other buyers who have similar tendencies as other categories with other sellers may also become information providers. Thus, the propensity analysis proximity display system 101 may be displayed on the purchase request terminal 11 used by the purchaser together with a plurality of other buyers.

In the e-commerce system described below, one purchaser is used as an information requester, and the information provider is shown by using only a plurality of sellers as examples. For other information providers, the following embodiments are applied by applying the following. Of course, it can be configured.

Similarly, when one seller is implemented as an information requester, a plurality of buyers may be implemented as a plurality of other sellers as information providers, and the configuration of such an embodiment may be configured by applying the embodiments described below. .

In the request for information and the provision of information other than the electronic commerce, it is natural that detailed configurations of the electronic commerce system described below may be applied to the specific configurations.

In the propensity analysis proximity display system 101 to which the propensity analysis proximity display technology according to the present invention is applied, the propensity analysis proximity display server 20 selects a plurality of pieces of information providing data based on the inputted information request data. By comparison, information providing data similar to the information request data are determined.

In addition, in order for the icon of the information provider related to the information providing data similar to the information request data to be displayed in close proximity to the icon of the information requestor on the screen of the information requesting terminal 110, the number of the information providers according to the similarity with the information requesting data. Classify informational data.

The meanings of "similarity" and "similarity" described throughout the present invention coincide with some words (letters) of the information request data or the buyer's request data or have words (letters) that are similarly classified in the relevant classification. This includes informational data or seller's data. Thus, the term 'similarity' means that the words (letters) have a matching word (letter) or many similar words.

In addition, the meaning of the information request data and the "similar" information providing data refers to a case where the information request data and the actual data of the information providing data correspond to or include some similar contents in the relevant classification. Of course, this will also apply to purchase request data and sales offer data. In addition, analyzing or calculating the degree of similarity refers to analyzing or calculating a case where a large number of words or letters coincide between both data or a large amount of matches in related fields.

As described above, the propensity analysis proximity display server 20 is configured to calculate UI display data to be displayed on the information requesting terminal 110. Based on the UI display data, a plurality of information providers The icon is displayed.

Accordingly, the data of the display distance for displaying the icon of the individual information provider and the icon of the information requester is calculated and calculated from the data of the degree of similarity between the individual information providing data and the information request data. In other words, when viewed based on the information requestor as the reference of the display, the icons of a plurality of information providers to be displayed are displayed separately from each other by the size of the data of the display distance. In general, the displayed data is not displayed with a unit of a general distance standard such as m (meter), but has a relative display distance shown among a plurality of information providers. For example, when the display distance from the information requester's icon that is the nearest information provider's icon is a reference distance of '1', the acons of other information providers are separated by the magnification of the nearest reference distance. It will be displayed in relative distance as if it has fallen.

In addition, the data of the display distance displayed at this time may be calculated by calculating the data of the display distance according to the degree of similarity between the actual data corresponding to the individual items between the information providing data and the information request data. As a result of comparing the actual data, the information providing data is displayed closely according to the degree of similarity between the information request data and the actual data. An example of the calculation method for this will be described later.

Therefore, the icon of the information provider of the information providing data including the information request data and the most actual data may be displayed closest to the information requestor icon.

After the data of the display distance is calculated as described above, the propensity analysis proximity display server 20, the UI display data including the information request data, a plurality of information providing data and the data of the display distance of the information request terminal 110 of the buyer It is to be transmitted to.

Accordingly, the information requesting terminal 110 receives information request data, a plurality of information providing data, and UI display data from the tendency analysis proximity display server 20, and displays the UI screen 110 'of the information requesting terminal 110. ), A plurality of information providers icons are displayed based on the information requestor icons.

In particular, as described above, the icons of a plurality of individual information providers are displayed to be spaced apart from the icons of the information requestors by the data of the display distance of the UI display data.

Therefore, the icon of the information provider having the information providing data most similar to the information request data desired by the information requester is displayed closest to the icon of the information requester, so that the information requester can display the UI screen 110 'of his information requesting terminal 110. You can easily check the details by selecting the icon of the nearest displayed information provider.

Thus, by looking at the information provided by a plurality of information providers in order to include more information that the information requester, the effort and inconvenience to see unnecessary information other than the desired information is saved.

Therefore, it is easy to check only the information they want to proceed more active information search activities.

As described above, with respect to the propensity analysis proximity display technology through the propensity analysis proximity display system 101, the information request data of the information requester and the information providing data of the information provider include general knowledge search information, location search information, and people. It may be applied to search of various information such as search and branch search information.

In addition, as one of the more preferred embodiments, the present invention may be applied to an electronic commerce system for product purchase and sale.

In this case, the information requester may be a buyer of an individual (one company) or a seller of an individual (one company).

In the case of becoming a requester of information as a buyer, a number of sellers in close proximity with desired purchase information or a plurality of other buyers having similar purchase information may be implemented as information providers.

Similarly, if the seller is an information requester, it may be implemented as an information provider with a large number of buyers similar to the sales information, or a plurality of other sellers having similar sales information.

Therefore, the following description shows an embodiment composed of only one buyer (information requester) and a plurality of sellers (information provider), so that the technical description of the present invention is better, but such one purchaser and a plurality of sellers each have an information requester and information. Not only is it limited to a provider, but it is natural that the configurations can be similarly applied to the various cases described above.

First, the purchase request terminal 11 used by the buyer corresponding to the information requester will be provided. In addition, the sales providing terminal 12 used by the seller corresponding to the information provider will be provided.

The buyer's purchase request terminal 11, the seller's sales providing terminal 12 and the network 13 is connected to the propensity to enter the buyer's purchase request data and the seller's sales offer data is stored in the data server 30 The analysis proximity display server 20 is provided.

Also included is a data server 30 which stores analysis classification data analyzed and classified about the purchase request data of the buyer, the sales offer data of the seller, the purchase request data and the sales offer data, and the propensity analysis approach to the propensity analysis proximity display technology. The display system 101 is provided.

In the present invention provided as described above, the tendency analysis proximity display server 20 compares a plurality of sales offer data based on the purchase request data to determine sales offer data similar to the purchase request data, and is similar to the purchase request data. In order to display the icons of the sales provider related to the sales offer data in close proximity to the icons of the purchase requesters on the screen of the purchase request terminal 11, the plurality of sales offer data are classified according to similarity with the purchase request data. .

In the propensity analysis proximity display server 20, UI display data for displaying on the purchase request terminal 11 is calculated. The UI display data is used to calculate a plurality of sellers based on the icon of the buyer. The icon is displayed.

Accordingly, the data of the display distance for displaying the icon of the individual seller and the icon of the buyer is calculated and calculated from data of similarity between the individual sales offer data and the purchase request data.

In the propensity analysis proximity display server 20 prepared as described above, UI display data including purchase request data, a plurality of sales providing data, and data of a display distance is transmitted to the purchase request terminal 11 of the buyer.

In the purchase request terminal 11, the purchase request data, a plurality of sales providing data and UI display data are received from the tendency analysis proximity display server 20, and the UI screen 111 of the purchase request terminal 11 is received. The icons of the plurality of sellers are displayed on the basis of the icons of the buyers, but the icons of the plurality of individual sellers are spaced apart from the icons of the buyers by the display distance data of the UI display data.

Then, when clicking or selecting the icon of the seller displayed on the purchase request terminal 11 in turn, the sales information provided by the seller is displayed in detail on the purchase request terminal 11. Thereafter, when the buyer selects an icon of a seller who wants to trade among a plurality of sellers, the purchase selection information of the seller selected by the buyer is transmitted from the purchase request terminal 11 to the propensity analysis proximity display server 20. The condition analysis electronic commerce system 100 to which the analysis proximity display technology is applied is prepared.

Looking at the detailed configuration of the tendency analysis proximity display server 20 is provided as described above may be prepared as follows.

First of all, the purchase request data transmitted from the purchase request terminal 11 and the sales providing terminal 12 are analyzed, and the inputted purchase request data and the sales offer data are classified by item, and the actual data classified by the item are analyzed and analyzed and classified data are The data analysis classification unit 22 to be stored in the data server 30 is provided.

Accordingly, the purchase request data and the sales offer data are input between the purchase request terminal 11, the sales providing terminal 12, and the propensity analysis proximity display server 20 connected through a network 13 such as a wired or wireless Internet. Are stored in. After that, each stored data is read by the data analysis classification unit 22 and analyzed for each state and classified by item.

Next, the purchase request data and the sales offer data which are analyzed and classified are read, and the similarity is analyzed for the individual actual data of the corresponding items of the multiple sales offer data based on the actual data of each item of the purchase request data. Similarity calculation unit 23 for each item is provided so that the data of the degree of similarity with respect to the purchase request data that is the reference to the individual sales offer data of the data is stored in the data server 30 together with the information of the sales offer data. .

That is, examples of individual items for the purchase request data and the sale providing data analyzed by the data analysis and classification unit 22 and classified for each item may be provided as shown in FIGS. 4, 5, and 9. Therefore, the classification by item will classify the actual data by each item with the analyzed data as in the example of the accompanying drawings.

Each of the data classified and analyzed as described above is determined by the item similarity calculating unit 23 to determine the similarity with respect to the actual data, and the data values of the similarity and the degree of similarity are calculated.

In this regard, 'similarity' refers to how similar the actual data is in the individual items of the individual sales request data that are similar to each other, based on the actual data in the individual items of the purchase request data that are the criteria. .

In this example of similarity, the degree of similarity will be determined by the difference of numerical values when the actual data of each item is composed of numerical values. For example, it can be used as 'period', 'price', 'model series number', 'sale period', 'negotiated price'.

If the actual data for each item is about a location such as a sales location, a purchase location, a waypoint, and the like, the degree of similarity may be determined by data of a distance calculated from a location coordinate value.

In addition, when the actual data for each item is a letter or a number, it may be determined in ascending or descending order, or from the values of the index table given by the user as an index value as shown in FIG. Similarity data may be produced.

In the following description, an embodiment of calculating the index table of FIG. 4 as reference data is shown.

As described above, similarity data for each item for a plurality of individual sales request data is calculated based on the purchase request data, which is a reference, and then a display distance is calculated.

That is, the data of the similarity level for each item of the individual sales offer data is read with respect to the purchase request data, and the data of the displayed distance calculated from the data of the similarity level for each item of the individual sales offer data is calculated and stored in the data server 30. The display distance data calculator 24 is provided.

The display distance data calculation unit 24 calculates the display distance data to be displayed on the UI screen 111 of the purchase request terminal 11. It is calculated and calculated from the data of similarity calculated by (23).

In the example of calculating the data of the display distance, if the data items of similarity are united, the data of similarity is calculated as the data of the display distance, and the data of the display distance of the icon of the corresponding provider is calculated. Will be calculated.

On the other hand, in the case of an example consisting of a plurality of similarity data items, the similarity data values for the respective items are calculated and calculated as data of the display distance of the icon of the corresponding sales provider.

Of course, when the display distance data is calculated from a plurality of similarity data values for the seller, a value obtained by simply adding data values of similarity degree as the data value of the display distance is calculated as shown in the description of the following description. You might be able to compute it if you can.

However, it can be applied only by such a simple summation method, while it can be calculated by multiplying or dividing a specific item, or a subtraction operation can be calculated between similarity data of some items that are not summation. will be.

In addition, the similarity data of a specific item may be weighted to the calculated similarity data value, so that the data of the display distance may be calculated in a state of being weighted or subtracted from other similarity data values.

As for the method of calculating the display distance data from the similarity data for individual items, the information on the type of propensity, the information on the individual data of the information provider or the information request, and the preference of the item requested by the information requester or seller The calculation method for the data of the display distance can be applied to suit various cases such as the weighting.

The method of calculating similarity data for individual items for a plurality of seller data is shown as an example based on the difference between individual index values calculated based on the index table value of FIG. 4 attached thereto. It is shown as an embodiment that the data of the display distance in the seller data is calculated and calculated by summing similarity data calculated for each item from the calculated seller data.

Therefore, it is natural that the individual components according to the calculation of the similarity data and the display distance data calculated as an example of such a method may be applied and inferred to other calculation methods.

In addition, in the condition analysis electronic commerce system 100 and the propensity analysis proximity display system 101 according to the present invention, the data server 30, the purchase request terminal 11 through the propensity analysis proximity display server 20; ), The input data storage unit 31 storing the data inputted from the sales providing terminal 12, the purchase request data and the sales providing data stored in the input data storage unit 31; The classification analysis data storage unit 32 for analyzing, classifying by item, analyzing actual data for each item, and storing the analyzed and classified data will be configured.

In addition, the data server 30, for each item of the purchase request data by the propensity analysis proximity display server 20 reading the analysis and classified purchase request data stored in the classification analysis data storage unit 32, sales offer data Based on the actual data, the degree of similarity is analyzed for the individual actual data of the corresponding item of the multiple sales offer data, and the similarity data and the corresponding sales offer for the purchase request data that are the reference for the multiple individual sales offer data. A similarity data storage 33 for storing information of data will be included. In the similarity data storage unit 33 is the data of the similarity level for each item associated with a plurality of sales offer data.

The similarity for each item of the individual sale providing data is read by the propensity analysis proximity display server 20 which reads data of similarity level for each item of the individual sale providing data with respect to the purchase request data stored in the similarity data storage unit 33. The display distance data storage unit 34, which stores the data of the display distance calculated and calculated from the data of the degree, is included.

The calculated and stored display distance data is transmitted to the purchase request terminal 11 of the buyer together with the UI display data, and is transmitted to the UI screen 111 of the purchase request terminal 11 of the buyer by the transmitted UI display data. Based on the buyer's icon, the icons of the multiple sellers will be displayed according to the data of the respective display distances.

The data of the display distance is calculated from the similarity data for each item of the seller, so that the icon of each seller corresponds to the buyer's icon based on the degree to which the seller's sales information data and the buyer's purchase offer data are similar. The seller's icon is displayed in close proximity.

Therefore, when a buyer selects a seller icon in which a seller's icon that has a lot of data desired by the user is displayed close to his icon, the seller's information will be displayed in detail as a sale offer content as shown in FIG. 9. In this way, the buyer selects the desired buyer from among the seller's information selected and checked by the buyer, and then continues to carry out the transaction process.

In this way, the buyer can select the seller icon that is displayed closely from the buyer's own icon because the seller is displayed close to the user's own icon. It is easy to look at, so buyers can easily find and buy the products they prefer.

In this way, the buyer can easily select the seller that includes the most preferred condition of the product he wants to check the details, and in this process, the buyer will send the information of the purchase request for the product he wants.

That is, the UI screen 111 of the purchase request terminal 11 with respect to the purchase request terminal 11 that receives the data of the display distance of the plurality of sale providing data and the individual sale providing data from the tendency analysis proximity display server 20. The seller's icon displayed in close proximity to the buyer's icon displayed on the seller's icon in turn may be selected, and the seller having the seller's desired sales information is selected from the seller's sales information as shown in FIG. 9. As such, the purchase selection information in which the buyer selects one seller for purchase is transmitted through the propensity analysis proximity display server 20 to receive and receive information between the purchaser and the seller who are users of the purchase request terminal 11 and the sales providing terminal 12. It is provided with a commerce processing unit 14 to proceed the sales transaction.

This is a trading transaction. In addition, the payment method is made in a variety of transactions, the payment system 15 is provided to perform the payment step during the data processing process between the commerce processing unit 14 and the purchase request terminal (11).

In addition, a condition analysis electronic commerce system 100 may be configured to include a delivery system 16 for allowing a buyer to deliver a product traded through a purchase request terminal 11 as a buyer request.

Hereinafter, the electronic commerce method by the propensity analysis proximity display system 101 and the condition analysis electronic commerce system 100 to which the propensity analysis proximity display technology according to the present invention described above will be described.

Of course, the condition analysis e-commerce method through the condition analysis e-commerce system will be described with reference to the accompanying drawings for the purchase and sale of goods, the embodiment of the present invention can be applied and applied to other tendency classification. There will be.

The condition analysis electronic commerce method to which the propensity analysis proximity display technology applied according to the present invention provided as described above is constituted by the propensity analysis proximity display system 101 and the condition analysis electronic commerce system 100 to which the propensity analysis proximity display technology is applied. Will be.

Accordingly, as an information requester, a buyer inputs purchase request data using the purchase request terminal 11, and as an information provider, the seller inputs sales provision data using the sales provision terminal 12, and the input data is a network. Through 13, the propensity analysis proximity display server 20 of the propensity analysis proximity display system 101 is transmitted.

That is, in the condition analysis electronic commerce method according to the present invention, the input data including the information of the purchase request data or the sales providing data input by the buyer or the seller is inclined in the purchase request terminal 11 or the sales providing terminal 12. The purchase and sale information input step S01 to be transmitted to the analysis proximity display server 20 and stored in the data server 30 will be performed.

Based on the input data, the propensity analysis proximity display server 20 analyzes the input data, classifies the analyzed data according to the classification items divided by the propensity, and analyzes the condition for the classified data. The proximity display step will be performed.

That is, based on the purchase request data of the buyer, the sales offer data having the condition of the tendency close to the purchase request data is discriminated among the sales offer data of the multiple sellers, and the similarity which is the most similarly close tendency is discerned. do. As described above, the similarity degree of each item is calculated among similarly provided sales offer data to calculate display distance data related to the sales offer data of the corresponding seller.

In such a processing configuration, the purchase request data of the purchaser, the sales offer data of the seller, etc., have a condition of purchase propensity or sales propensity in the purchase or sale, and the purchase condition and the sales condition may be a single item. It may also have multiple condition items.

Therefore, it is possible to extract the seller's propensity to sell (the sales condition) that has the most similar contents to the purchase conditions of the purchase propensity desired by the buyer so that the buyer can see it well. In order to achieve this, a seller having sales offer data having a similarity to a purchase tendency (purchase condition) of the purchase request data is extracted.

In this invention, the degree of similarity refers to the degree of similarity between the actual data of the individual item of the purchase propensity (purchase condition) of the buyer and the actual data of the corresponding item of the seller's offer data. For example, the degree of similarity may be determined by a numerical value having a sequential order, in the case of a consonant letter in the descending or ascending order. Or when there is a matching word (letter) by comparing both data, a similar word (letter) or classification content in a related classification, or an index value for each item is given in advance as shown in the example below. The similarity level may be determined by comparing index values for each item of the sales offer data.

In addition, the data of the display distance in the present invention refers to the display related to the distance at which the icon of the individual seller is displayed when the user's icon actually displayed on the UI screen 111 of the purchase request terminal 11 of the buyer is actually displayed. Refers to distance data. The data of the display distance is calculated from data of similarity in one or a plurality of items in the sales offer data of a plurality of sellers to calculate the data of the display distance.

In particular, in one sale offer data, the data of the display distance inherent in the sale offer data is calculated by calculating from the similarity data of various items of the sale offer data.

By going through the process of calculating the data of the unique display distance in the sales offer data of the corresponding seller will have the data of the individual unique display distance associated with the sales offer data of each of a plurality of individual sellers.

Of these, when the relative size of the display distance of the icon of the nearest seller through the display distance data is set as the reference display distance, the data of the display distance of the other sellers and the display distance of the nearest seller are compared. The relative display distance calculated by the magnification of the reference display distance may be calculated.

As such, by comparing and analyzing the data input by the buyer and the seller, the icon of the seller matching or approaching the condition of the propensity desired by the buyer is displayed close to the buyer's screen. The UI screen 111 of the purchase request terminal 11 is displayed. By selecting the icon of the seller displayed in close proximity to the icon of the buyer displayed near the center of the buyer can easily purchase the desired item.

As such, the first condition for the transaction between the buyer and the seller is that the locations of the relevant clients are displayed on the screen of the buyer (or vice versa). In a particularly preferred description, the information requester is described as a buyer, but if the seller and the buyer are interchanged, they can be applied in reverse. Therefore, whether a user using the system according to the present invention is a buyer or a seller, it is possible to easily check all the displayed position of the other clients, buyer-> seller, or buyer-> other buyer, seller-> other seller, As in Seller-> Buyer, you can make a deal by finding reasonable prices while modifying the terms of each other.

And all the clients of people who want to buy on the seller's screen, the location of the other sellers should be displayed. In addition, the icon of other buyers may be displayed together with the icons of people to sell on the buyer's screen. That is, the client of the icon displayed centering on the icon of the buyer may be a seller, and further, the other buyer may also be a client of the displayed icon. Therefore, you can use it well while looking at the purchase conditions and sales conditions.

In the present invention as described above, the buyer checks the information of a plurality of sellers he / she wants to purchase with the UI screen 111, and then looks at the example of purchasing a product.

First, in the purchase sales information input step (S01), the purchase request data entered by the buyer through the buyer terminal 11 will be transmitted to the propensity analysis proximity display server 20 and stored in the data server 30.

An example of such purchase request data may be seen as an example of purchasing a computer product as shown in FIG. 5.

In addition, even in a plurality of sellers, as shown in FIG. 9, the sales provision data may be input through the sales provision terminal 12 and stored in the data server 30 through the propensity analysis proximity display server 20.

Furthermore, in a more preferred example, as shown in FIG. 4, the analysis, classification, and calculation of the similarity and display distance may be performed while the table of index values for each item is stored in the data server 30 in advance. There will be.

First, the propensity analysis proximity display server 20 analyzes the purchase request data or the sales offer data stored in the data server 30, and details the purchase request data or the sales request data as items of the classification data stored in the data server 30. Purchasing sales information analysis classification step (S02) to classify the data is performed.

Thus, each item of the requested data or the provided data is for the goods as in the example of Fig. 4, 5, 9, etc., the period of use, price, location, manufacturer, product model name, sales period, compromise price of the goods For example, it may consist of one or more items of a trading method. Of course, in the case of information retrieval rather than other products or products, the corresponding items may be different, and the index values given by these items may also be appropriately determined and operated according to the contents of the corresponding products or information.

Then, data related to the buyer and the seller are input and analyzed, respectively, and the classification process of the data according to the corresponding items is performed, by the tendency analysis proximity display server 20, the data server 30 by the purchase sales information analysis classification step. Analyzes and classifies purchase request data and sales offer data stored in), and analyzes the similarity degree for individual actual data of corresponding items of multiple sales offer data based on actual data of each item of purchase request data, Similarity calculation step (S03) for each item is performed so that the data of the degree of similarity with respect to the purchase request data which is a reference to a plurality of individual sales offer data is stored in the data server 30 together with the information of the corresponding sales offer data. will be.

That is, as shown in FIGS. 5 and 9, data input by a buyer and a seller are input. As shown in the example of FIG. 4, the purchase request data of the buyer and the sales offer data of the seller are based on an index table. After assigning an index value for each item, similarity data for each item is calculated.

As shown in FIG. 7, the process of calculating the operation is as follows: 'buyer (seller) data input', '5 months of usage period (actual data input by other items)', and 'comparison with reference values of the period of use table' (reference index Table values are compared with actual data for each item ', and' set index values according to the compared reference values (by comparing the index values calculated by the corresponding index table) '.

Accordingly, as shown in FIG. 8, the item-specific data input by the buyer or seller is subjected to a process of comparing with the criteria of the item-specific index table. That is, if the value entered in the price item is 600,000 won, the indexed value will have an index value of 'B5'.

As a result, when the index value for each item of the purchase request data of the buyer is calculated, the index value for each item may be given as shown in FIG. 6.

In addition, in the case of the seller, as shown in FIG. 9, the index values for each item are calculated as shown in FIG. As such, according to an embodiment of the present invention, the index values of the buyer and the seller may be calculated according to the classification of the index table previously input for the item-specific data input by the buyer or the seller.

Similarity is calculated for the individual sales offer data based on the purchase request data with respect to the purchase request data and the sales offer data of the buyer and the seller calculated as described above.

For example, as shown in the example of FIG. 11, the data of similarity is calculated by comparing and comparing the purchase request data with each item of the sales offer data of the individual seller, which is compared and discriminated for each item among the purchase request data of the buyer. .

That is, the index values are calculated for individual items of the buyer and seller's purchase request data and sales offer data, which are converted from the index table, which is the reference data, and the relative display distance data is calculated by subsequent calculation processing. will be.

As an example of the process calculated for each item, when describing the term of the 'use period', the actual data value of the use period item among the purchase request data of the buyer, which is a criterion of similarity determination, is '5 months'. . In the index table of FIG. 4, the data of '5 months' of the usage period item is '4 to 6 months' usage data, and the index value is 'A4' as shown in FIG. 6.

On the other hand, the period of use of the seller 1 illustrated in FIG. 9 is 'new product', and the index value corresponding to the actual data of the term of 'use of new product' is shown in FIG. 4 as shown in FIG. Will be.

Accordingly, as shown in the example of FIG. 11, the purchase request data of a buyer who wants to purchase an item whose actual data of the term 'use period' is '4 to 6 months', and the actual data of the 'use period' item is 'new product'. When the sales offer data of a seller who intends to sell an item of merchandise is compared with a relative position calculation based on a given index table, the index value is different from the index value by three levels (| A4-A1 |). Based on the index table, which is an absolute reference suggestion, the index calculation value between the purchase request data of the buyer and the seller request data, which are the basis of sales, is similar in the term of 'use period'. Will be the data value of.

In another example, for an item of price, the buyer's actual data value is $ 600 and the index value is 'B5'. In addition, the seller's actual data value is '1.1 million won' and the index value is 'B6'.

Accordingly, since the difference between the index values between the two is as much as one level, the data of similarity is calculated as one level. In this way, when similar to the purchase request data of each item buyer as shown in Figure 12 is the data value of the similarity for each item in the sales offer data of the individual seller is calculated.

Next, the data of the display distance is calculated from the similarity data values thus calculated.

That is, the propensity analysis proximity display server 20 reads the similarity data for each item of the individual sales providing data with respect to the purchase request data stored in the data server 30, and the similarity level for each item of the individual sales providing data. The display distance data calculation step S04 is performed in which data of the display distance calculated and calculated from the data is stored in the data server 30.

For example, as shown in FIG. 13, the data of the display distance may be calculated by arithmetic processing of simply adding up data of similarity for each item.

As a result, the data value of the display distance of the seller 1 will be '10', the data value of the display distance of the sales offer data of each seller, such as '9' in the case of the seller 2 and '5', etc. This is calculated.

In this example, it is illustrated that the data of the display distance is calculated by a simple summation of the similarity data for a plurality of items. However, it may be arranged that the display distance is calculated by adding an additional value to the item in the form of data or to the buyer. In addition, other operations can be performed by applying various operations such as adding, subtracting, multiplying, and dividing, or by applying formulas in which they are combined, not simple sums. During the process, it may be selectively applied according to the preference tendency of the buyer, the type of inclination field, and the conditions of the seller or the buyer.

In this way, the data of the similarity degree and the display distance are calculated based on the purchase request data of the buyer and the seller, the sales offer data, and the index table, which is the reference data for the calculation. In the propensity analysis proximity display server 20, UI display data displayed on the UI screen of the purchase request terminal 11 is calculated, and is individually based on the buyer's icon according to the display distance data included in the UI display data. The seller's icon is displayed, and UI display data including purchase request data, a plurality of sales providing data, and data of the display distance are transmitted from the tendency analysis proximity display server 20 to the purchase request terminal 11 of the buyer. The display data transmission step (S05) is performed.

In addition, the purchase request terminal 11, receiving the purchase request data, a plurality of sales offer data and UI display data from the propensity analysis proximity display server 20, the UI screen 111 of the purchase request terminal 11 A propensity display step of displaying a plurality of seller's icons on the basis of the buyer's icon on the screen, wherein the plurality of individual seller's icons are spaced apart from the buyer's icon by the display distance data of the UI display data (S06). Will be performed.

That is, the data of the similarity level and the display distance of individual sales offer data are calculated based on the purchase request data from the purchase request data and the sales offer data, and then the propensity analysis is performed using the UI display data. It is transmitted from the proximity display server 20 to the purchase request terminal 11 of the buyer, and the individual seller centered on the icon of the buyer on the UI screen 111 of the purchase request terminal 11 according to the transmitted display distance data. Icon will be displayed.

Accordingly, the icon of the individual seller is displayed using the data of the illustrated display distance shown in FIG. 13. As shown in FIG. 14, the icon of I (when the buyer is the reference point) is a reference point (0, 0) of all coordinates ( If the center position on the UI screen) is set, the icon of the individual seller, which is the other counterpart, is displayed on the UI screen according to the data value of the display distance.

In the example of FIGS. 13 and 14, the buyer and the seller 1 will be displayed by being separated by a relative distance size of '10', which is a data value of the display distance when comparing the reference value data. And the buyer and seller 2 will be displayed spaced apart by the relative distance size of the data value of the display distance '9'. Further, with respect to other sellers, seller 3 is the display distance '5', seller 4 is the display distance '5', seller 5 is the display distance '8', seller 6 is the display distance '10' and seller 7 is the display distance '15' , Seller 8 is represented by the display distance '16'.

As such, the icons of the plurality of sellers are displayed in accordance with the data of the similarity and the display distance of the respective sales offer data, which is the reference to the purchase request data of the buyer, and the data of the display distance calculated from the similarity data is It is calculated and displayed according to the similarity between the purchase request data and the sales offer data.

Thus, as shown in FIG. 14, when the icon of the buyer (I), which is the reference point of the display, is centered, the icon of the seller displayed closest to each other will be the seller having the closest tendency.

Therefore, the buyer as a user can easily identify the seller with the tendency that is closest to his or her request purchase tendency at a glance. Therefore, the buyer can select the icon of the seller that is closest to his / her icon and select the You can easily check the data.

Referring to the drawing shown in FIG. 14, since the icons such as seller 3 and seller 4 are displayed closest to each other (data of the display distance is '5'), the propensity of the sales offer data of these sellers 3 and 4 is first displayed. Will be examined.

According to the actual data of each seller 3 item, 'Use period' is '2 months', 'Price' is '350,000 won', 'Location' is 'Busan', 'Manufacturer' is 'Samsung', 'Model name' is 'E6400', 'sale period' is' 2 months', 'negotiation' '30, 000 won ',' transaction method 'is' quick' and so on. In comparison with the purchase propensity condition of buyers, 'Location' item is the same as 'Busan', and among other items, the items such as 'Use period', 'Price', 'Selling period' have similar similarities. Able to know.

In addition, if you look at the actual data of each item of Seller 4, 'Use period' is '3 months', 'Price' is '780,000 won', 'Location' is 'Daegu', 'Manufacturer' is 'MSI', 'Model name' is 'i3', 'sale period'is' one month ',' negotiation 'is '50, 000 won' and 'transaction method'is' overseas delivery '. Thus, items such as 'price' and 'sale period' are most similar, and items such as 'use period' and 'location' have somewhat similar data.

In this way, the buyer selects an icon of a closely displayed seller having a proximity tendency from among a plurality of seller icons displayed on the UI screen 111 of his purchase request terminal 11, and has various contents as shown in FIG. 9. It is easy to check the sales offer data of each one of the sellers in detail. In this way, the buyer selects an icon of a seller who wants a transaction displayed on the purchase request terminal 11, and a buying step (e.g., a purchase process and a commerce) is performed. S07) is performed.

As described above, the e-commerce method will be performed while looking at the icons of the plurality of sellers displayed on the buyer terminal 11 UI screen 111 of the buyer.

Accordingly, the icon of the buyer is displayed at the center (0, 0) of the display, and other displayed icons are displayed apart from the icon of the buyer by the data of the display distance. In this display method, only the distance of the display is calculated and the display angle may be determined in a manner that is randomly determined as shown in FIGS. 14 and 15. In other words, the display distance is the data of the calculated display distance, and the display angle may be determined and displayed as an arbitrary random value.

In addition, as described below, the display angle may be displayed as a value of rectangular coordinates, and the display distance separated from the icon of the shopper, which is the center point, becomes the radius of the display, and the 'X' value of the display that is one-sided coordinate value is If arbitrarily determined, another coordinate value 'Y' may be determined and displayed by the following calculation method.

That is, among the data of the display distance calculated by the display distance data calculation step S04, the UI screen 111 of the purchase request terminal 11 with respect to the sales offer data whose data value of the display distance is equally or similarly calculated. The distance between the icon of the buyer and the icon of the seller displayed on is processed according to the data of the display distance, and the position where the icon of the seller is displayed is an orthogonal coordinate operation using the data value of the display distance as a radius and the rectangular coordinate value of the position. It may be processed to be calculated in a method or group distribution method.

In the rectangular coordinate calculation method, a data value of a display distance is a radius (r), and one side coordinate value (x) of the displayed rectangular coordinate is determined randomly, and another coordinate value (y) is

y ^ 2 = r ^ 2-x ^ 2

It may be provided to be calculated.

Accordingly, in the display coordinates of the icons of other clients (sellers) from the criteria on which the icon of the buyer (purchaser) is displayed, there may be several cases where the data of the display distances is the same, and thus a plurality of data having the same display distances are used. The display coordinates of the icon of the seller client may be set to be randomly selected.

For example, as shown in FIG. 15, when each distance of the plurality of clients based on the center point (b) is '10', the display screen generated by them will be displayed while forming a circle.

r ^ 2 = x ^ 2 + y ^ 2 (Radius Squared = X-Coordinate Squared Square + Y-Coordinate Squared)

It can be expressed as a radius value r and a rectangular coordinate system (X-Y).

Accordingly, the X coordinate value may be determined as an arbitrary random value within a range not larger than the data value of the radius of the display distance.

[Rand ()% (r)]

Substitution of the X coordinate value with such a random value is an example in which the radius (data of the display distance, r) is set to '10', such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, etc. Among the numerical values will be determined. Of course, the coordinate values of X or Y may be determined in integer form, or may be operated to be displayed in real form. In this example, each coordinate value is determined in an integer form.

Therefore, the equation given in the previous example,

y ^ 2 = r ^ 2-x ^ 2

According to, when the radius (data value of the display distance, r) is set to '10' and the X coordinate value given as an arbitrary random value is set to '9',

y ^ 2 = 10 ^ 2-9 ^ 2 = 100-81 = 19,

y is the coordinate value of the numerical value of approximately 4.35 is computed.

In the case of the embodiment displayed by rounding up or down, the value becomes '4'.

Therefore, within the radius of the position display, it can be represented by the coordinate value of (9, 4). As another example, when the X coordinate values are given as '8' and '7', the coordinate values may be displayed as (8, 6), (7, 7), or the like.

Next, in the group distribution method, as shown in FIGS. 18 to 21, a plurality of sales providing data whose data values of display distances are calculated as the same or similar data values may be provided in groups for each item. There will be.

In the example of FIG. 9 and the like illustrated in the present invention, the number of sellers is limited to eight. However, since a number of sellers are actually targeted, a seller having a data value of the same or similar display distance will be considerable. In addition, when processing the display of icons of not only multiple sellers but also other buyers centering on the icons of buyers who want to purchase, it may be more difficult to distinguish since more client icons are displayed on the screen at the same time.

Of course, if you become a seller as an information requester, the icons of many buyers and many other sellers are displayed on a single screen centering on the icons of the sellers themselves.

Of course, a few counterpart clients may be displayed based on the display distance data instead of a separate display method.

But for many other clients, a more visually discernible display can be presented.

In other words, the user can check the display position of the classified client in the propensity analysis process to confirm the seller and the buyer. Therefore, during final decision making (purchase decision or sales decision in case of seller), it is necessary to compare the clients whose data values of the displayed distance are displayed at the same or similar distance. Especially when a large number of clients have data values with similar display distances, it becomes cumbersome to check them individually. To alleviate this hassle, clients that can be displayed within a specific range can be classified by level, so that clients included in the level can be compared and viewed separately.

For example, as shown in the example of FIG. 18, in a state where icons of a plurality of different clients are displayed on a screen of a user (buyer), a group having a predetermined range of data values of display distances may be classified by level.

Thus, if the number of sellers classified by level will be as shown in FIG.

Level 1 will be Seller 1, Seller 2, Seller 3, Seller 4, Seller 5, Seller 6, and at the second level, Seller 7, Seller 8, and so on. In addition, individual sellers will be displayed by the display method as shown in FIG.

Therefore, after the UI display data is processed by the propensity analysis proximity display server 20 to distinguish the groups by level, the UI display data is transmitted to the purchase request terminal 11 to be displayed. We will make a purchase decision by selecting the icon of the seller and looking at the sales contents of the sellers in the Level 1 group.

In other words, from the user's point of view (the buyer, sometimes the seller), another client (seller) at level 1 is considered to be the most similar to the value of the item's reference data because it is most similar to his or her desired propensity. You will select a distributed seller to proceed with the search and purchase.

Furthermore, the data of the clients classified by levels will be used as a detailed search to make the final decision of the user. This decision is made while viewing the display contents displayed on the UI screen 111 of the user's (purchaser's) purchase request terminal 11, and can be easily compared and visually judged in order to provide better help in making this decision. You can use a pop-up window or a separate page switching method. That is, the data is processed by the propensity analysis proximity display server 20 so that a separate pop-up window or a separate page switching method is applied, and the processed data is transmitted to the purchase request terminal 11 and displayed on the screen.

In particular, in the screen contents shown to the user (purchaser), a plurality of individual group reference coordinate values having a reference value at a specific coordinate value of the screen of the purchase request terminal 11 are provided for each group, and based on the group reference coordinate values. To divide the regions to form regions (aggregate spaces) divided into a plurality of groups.

In addition, the icons of sellers related to the group may be collected and viewed in the divided areas of the groups.

As an example of such a group-specific region, it may be implemented as a pentagonal group region as shown in FIG. 21. In this way, the setting of the group area, distribution setting of sellers corresponding to the group, etc. may be transmitted to the purchase request terminal 11 after being processed by the tendency analysis proximity display server 20 and displayed.

Coordinates to be included in the aggregate space (group area) may be divided into groups, with the emphasis on the most similar among the similar items of each client (seller) within the range of the reference value.

That is, as shown in the example of FIG. 21, among the purchase request data of the buyer and the sale providing data of the seller, among the plurality of items, which may affect the purchase or sale, the distance according to the 'selling period', 'location', and 'use' Periods, 'trade-offs', 'prices', etc. are the basic group areas, and since the items are five items, the group areas may be implemented in a pentagonal form.

In this example, the seller 1 has an index value of '0' for the similarity data of the item 'selling period', and thus the similarity level in the 'selling period' of the purchase request data requested by the buyer is closest to each other. Seller 1 may be displayed to belong to the display group of the 'sale period'.

In addition, the seller 2 may be displayed to belong to the display group for the 'price' because the index value is '0' with the data of the similarity of the items of the 'price'.

This allows multiple sellers to be divided into groups of items that most closely resemble the buyer's propensity to buy, so buyers (users) focus on groups of items they prefer most, so sellers with a more preferred preference tend to be faster. And you can choose and buy a product.

Of course, when the number of conditions of purchase tendency that the buyer thinks is less, the shape of the displayed group area may be classified into various shapes such as square, triangle, hexagon, and hexagon instead of pentagon.

Furthermore, in the display method of the group region, each group region may be displayed as a group spaced apart from each other, as shown in FIG. 21. For example, similarity tends to display 'use period', 'sale period' and 'transaction method' on one side as triangular group display area, which is one small group area, and 'price' and 'negotiation' on the other side of the screen. ',' May be implemented to be displayed as a triangular group display area which is a small group area. That is, triangular small groups may be divided and displayed on both sides.

Likewise, if necessary, the application is displayed in a number of small groups, various types of groups, and areas, so that the user (buyer) can proceed with the purchase while looking at the classification of his or her favorite tendency.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. The technical idea of the present invention should not be construed as being limited.

11: terminal for purchase request 12: terminal for sale
13: network 14: commerce processing unit
15: payment system 16: delivery system
20: propensity analysis proximity display server 22: data analysis classification unit
23: Similarity calculation unit for each item 24: Display distance data calculation unit
30: data server 31: input data storage unit
32: classification analysis data storage unit 33: similarity data storage unit
34: display distance data storage
100: condition analysis e-commerce system
101: propensity analysis proximity display system
110 Information request terminal 120: Information provision terminal
110 ', 111: UI screen

Claims (8)

An information request terminal 110 used by the information requester;
Information providing terminal 120 used by the information provider,
It is connected to the information requesting terminal 110 of the information requester, the information providing terminal 120 of the information provider and the network 13, the information requesting data of the information requester and the information providing data of the information provider is input and stored in the data server 30. Propensity analysis proximity display server 20 to be,
It includes a data server 30 that stores the information request data of the information requester, the information provider data of the information provider, the analysis classification data analyzed and classified for the information request data and the information providing data,
The tendency analysis proximity display server 20,
Comparing a plurality of information providing data on the basis of the information request data to determine similar information providing data similar to the information request data,
In order for the icon of the information provider related to the information providing data similar to the information request data to be displayed in close proximity to the icon of the information requestor on the screen of the information requesting terminal 110, a plurality of pieces of information depending on the similarity with the information request data. Classify the provided data,
In the propensity analysis proximity display server 20, the UI display data to be displayed on the information requesting terminal 110 is calculated, and the icons of a plurality of information providers are displayed based on the icons of the information requestor by the UI display data. Although the data of the display distance for displaying the icon of the individual information provider and the icon of the information requester is calculated from the data of the degree of similarity between the individual information providing data and the information request data,
In the propensity analysis proximity display server 20, UI display data including information request data, a plurality of information providing data and data of the display distance is provided to be transmitted to the information request terminal 110 of the buyer,
The information requesting terminal 110 receives information request data, a plurality of information providing data, and UI display data from the tendency analysis proximity display server 20, and displays the UI screen 110 ′ of the information requesting terminal 110. Icon of a plurality of information providers to be displayed mainly on the icon of the information requestor, characterized in that the icons of a plurality of individual information providers are displayed so as to be spaced apart from the icon of the information requestor by the data of the display distance of the UI display data Propensity Analysis Proximity Display System with propensity analysis technology.
A purchase request terminal 11 used by the buyer,
A sales provision terminal 12 used by the seller,
Analysis of propensity to be connected to the buyer's purchase request terminal 11, the seller's sales providing terminal 12 and the network 13, the buyer's purchase request data and the seller's sales providing data are stored in the data server 30 Proximity display server 20,
It includes a data server 30 for storing the analysis classification data analyzed and classified for the purchase request data of the buyer, the sales offer data of the seller, the purchase request data and the sales offer data,
The tendency analysis proximity display server 20 compares a plurality of sales offer data based on the purchase request data to determine sales offer data similar to the purchase request data, and a sales provider associated with the sales offer data similar to the purchase request data. In order to be displayed in close proximity to the icon of the purchase requester icon on the screen of the purchase request terminal 11, and classifies a plurality of sales offer data according to the similarity with the purchase request data,
In the propensity analysis proximity display server 20, the UI display data to be displayed on the purchase request terminal 11 is calculated, and the icons of a plurality of sellers are displayed based on the buyer's icon by the UI display data. The data of the display distance for displaying the icon of the individual seller and the icon of the buyer is calculated from the data of the degree of similarity between the individual sales offer data and the purchase request data.
In the propensity analysis proximity display server 20, UI display data including purchase request data, a plurality of sales providing data and data of the display distance are provided to be transmitted to the purchase request terminal 11 of the buyer,
The purchase request terminal 11 receives the purchase request data, a plurality of sales providing data and the UI display data from the tendency analysis proximity display server 20, and displays the UI screen 111 of the purchase request terminal 11. The icons of a plurality of sellers are displayed centering on the icons of the buyers, and the icons of a plurality of individual sellers are displayed to be spaced apart from the icons of the buyers by the data of the display distance of the UI display data.
Among the icons of the seller displayed on the purchase request terminal 11, the purchase selection information selected by the buyer for the icon of the seller who wishes to trade is provided to be transmitted from the purchase request terminal 11 to the propensity analysis proximity display server 20. Condition analysis e-commerce system with propensity analysis proximity display technology.
The method of claim 2,
The propensity analysis proximity display server 20,
Analyze the purchase request data and the sales offer data inputted and transmitted from the purchase request terminal 11 and the sales providing terminal 12, classify by item, analyze the actual data classified by the item, and analyze and classify the data. Data analysis classification unit 22 to be stored in the server 30 is provided,
Analyze and classify purchase request data and sales offer data, analyze similarity with respect to individual actual data of corresponding item of multiple sales offer data based on actual data of each item of purchase request data, Similarity calculation unit 23 for each item is provided so that the data of the degree of similarity to the purchase request data that is the reference to the sales offer data is stored in the data server 30 together with the information of the sales offer data.
The data of the similarity level for each item of the individual sales offer data is read with respect to the purchase request data, and the data of the display distance calculated from the data of the similarity level for each item of the individual sales offer data is stored and stored in the data server 30. Condition analysis electronic commerce system to which the propensity analysis proximity display technology is applied, characterized in that the display distance data calculation unit 24 is included.
The method of claim 2,
The data server 30,
An input data storage unit 31 for storing data input from the purchase request terminal 11 and the sales providing terminal 12 through the tendency analysis proximity display server 20;
The purchase request data and the sales offer data stored in the input data storage unit 31 are analyzed by the propensity analysis proximity display server 20, classified by item, and the actual data of the corresponding item are analyzed and analyzed and classified data are stored. Classification analysis data storage unit 32,
Multiple sales based on the actual data for each item of the purchase request data by the tendency analysis proximity display server 20 reading the analysis and classified purchase request data stored in the classification analysis data storage unit 32 and the sales offer data. Analyze the degree of similarity with respect to the individual actual data of the corresponding item of the offer data, and the similarity degree of the similarity data about the purchase request data which is a reference to the multiple individual offer data and the information of the corresponding offer data. A data storage unit 33,
The similarity degree for each item of the corresponding individual offer data by the tendency analysis proximity display server 20 which reads the similarity data for each item of the individual offer data for purchase request data stored in the similarity data storage unit 33. The condition analysis electronic commerce system to which the propensity analysis proximity display technology is applied, characterized in that it comprises a display distance data storage unit 34 for storing the data of the calculated display distance calculated from the data of.
The method of claim 2,
On the UI screen 111 of the purchase request terminal 11 with respect to the purchase request terminal 11 which has received a plurality of sales providing data and data of the display distance of the individual sales providing data from the tendency analysis proximity display server 20. The purchase selection information which selects one seller's icon for purchase among the displayed seller's icons is transmitted and received through the propensity analysis proximity display server 20 to receive and receive a user who is a user of the purchase request terminal 11 and the sales providing terminal 12. And a transaction processing unit 14 for allowing a sales transaction to proceed between the seller and the seller,
There is provided a payment system 15 for performing a payment step during the data processing process between the commerce processing unit 14 and the purchase request terminal 11,
A condition analysis electronic commerce system to which a propensity analysis proximity display technology is applied, comprising a delivery system 16 for allowing a purchaser to deliver a product traded through a purchase request terminal 11 at a buyer request.
Input data including information of purchase request data or sales providing data input by the buyer or seller is transmitted from the purchase request terminal 11 or the sales providing terminal 12 to the propensity analysis proximity display server 20 to receive the data server ( A purchase sale information input step (S01) to be stored in 30);
The propensity analysis proximity display server 20 analyzes the purchase request data or the sales offer data stored in the data server 30, and the purchase request data or the sales request data as detailed data of the classification data stored in the data server 30. Purchasing sales information analysis classification step (S02);
In the propensity analysis proximity display server 20, the purchase and purchase analysis data classified and stored in the data server 30 and the sale providing data are read by the purchase and sale information analysis classification step, and actual data for each item of the purchase request data is read. Based on the analysis, the degree of similarity is analyzed for the individual actual data of the corresponding item of the multiple sales offer data, and the similarity data for the purchase request data that is the reference for the multiple individual sales offer data is included in the relevant sales offer data. Item-specific similarity calculation step (S03) to be stored in the data server 30 together with the information;
The propensity analysis proximity display server 20 reads the data of similarity for each item of the individual sales providing data with respect to the purchase request data stored in the data server 30, and the data of similarity for each item of the individual sales providing data. A display distance data calculation step (S04) for storing the data of the display distance calculated and calculated from the data server 30;
In the propensity analysis proximity display server 20, UI display data displayed on the UI screen of the purchase request terminal 11 is calculated, and is individually based on the buyer's icon according to the display distance data included in the UI display data. The seller's icon is displayed, and UI display data including purchase request data, a plurality of sales providing data, and data of the display distance are transmitted from the tendency analysis proximity display server 20 to the purchase request terminal 11 of the buyer. Display data transmission step (S05);
In the purchase request terminal 11, the purchase request data, a plurality of sales providing data and the UI display data from the propensity analysis proximity display server 20, received on the UI screen 111 of the purchase request terminal 11 A propensity display step (S06) in which a plurality of seller's icons are displayed based on a buyer's icon, and the icons of a plurality of individual sellers are displayed to be spaced apart from the buyer's icon by data of the display distance of the UI display data;
The buyer selects an icon of a seller who wants a transaction displayed on the purchase request terminal 11, and a tendency analysis proximity display technology is applied, characterized in that a purchase step (S07) including a purchase procedure and a commerce is included is provided. Condition Analysis E-Commerce Method.
The method according to claim 6,
Each item of the requested data or the provided data is applied to at least one item of the term of use, price, location, manufacturer, product model name, sales period, trade-off price, and transaction method. Condition Analysis E-Commerce Method.
The method according to claim 6,
Of the display distance data calculated by the display distance data calculation step (S04), the sales offer data for which the data value of the display distance is calculated equally or similarly is displayed on the UI screen 111 of the purchase request terminal 11. The displayed distance between the icon of the buyer and the icon of the seller is processed according to the display distance data, and the position at which the icon of the seller is displayed is a Cartesian coordinate calculation method using the data value of the display distance as a radius and the rectangular coordinate value of the position. Or processed in a group distribution manner,
In the rectangular coordinate calculation method, a data value of a display distance is a radius r, and one side coordinate value x of the displayed rectangular coordinate is randomly determined, and another coordinate value y is
y ^ 2 = r ^ 2-x ^ 2
It is provided to be calculated to be,
In the group distribution method, a condition analysis electronic commerce to which a propensity analysis proximity display technology is applied is provided such that a plurality of sales providing data whose data values of display distances are calculated with the same or similar data values are displayed in groups for each item. Way.

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