JP2011043916A - Open market system, server for the same and control method for the same - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an open market system for objectively evaluating betting forecast data. <P>SOLUTION: The open market system includes: a server; seller terminals to be connected to the server; and buyer terminals to be connected to the server. The server includes a results analysis part that collates and analyzes a result of the betting forecast data, and creates data on results. The server also includes a form part that outputs a form for inputting the betting forecast data presented from the seller terminal. The seller terminal inputs the standardized betting forecast data by use of the form. The server outputs the data on the analyzed results and the presented betting forecast data from a betting presentation part. The server also includes a settlement part performing settlement related to the presented betting forecast data. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an open market system, an open market system server, and an open market system control method. More particularly, the present invention relates to an open market system for buying and selling buy-out forecast data, an open market system server, and an open market system control method.

In Japan, public competitions, which are sporting events held by public institutions and gambling, include horse racing, bicycle racing, boat racing, and auto racing.
Conventionally, prediction information such as which horse or player wins in such a public competition has been provided by a paper medium such as a newspaper or a magazine.
However, in recent years, with the development of a communication line network such as a mobile phone network and the Internet, prediction data that can be browsed by a terminal is sold and provided for a fee or in a form such as free and advertising revenue.

As a conventional system for providing such prediction data, with reference to Patent Document 1, a prediction information providing system capable of providing prediction information according to the race progress state or the like is described by a prediction information providing terminal (hereinafter referred to as conventional technology). 1).
In the prediction information provision system of Prior Art 1, the information held from the host of the Totalizeta system, which is a computer system that sells, pays, and displays odds (approximate dividend rate), etc. for voting tickets in public competitions, is aggregated to public competitions. Information provided by a plurality of predictive information providers in the vicinity of the field can be provided in correspondence with the progress of the race even in a remote area such as a stadium.

JP 2006-85532 A

However, conventionally, in the public competition, there is a problem that most of the products sold and sold as the forecast data are simply predictions such as the race arrival order.
Therefore, referring to paragraph [0067] of the prior art 1 and the like, “buy eye” is a combination, a set number, or an object number for purchasing a voting ticket (ticket) when purchasing a voting ticket from race prediction data. ”Was output.
That is, in the prediction information providing system according to the prior art 1, the purchase prediction data itself cannot be bought and sold as a product.
For this reason, there was a problem that it was difficult to compare results. Therefore, the buyer of the forecast data has no choice but to believe in the seller's self-declaration and hesitated to buy the forecast data.

  This invention is made | formed in view of such a condition, and makes it a subject to eliminate the above-mentioned subject.

The open market system of the present invention is a public market system comprising a server, a seller terminal connected to the server, and a buyer terminal. The server collates and analyzes the results of past buy eye forecast data and A performance analysis unit for creating data, a form unit for outputting a form for inputting buy eye forecast data to be exhibited from the seller terminal, analyzed result data, and the buy eye forecast data that has been submitted; And a settlement unit for making a settlement related to the forecasted purchase eye data that has been exhibited, and the seller terminal uses the form to purchase forecasted eye forecast data. It is characterized by inputting and entering.
The open market system of the present invention is characterized in that, as the analysis, the performance analysis unit calculates statistics and ranking and creates performance data.
The open market system of the present invention is characterized in that the buy-and-sale section searches for the record data in response to an instruction from the buyer terminal.
In the open market system of the present invention, the server further includes a result acquisition unit that acquires a result of the exhibited buy eye forecast data, and the performance analysis unit includes the exhibited buy eye forecast data, the result The data is collated and the result data is updated.
The server of the open market system of the present invention is for inputting a buy-through forecast data to be exhibited from a seller terminal, a performance analysis unit that collates and analyzes results of past buy-up forecast data and creates a record of actual results. A form part for outputting a form, a data of the analyzed results, a buy-ahead selling part for outputting the expected buy-ahead data, and a settlement part for performing a settlement related to the expected buy-ahead data displayed It is characterized by providing.
The open market system control method according to the present invention is a public market system provided with a server, a seller terminal connected to the server, and a buyer terminal, and compares and analyzes the buy eye forecast data and the result of the buy eye forecast data. Create actual data, output a form for entering the expected purchase data to be listed, enter the expected purchase data that has been standardized using the form, submit the listing, and analyze the actual results It outputs the data and the buy eye forecast data that has been exhibited.

  According to the present invention, it is possible to objectively compare past results by collecting and publicizing prospective purchase data that sellers have exhibited, and to release psychological barriers to buyer purchases. A market system can be provided.

It is a conceptual diagram of the system configuration | structure of the open market system X which concerns on embodiment of this invention. It is a block diagram which shows the control structure of the server 10 which concerns on embodiment of this invention. It is a flowchart of the open market process which concerns on embodiment of this invention. It is a conceptual diagram of the buying eye forecast data which concerns on embodiment of this invention. It is an example of a screen of the results / buy eye forecast search displayed on the browser of buyer terminal 21-1 concerning an embodiment of the invention. It is an example of a screen of the results / buy eye forecast search result displayed on the browser of buyer terminal 21-1 concerning an embodiment of the invention. It is a screen example of the web form displayed on the browser of seller terminal 23-1 concerning an embodiment of the invention.

<Embodiment>
[Configuration of Open Market System X]
First, with reference to FIG. 1, the structure of the open market system X which concerns on embodiment of this invention is demonstrated. In the present embodiment, an example of a configuration in which the public market system X buys and sells public game purchases as an open market will be described.
In the open market system X according to the embodiment of the present invention, a server 10, buyer terminals 21-1 to 21-n, seller terminals 23-1 to 23-n, and an administrator terminal 25 are connected to a wireless telephone network. Or a network 5 such as the Internet or an intranet.
Specifically, the network 5 is a wireless communication line such as a mobile phone network, a PHS network, WiMAX, a wireless LAN, a wired telephone line, a LAN, a power line LAN, a cLink, and a dedicated line. Further, as the form of the network 5, an IP network or other star-shaped or ring-shaped network can be used.
The server 10 is a server that uses a PC / AT compatible machine, a dedicated machine, a blade server, or the like and is connected to the network 5 to provide various services related to the purchase market.
The buyer terminals 21-1 to 21-n, the seller terminals 23-1 to 23-n, and the administrator terminal 25 are web browsers capable of browsing HTML (Hyper Text Markup Language) or the like. PC / AT compatible machine, PDA (Personal Data Assistant), mobile terminal, smart phone, mobile phone, and the like.
The buyer terminals 21-1 to 21-n are terminals that are used by the buyer (buyer user) of the forecasted eye data.
The seller terminals 23-1 to 23-n are terminals used by sellers (seller users) of buy-out forecast data.
The administrator terminal 25 is a terminal used by the administrator of the server 10.

[Control Configuration of Server 10]
Next, with reference to FIG. 2, the control configuration of the server 10 that provides the buying market will be described in detail. The server 10 mainly includes a network input / output unit 110, an open market server 120, a database server 130, and a DNS / mail server 140.

The network input / output unit 110 is a router, a load balancer (load balancer), or the like. The network input / output unit 110 receives IP packets from the network 5 and transmits them to each unit, and collectively transmits the IP packets from each unit to the network 5.
The network input / output unit 110 also supports packet filters, firewalls, syslogs, and the like.

The open market server 120 is a server that provides open market services accessed by buyers and sellers. A control unit such as a CPU includes a form unit 121, a performance analysis unit 122, a purchase item listing unit 124, a result acquisition unit 125, Various programs, classes, and the like such as the web server unit 126 and the settlement unit 127 are executed using hardware resources. These various programs, classes, etc. are services (daemons), CGIs (Common Gateway Interface), servlets, ASPs (Active Server Pages), PHP, various scripts, and other web applications, as a group of programs that perform specific operations. Can be provided.
The form unit 121 is a part such as CGI, servlet, XML data, or the like related to a data form for inputting buy-out forecast data. The form unit 121 creates a data form for the seller to input stylized buy-out forecast data, which will be described later. Further, when the form unit 121 receives data input using the data form from the seller terminals 23-1 to 23-n, the form unit 121 stores the data in the user database 131.
The performance analysis unit 122 refers to a performance database 132, a competition database 133, and the like, which will be described later, and calculates performance data such as a hit rate, a collection rate, a collection amount, a sales amount, etc., which become the “result” of the seller's expectation. This is the part to be created. Further, the result analysis unit 122 calculates a correlation coefficient based on data and the like such as confidence degree D106 (FIG. 4) in the buy eye forecast data described later, and uses various statistical methods and artificial intelligence methods. This is a part for calculating the tendency of seller data. Further, it is possible to create forecast data “likely to be correct” by performing maximum likelihood estimation by combining seller rankings and buy-out data.
The buy-out listing unit 124 is a part having a function of outputting data that can be displayed by a browser or the like in accordance with an instruction from the terminal, such as CGI, servlet, ASP, PHP. The buy-and-sale display unit 124 is a data that can be transmitted by the web server unit 126 such as HTML or XML about the purchase-ahead forecast data transmitted by the seller and the data related to the results aggregated / analyzed / calculated / evaluated by the result analysis unit 122. Convert to In addition, when the buyer searches for purchases from the purchase eye prediction data, the purchase item listing unit 124 can set various conditions and perform a search from the user database 131. In addition to the function of displaying a comment or the like, the buying eye listing unit 124 has functions such as SNS (Social Network Service), BBS (Bulletin Board System), and chat, and the buyer directly inquires of the seller. It is also possible. Thereby, the transaction which does not go through an open market can be suppressed. In addition, the buying eye listing unit 124 has a natural language processing function such as creating a text of an email to be transmitted.
The result acquisition unit 125 uses a timer or the like from a server (not shown) in which public competition results or bond prices are disclosed, and fetches the results after the results are known, so that HTML, XML, text data, This is a part for obtaining result data provided as binary data or the like. In addition, the result acquisition unit 125 can also convert a format suitable for the competition database 133 using a parser.
The web server unit 126 is a service (daemon) of a WWW (World Wide Web) server, and can use various modules such as IIS (Internet Information Server), Apache, and the like. In addition, the web server unit 126 can access the execution output of each program from each terminal by a WWW web browser by using an access control by an account using an interface such as a common gateway interface (CGI), a servlet, and PHP. To. The web server unit 126 is for building a WWW website including HTML, CHTML (compact / HTML) data, image data such as JPG, GIF, FLASH, and AVI, audio data, and other data. Data can also be read from each database or storage medium and transmitted.
The settlement unit 127 is a part that performs credit settlement, settlement at an Internet bank, and settlement in the Internet currency using SSL (Secure Socket Layer). At this time, processing such as e-mail transmission and web page creation can be performed according to conditions such as a time limit set by the buyer or seller.

The database server 130 is a part configured using a control unit such as a CPU, an SQL server, various storages, and hardware resources such as a program, and includes a user database 131, a performance database 132, a competition database 133, and an ID creation unit 135. It has a part like.
The user database 131 includes seller / buyer user ID (identification), password, credit card information for settlement, purchase forecast data, purchase / sales history, search history, description / viewing history such as comments, SNS and BBS, It is a database that stores other data such as addresses and telephone numbers.
The performance database 132 is a database that stores the buyer's purchase forecast data and the performance.
The competition database 133 is a database that stores data on the results of public competitions for buy eye prediction data, time-series data such as prices of various bonds, and the like. The competition database 133 stores not only the results, but also the attributes used for the buy eye prediction data, such as course, distance, ground and wave roughness, and the like accompanying the competition.
The ID creation unit 135 is a part that issues a password, key data, and the like related to an ID in order to allow each user of a seller and a buyer to access each database. The ID creation unit 135 also inputs data on the user database 131 of each seller and buyer in the user database 131 in accordance with signals from the buyer terminals 21-1 to 21-n and seller terminals 23-1 to 23-n. You can also.

The DNS / mail server 140 is a general DNS (domain name server) or an SMTP (Simple Mail Transfer Protocol) server for delivering electronic mail to users. As the DNS, Windows (registered trademark) DNS can be used, and as the SMTP service (daemon, program), a known SMTP program, for example, Mail Server manufactured by ArGoSoft or the like can be used.
In addition to being used as a DNS, this DNS / mail server 140 can also send the user ID and password of the account to the user who has entered the registration form on the official site.

[Public Market Processing of Open Market System X]
Next, an outline of the flow of open market processing in the open market system X will be described.
As described above, the public market according to the embodiment of the present invention is a public market for buying eye forecast data that can clearly analyze, compare, and disclose the past performance of the seller as data. In this open market, the buyer can select “expected purchase” with reference to the performance data and purchase at the same price. In other words, it is a market service where sellers and buyers are free to participate.
More specifically, the seller (seller user) uses the seller terminals 23-1 to 23-n to access the server 10 and submit buy eye forecast data for sale.
The server 10 provides, as a “buy market”, the past forecast results, the price of the forecast data, and the like for the buy eye forecast data.
Then, the buyer (buyer user) purchases forecast data from the buyer's eye market using the buyer terminals 21-1 to 21-n.
When each competition is completed and the results are known, the server 10 updates the results by sending and receiving money and acquiring the competition results.
After that, the purchase price is collected from the buyer, the commission is deducted, and the sales money is given to the seller.

[Buy eye forecast data structure]
Here, with reference to FIG. 4, an example of each attribute related to the buy-ahead forecast data provided by the seller will be described. The buyer's eye forecast data is transmitted to the server 10 by the seller terminals 23-1 to 23-n, and is stored in the result database 132 by the form unit 121 of the server 10.
The target game D101 is an attribute for specifying the game, such as the date, place, name, etc. of the game.
The betting ticket type D102 is an attribute indicating the ticket type to be predicted. Specifically, symbols, numbers, and the like corresponding to a predetermined target game type such as a single win, double win, double win, double win, triple win, triple win, triple win, etc. can be prepared as attributes for each game.
The purchase eye D103 is an attribute indicating an expected purchase eye corresponding to the betting ticket type. As this attribute, it is possible not only to simply store the flag of the purchase, but also to store accompanying data such as rank and “next point”.
The amount D104 is an attribute of the amount at the time of selling the buy-out forecast data. This amount is not simple money, and for example, a virtual currency unit such as “point” or “credit” or an amount such as what percentage of the winning amount can be prepared and used. In addition, when the buyer pays, information such as prepaid or postpaid can be stored. In the case of postpay, it is possible to determine a value such as purchasing a ticket via the server 10 and paying what percentage at the time of purchase. In the case of postpay, it is possible to prepare a system in which a deposit or a fee is paid in advance to the administrator of the server 10 in order to avoid a situation in which a seller with the same ID prepares a lot of random buy eye forecast data. Further, in the case of advance payment, it is also possible to store information such as depositing a fee first and remitting money to the seller only when an expectation is made.
The prediction method D105 is an attribute that stores a factor that is regarded as most important when the seller makes a purchase eye prediction. As this prediction method, a statistical method, an artificial intelligence method, a heuristic method using data, and other methods can be categorized and stored in about 10 types. For each method, for example, artificial intelligence can store subcategories such as “kernel machine”, “neural network”, and “decision tree”.
The degree of confidence D106 is an attribute that stores the degree of confidence with respect to the prediction as a self-evaluation. Specifically, the evaluation of about 3 to 5 levels such as A, B, and C self-reported by the seller is stored.
The comment D107 is a column for freely entering and storing a message to the buyer regarding the prediction. In this comment, when a contact or spam not via the server 10 is described, the form part 121 of the server 10 notifies the administrator terminal 25 via the DNS / mail server 140, and the ID Warn users.

In the following, processing for buying and selling such buy eye forecast data in the open market will be described in detail based on the processing of the server 10 with reference to the flowchart / timing chart of FIG.
Moreover, below, the example of a flow of a series of processes between the server 10 is demonstrated by making the seller terminal 23-1 and the buyer terminal 21-1 into a representative example.

(Step S101)
First, the performance analysis unit 122 of the server 10 performs past performance aggregation processing.
Specifically, first, the performance analysis unit 122 refers to the performance database 132 and the competition database 133. Then, the result analysis unit 122 uses the result data acquired by the result acquisition unit 125 and the purchase forecast data previously sold by the seller of the same ID, such as hit rate, recovery rate, recovery amount, sales amount, etc. Calculate "Actual". In this process, the past “actual result” which is the previous “actual result” is calculated.
Specifically, the performance analysis unit 122 first calculates a hit ratio that is a hit ratio in each race. At this time, values used in later statistical processing, such as data on the accuracy of the weather and confidence, are also calculated. In addition, it also calculates values such as hit rates by ticket types such as wins and frame runs, and differences between rank prediction and actual rank.
In addition, the performance analysis unit 122 calculates the amount of money to be collected by multiplying the hit ticket with the odds when the ticket is purchased with a predetermined purchase price based on the expected purchase as the collection amount. In addition, the expected value is calculated in percentage units as the amount collected and the collection rate.
Furthermore, the performance analysis unit 122 can also calculate the number of sales people and the sales amount when the seller sells the buy eye forecast data.
These values relating to “actual results” can be calculated and analyzed by taking an average of 1 to 3 months. It is also possible to calculate as time series data.
The performance analysis unit 122 stores the calculated and analyzed “result” in the performance database 132 as performance data.

(Step S102)
Next, the performance analysis unit 122 performs statistics / ranking creation processing.
The performance analysis unit 122 calculates and evaluates the ranking from the totals of “actual results” of sellers of each ID. At this time, the performance analysis unit 122 determines each condition such as the type and attribute of the competition, each value related to the “result”, the odds condition such as the betting ticket, the prepayment / postpay, the absolute value or the average value of the collection amount. Can be analyzed. Further, based on the “actual record” of the seller of each ID, it is possible to calculate and evaluate the statistical significance of the buying eye forecast.
Further, the performance analysis unit 122:
(1) Not only publishing past rankings but also rankings that can be recommended in terms of data.
Here, by analyzing the performance of each seller, the correlation coefficient between the “confidence level”, “target rate”, and “recovery rate” for each seller is calculated. In addition, the correlation coefficient is used to predict the “success rate” and “recovery rate” in the next competition. It is possible to create a ranking of recommendation and create ranking data by synthesizing the “Accuracy rate” and “Recovery rate” that narrow down the competition attributes.
(2) Not only looking at individual seller data, but also counting by “voting ticket type”, “amount”, “forecast method”, which ticket type, which amount, and which forecast method is most efficient, The ranking data is created by clarifying the problem.

In addition to this, the performance analysis unit 122 can perform statistical methods such as Bayesian estimation based on each purchase eye prediction data, and can acquire, for example, purchase prediction data with high likelihood and certainty. This data can be provided to buyers for a fee and distributed to each seller at a predetermined rate.
Furthermore, by calculating the statistical significance of the buy-out forecast and the specific competition results, it is possible to point out any fraudulent activities such as so-called eight-choice in public competitions.
The performance analysis unit 122 also stores rankings and various statistical values in the performance database 132 as performance data.

(Step S103)
Next, using the web server unit 126, the purchase item listing unit 124 performs a result disclosure process.
Specifically, the buying eye exhibition unit 124 is used for browsing HTML, XML, or the like displayed on the web server unit 126 with respect to results data such as rankings, results, and rankings obtained by the result analysis unit 122. Is created and stored in the results database 132.
When the buyer user browses the browsing data with a web browser, the web server unit 126 transmits each calculation result, ranking HTML, XML, and the like.

Further, the buy-and-sale display unit 124 can search the record data based on the instruction from the web browser of the buyer terminal 21-1.
Referring to FIG. 5, the buyer inputs the target competition attribute in the input field 610 of the web browser screen of the buyer terminal 21-1, the betting ticket type in the input field 620, and the amount range in the input field 630. A prediction method can be input in the column 640 and a target period can be input in the input column 650, and a search can be performed using any of these attributes.
Specifically, for the target competition attribute, an attribute associated with the competition such as the target competition, the stadium, and the course and distance can be selected. For example, when a target game such as horse racing is selected, the target game attribute is automatically selected and displayed as an attribute that matches the target game.
As the voting ticket type, the ticket type desired by the buyer can be selected.
In the amount range, it is possible to obtain a buy eye forecast according to the buyer's budget and purchase method. In other words, it is possible to select information such as the price of the buy-out forecast data and whether to pay at a fixed amount / percentage of the acquisition amount, prepayment or postpayment.
The prediction method can be selected if there is a method that the buyer particularly trusts among the prediction methods D105 of the buy eye prediction data.
The target period can be selected from the past results, such as the previous week, the past month, three months, one year, etc., when searching for the hit rate of the seller of the prospective buyer.
By filtering with each of these attributes, you can obtain search results that match the type of ticket you are looking for.

Referring to FIG. 6, the buying eye listing unit 124 converts the actual data into browsing data based on the seller ID that matches the search condition, and transmits the browsing data to the web server unit 126. The converted result data can be displayed on the web browser of the buyer terminal 21-1.
It is also possible to display rankings, and the results data is ranked according to the ranking of hit rate results, recovery rate results, recovery amount results, sales amount results, etc., and converted to browsing data for display. You can also.
Further, in the browser display screen example of FIG. 6, by selecting a seller ID link 700 or the like, it is possible to browse in detail the purchase eye prediction that the seller has exhibited.
Further, when the seller of the ID has exhibited the purchase eye forecast data described later, the purchase data purchase screen is displayed by clicking a link 710 or the like. Thereby, the buyer can purchase the purchase eye forecast data via the server 10.

(Step S104)
Next, the form unit 121 performs form distribution processing.
Specifically, after the analysis result disclosure process, the seller is further provided with a prediction of a new purchase. At this time, the seller is authenticated by the ID and password. Here, in the case of a user who desires a new seller, an ID creation unit 135 is used to create a seller ID, and the user database 131 is accessed to input each information.
When the authentication is completed, the form unit 121 transmits a web form in a predetermined format for inputting the buy-ahead forecast data according to the access from the web browser of the seller terminal 23-1 (timing T101).

When the browser of the seller terminal 23-1 receives and displays the web form, the user who wishes to be a seller inputs buy eye prediction data using the web form.
Here, with reference to FIG. 7, the example which inputs this buy-up forecast data specifically using the web form which is an input form is demonstrated.
In the screen example of FIG. 7, first, a user name and a password are input to the web form displayed on the browser using the input field 500.
The seller then enters the input field 510 into the target game D101, the input field 520 into the voting ticket type D102, the input field 530 into the buying item D103, the input field 540 into the amount D104, and the input field 550 into the prediction method D105. The input field 560 inputs data corresponding to the confidence level D106, and the input field 570 inputs data corresponding to the comment D107.
After that, when it is detected that the seller clicks the transmission button 580 with a pointing device such as a mouse, the seller terminal 23-1 transmits the input data to the server 10 as the expected purchase data (timing T102).
When the form part 121 receives the buy eye forecast data via the network input / output part 110, the form part 121 stores it in the results database 132 in association with the seller's ID.
The distribution of the form and the purchase eye forecast data can be repeated because there are usually a plurality of forecasts created by the seller.
Note that the input items of each attribute can be input using selection fields such as a slide bar, radio box, check box, etc., numerical values, or symbols so as not to leave ambiguity.

(Step S105)
Next, the buying eye listing unit 124 performs buying eye market listing processing.
Specifically, the buying eye listing unit 124 uses the above-mentioned ranking of the portion of the buying eye forecast data according to each seller ID that may be disclosed, for example, information other than the seller ID, the buying eye D103, and the like. The information is combined into HTML, XML, etc., and stored in the performance database 132 as exhibition data.
When the buyer terminal 21-1 accesses these exhibition data, the web server unit 126 transmits them.
Further, the exhibition data can be searched in the same manner as the above-mentioned record data.
The buyer searches the exhibition data using the web browser of the buyer terminal 21-1, compares and examines the analysis result and ranking of the actual data, and determines the purchase.
Upon receiving this, the settlement unit 127 presents the contract conditions and makes a “purchase reservation” (timing T103).

(Step S106)
Next, the settlement unit 127 performs a sales process.
In the case of postpay, the settlement unit 127 that has received the purchase reservation signal makes a purchase reservation with another server in cooperation with an off-site ticketing site or the like. This purchase reservation is preferably configured to be canceled only via the server 10. For example, the ID creation unit 135 can create and store an ID, a password, and the like, make a purchase reservation via the ID, a password, and the like, and store them in the user database 131. The ID and password at this time are stored in a storage unit that the user of the user database 131 cannot access, and the buyer can know only that the purchase has been made.
Further, in the case of prepaid, the settlement unit 127 can also settle the price by referring to the user database 131 using SSL or the like.
Thereafter, the settlement unit 127 provides the purchaser with “buy eye prediction” information to the buyer through the web server unit 126 or the DNS / mail server 140 (timing T104).

(Step S107)
Next, the result acquisition unit 125 performs a result acquisition / collation process.
In this process, first, the result acquisition unit 125 fetches the data of the result of public competition from another server connected to the network 5 when the competition result of the target competition of the exhibited buy eye prediction data is found. Get in. This acquisition can be performed by setting the timing with a timer. In addition, the manager of the server 10 can directly input the competition result using the manager terminal 25.
Then, the result acquisition unit 125 compares the acquired result data with the exhibited buy-ahead prediction data, and collates whether the result matches.
The result acquisition unit 125 stores the collated data in the result database 132.

(Step S108)
Next, the performance analysis unit 122 performs performance totaling / analysis processing.
This performance aggregation / analysis process is a process of calculating, analyzing, and evaluating the seller's performance again after the results are known.
Specifically, the performance analysis unit 122 performs the same processing as the past performance tabulation processing in step S101 and the statistics / ranking creation processing in step S102, with the above-described collated data added.
As a result, the performance of the seller of each ID is updated and stored in the performance database 132.

(Step S109)
Next, using the web server unit 126, the buying eye listing unit 124 performs the latest result disclosure process.
This latest result release process performs the same process as the result release process in step S103. That is, when the buyer user browses, the buying eye listing unit 124 uses the web server unit 126 to transmit each calculation result, ranking HTML, XML, or the like.
In addition to this, if multiple sellers have submitted buy-ahead forecast data in the same public competition, each race and daily ranking can be created and output as “breaking news”, DNS / It can also be transmitted by the mail server 140.

(Step S110)
Next, the settlement unit 127 performs price collection / sales payment processing.
Specifically, in the case of postpay, the settlement unit 127 first collects the purchase price from the buyer (timing T105).
The settlement unit 127 performs settlement with reference to the user database 131 using SSL or the like in the case of prepaid collection.
Thereafter, the settlement unit 127 deducts the fee and remits (pays) the sales to the seller (timing T106).
Further, in the case of advance payment, the purchase price that was first settled and collected is subtracted from the fee of the administrator of the server 10 and sent as sales after the result is disclosed. As a result, it is possible to prevent the seller's behavior to receive a charge by simply listing random buy-and-forecast forecast data first, thereby making the seller feel responsible.
Further, even when the settlement unit 127 is set to send in advance payment after the result is disclosed, the settlement unit 127 can remit the money by deducting the fee.

With the configuration described above, the following effects can be obtained.
First, the conventional technique 1 uses simple prediction data as described above, and cannot buy and sell the buy eye prediction data as a product. For this reason, there was a problem that it was difficult to compare the actual results as to whether or not the prediction was actually made.
On the other hand, the open market system according to the embodiment of the present invention allows the seller to provide buy eye forecast data using a form. Thereby, it is possible to clarify information regarding whether or not the buy eye prediction data is matched with the result of the competition, that is, whether the prediction is correct, that is, whether or not it has been hit. In addition, this information can be aggregated and published as results, and the hit rate and the like can be disclosed by analysis.
Therefore, the transparency and convenience of the prediction data can be improved. This makes it easier for buyers to analyze, compare and review seller performance.
Moreover, the open market system which concerns on embodiment of this invention can also improve transparency and convenience by specifying a transaction form and a price.
These can remove psychological barriers to buyer purchases.
Transparency can be further improved by unifying the transaction form such as prepaid and postpaid.

In addition, since the forecast data used in the past varied in terms of transaction type and price depending on the seller, it was unclear and a high barrier to entering as a new buyer.
Furthermore, even if the conventional technique 1 is used, there is no objective evaluation, so in order to enter as a seller, there are many things such as establishment of stores and Internet sites, advertisements, establishment of sales collection methods, etc. It required initial cost, labor, and time. For this reason, the barriers to entry of sellers were high and not everyone could enter.
On the other hand, since the seller uses the open market system according to the embodiment of the present invention, it is only necessary to input a purchase by a form, so that the initial cost, labor, and time are hardly required. In addition, since the buy eye forecast data is objectively and objectively evaluated according to the ranking and performance, a seller who has made a “correct” buy eye forecast can surely earn income. Therefore, the seller can be motivated to make a better prediction.
As a result, there is an effect that anyone can easily enter the expected buying market of public competition. As a result, it is expected that the prospective buying market is activated and the buyer can obtain more accurate buying eye forecast data.

In the past, the reliability of forecast data in operations where “prediction data” itself, which predicts some numerical result related to monetary and social benefits for complex events, has commercial value. And value evaluation was a difficult problem.
Such events include predictions of winning and losing in public competitions, predictions of prices of various receivables, etc. whose prices are determined by the “market” as collective knowledge of external factors, market conditions, speculation of each person, etc. There are win-loss predictions.
The open market system according to the embodiment of the present invention can be applied to these by allowing sellers to list events that can be specifically evaluated as buy eye forecast data.
For example, in the case of a stock price, a portfolio or the like that is a combination of several brands that rise or fall can be evaluated as buy eye forecast data.
Also, in the case of an election, it is possible to buy and sell predictions for winning or losing in the constituency as buying eye prediction data.
In addition, the open market system according to the embodiment of the present invention can select a predetermined purchase when applied to a public competition, but in other cases, the purchase itself is created in a permutation and combination. A purchase eye creation unit (buy eye creation means) may be provided.

  Note that the configuration and operation of the above-described embodiment are examples, and it is needless to say that the configuration and operation can be appropriately changed and executed without departing from the gist of the present invention.

  INDUSTRIAL APPLICABILITY The present invention can be used industrially to provide an open market system that can objectively evaluate buy eye forecast data, thereby realizing an expected buy eye market for public competition.

5 Network 10 Servers 21-1 to 21-n Buyer terminal 23-1 to 23-n Seller terminal 25 Administrator terminal 110 Network input / output unit 120 Open market server 121 Form unit 122 Performance analysis unit 124 Buy item listing unit 125 Result acquisition Unit 126 Web server unit 127 Settlement unit 130 Database server 131 User database 132 Performance database 133 Competition database 135 ID creation unit 140 DNS / mail servers 500, 510, 520, 530, 540, 550, 560, 570, 610, 620, 630 , 640, 650 Input field 580, 670 Send button 700, 710 Link 680 Clear button X Open market system

Claims (6)

In an open market system comprising a server, a seller terminal connected to the server, and a buyer terminal,
The server
A results analysis unit that collates and analyzes the results of past purchase forecast data and creates results data;
A form part for outputting a form for inputting buy eye forecast data to be exhibited from the seller terminal;
A buy-and-sale section that outputs the analyzed data of the results and the forecasted buy-and-buy forecast data to the buyer terminal;
A settlement unit that performs settlement related to the bought eye forecast data exhibited,
The seller terminal is
An open market system characterized by inputting buy-and-buy forecast data standardized using the form for listing. The open market system according to claim 1, wherein as the analysis, the performance analysis unit creates statistics data by calculating statistics and rankings. The open market system according to claim 1 or 2, wherein the buying eye listing unit searches for the data of the results in accordance with an instruction from the buyer terminal. The server
And further comprising a result obtaining unit for obtaining a result of the exhibited buy eye forecast data,
4. The disclosure according to claim 1, wherein the actual result analysis unit collates the bought eye forecast data with the result data and updates the actual data. Market system. A results analysis unit that collates and analyzes the results of past purchase forecast data and creates results data;
A form part that outputs a form for inputting buy-out forecast data to be exhibited from the seller terminal,
A buy-and-sell section that outputs the analyzed data of the results and the buy-and-buy forecast data that has been listed;
A server of an open market system, comprising: a settlement unit configured to perform settlement related to the bought eye forecast data exhibited. In an open market system comprising a server, a seller terminal connected to the server, and a buyer terminal,
Check the purchase forecast data and the results of the buy forecast data and analyze them to create actual data,
Output a form to enter the buying eye forecast data to be listed,
Enter the purchase forecast data that was stylized using the form and submit it,
A control method for an open market system, characterized in that the analyzed result data and the bought eye forecast data are output.

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