Information processing device, information processing method, and control program

Abstract

In order to solve the problem of assessing the value of a seller's data, an information processing device (1) is provided with: an augmentation means (21) that augments target data (D1) to be analyzed owned by a buyer (B) using public data (40) owned by a seller (S) that has been published or is scheduled to be published; and an estimation means (22) that estimates the degree of value improvement (46) of a second analysis result (45), which is the result of analyzing augmented target data (43), relative to a first analysis result (44), which is the result of analyzing target data (41), which is the original version of the augmented target data (43) before the augmentation.

Description

[Technical field]

[0001] The present invention relates to a technique for evaluating the value of data. [Background technology]

[0002] When target data held by one company is combined with other data held by another company and analyzed, it is possible to obtain analysis results that are highly useful to a particular user (for example, the holder of the above-mentioned target data). Hereinafter, other data that can be combined with the target data to improve the value of the analysis results of the target data is referred to as "analysis support data."

[0003] However, it is not generally expected that companies will willingly provide their data to other companies.

[0004] Here, if the data that can be used as analysis support data is public data that has already been made public or is scheduled to be made public, it is likely that companies will not hesitate to provide their own data to other companies.

[0005] However, it is believed that various companies possess huge amounts of public data in various formats. Therefore, even if public data can be used as analysis support data, it is not easy to determine its fair value.

[0006] For example, Patent Document 1 describes a data analysis system that classifies a data group related to a web page, evaluates the degree of recommendation to a user, and makes a recommendation to the user.

[0007] Also, Non-Patent Document 1 describes a method for determining the selling price of a geoinformation product (GIP). According to the above-mentioned method, the selling price is determined taking into consideration the costs involved in data production work, such as collection, synthesis, and maintenance of the data that is the basis of the geographical information product. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] International Publication No. WO2016 / 103519 [Non-patent literature]

[0009] [Non-Patent Document 1] Frank, A.. “PRICE DETERMINATION FOR GEOGRAPHIC DATA.” (1999) [Non-Patent Document 2] “About Causal Analysis Technology”, [online], NEC Corporation, [Retrieved March 5, 2021], Internet (URL: https: / / jpn.nec.com / press / 202010 / images / 0201-01-01.pdf) [Non-Patent Document 3] Ding, Rui et al. “QuickInsights: Quick and Automatic Discovery of Insights from Multi-Dimensional Data.” Proceedings of the 2019 International Conference on Management of Data (2019) Summary of the Invention [Problem to be solved by the invention]

[0010] None of the prior art documents describe the evaluation of the value of public data from the perspective of the benefits provided to the buyer. In order to quantitatively evaluate the value of the seller's public data, it is necessary to estimate the benefits provided to the buyer by utilizing the public data in the analysis of the target data.

[0011] One aspect of the present invention is to provide a technique for assessing the value of data by estimating the benefits to a buyer that can be derived from utilizing a seller's data. [Means for solving the problem]

[0012] An information processing device according to one aspect of the present invention includes an expansion means for expanding target data held by a buyer to be analyzed using public data held by a seller that has been made public or is scheduled to be made public, and an estimation means for estimating the degree of value improvement of a second analysis result, which is the result of analyzing the target data after it has been expanded, relative to a first analysis result, which is the result of analyzing the target data before it is expanded.

[0013] A data distribution method according to one aspect of the present invention includes at least one processor determining a sales price for a service of analyzing the expanded target data based on the profit a buyer will gain when target data held by a buyer to be analyzed is expanded and analyzed using public data held by a seller that has been made public or is planned to be made public, supplying analysis result data to the buyer, which is the result of analyzing the target data expanded using the public data, in return for a remuneration equivalent to the sales price, and paying the seller a remuneration equivalent to a portion of the sales price as consideration for the use of the public data.

[0014] An information processing method according to one aspect of the present invention includes at least one processor expanding target data to be analyzed held by a buyer using public data held by a seller that has been made public or is scheduled to be made public, and estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been expanded, relative to a first analysis result, which is a result of analyzing the target data before it is expanded.

[0015] A control program according to one aspect of the present invention causes a computer to execute an extension process for extending target data held by a buyer to be analyzed, using public data held by a seller that has been made public or is scheduled to be made public, and an estimation process for estimating the degree of value improvement of a second analysis result, which is the result of analyzing the target data after it has been expanded, relative to a first analysis result, which is the result of analyzing the target data before it is expanded. Effect of the Invention

[0016] According to one aspect of the present invention, the value of data can be assessed. [Brief description of the drawings]

[0017] [Figure 1] 1 is a block diagram showing a configuration of a main part of an information processing device according to a first exemplary embodiment of the present invention. [Diagram 2] 1 is a flowchart showing a process flow of an information processing method according to the first exemplary embodiment of the present invention. [Diagram 3] FIG. 11 is a diagram showing a schematic configuration of a data distribution system according to an exemplary embodiment 2 of the present invention. [Figure 4] 11 is a flowchart showing the flow of a data distribution method according to an exemplary embodiment 2 of the present invention. [Diagram 5] FIG. 11 is a block diagram showing a configuration of a main part of an information processing device according to an exemplary embodiment 3 of the present invention. [Figure 6] FIG. 13 is a diagram showing an example of an assessment request screen. [Figure 7] FIG. 13 is a diagram showing an example of an appraisal result screen. [Figure 8] FIG. 13 is a diagram showing an example of a search request screen. [Figure 9] FIG. 13 is a diagram illustrating an example of a search result screen. [Figure 10] FIG. 11 is a diagram illustrating an example of a detailed configuration of an extension unit of an information processing device according to an exemplary embodiment 3 of the present invention. [Figure 11] 11 is a diagram showing an example of a data structure of various data input / output in an extension unit; FIG. [Figure 12] FIG. 11 is a diagram illustrating an example of a detailed configuration of an estimation unit of an information processing device according to an exemplary embodiment 3 of the present invention. [Figure 13] FIG. 13 is a diagram illustrating an example of a data structure of pre-extension data in a modified example. [Figure 14] FIG. 11 is a diagram showing an example of a data structure of first causal relationship data (first analysis result) output by an analysis unit of an information processing device according to an exemplary embodiment 3 of the present invention. [Figure 15] FIG. 13 is a diagram showing an example of a data structure of expanded data in a modified example. [Figure 16] FIG. 11 is a diagram showing an example of a data structure of second causal relationship data (second analysis result) output by an analysis unit of an information processing device according to an exemplary embodiment 3 of the present invention. [Figure 17] FIG. 11 is a diagram showing an example of a data structure of purchase record information stored in a storage unit of an information processing device according to an exemplary embodiment 3 of the present invention. [Figure 18] FIG. 11 is a sequence diagram showing the flow of processing in an information processing method according to an exemplary embodiment 3 of the present invention. [Figure 19] FIG. 2 is a block diagram showing an example of a hardware configuration of an information processing device in each exemplary embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] Example Embodiment 1 A first exemplary embodiment of the present invention will be described in detail with reference to the drawings. This exemplary embodiment is a basic form of the exemplary embodiments described later.

[0019] <Configuration of information processing device> Fig. 1 is a block diagram showing a configuration of an information processing device 1 according to this exemplary embodiment. As shown in Fig. 1, the information processing device 1 includes an extension unit 21 and an estimation unit 22. The extension unit 21 is a configuration that realizes the extension means in this exemplary embodiment. The estimation unit 22 is a configuration that realizes the estimation means in this exemplary embodiment.

[0020] The expansion unit 21 expands the target data to be analyzed that is held by the buyer, using public data that is held by the seller and has been made public or is scheduled to be made public.

[0021] The estimation unit 22 estimates the degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been expanded, relative to a first analysis result, which is a result of analyzing the target data before it has been expanded.

[0022] In the following, the target data before being extended is referred to as pre-extension data, and the target data after being extended is referred to as post-extension data.

[0023] The degree of value improvement of the second analysis result is an index showing how much more useful and meaningful the second analysis result is to the buyer who holds the target data compared to the first analysis result. The first analysis result is an analysis result obtained as a result of analyzing the pre-expansion data currently held by the buyer without using public data. The second analysis result is an analysis result obtained as a result of analyzing the post-expansion data that has been expanded using public data. In other words, the degree of value improvement indicates the benefit to the buyer, specifically, the usefulness of the public data held by the seller as analysis support data.

[0024] The information processing device 1 may be realized by a computer and a control program. The above-mentioned control program is a control program that causes the above-mentioned computer to function as the above-mentioned extension unit 21 and the estimation unit 22.

[0025] <Processing flow of information processing method> 2 is a flowchart showing a process flow of an information processing method according to this exemplary embodiment. The information processing method is executed by the information processing device 1.

[0026] In step S1 (extension process), the extension unit 21 extends target data to be analyzed that is held by a buyer, using public data that is held by a seller and has been made public or is scheduled to be made public.

[0027] In step S2 (estimation process), the estimation unit 22 estimates the degree of value improvement of the second analysis result, which is the result of analyzing the expanded data, relative to the first analysis result, which is the result of analyzing the pre-expansion data.

[0028] As described above, the information processing device 1 according to this exemplary embodiment is configured to include an expansion unit 21 that expands the target data to be analyzed held by the buyer using public data held by the seller that has been made public or is scheduled to be made public, and an estimation unit 22 that estimates the degree of value improvement of the second analysis result, which is the result of analyzing the target data after it has been expanded, compared to the first analysis result, which is the result of analyzing the target data before it is expanded.

[0029] In addition, the information processing method according to this exemplary embodiment has a configuration including: at least one processor expanding target data to be analyzed held by the buyer using public data held by the seller that has been made public or is scheduled to be made public; and at least one processor estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been expanded, relative to a first analysis result, which is a result of analyzing the target data before it has been expanded.

[0030] Therefore, according to the information processing device 1 and the information processing method according to the present exemplary embodiment, it is possible to quantitatively estimate the benefit of the buyer brought by utilizing the seller's public data in the analysis of the buyer's target data as the degree of value improvement. As a result, it is possible to obtain an effect that the value of the seller's public data can be evaluated from the viewpoint of the benefit brought to the buyer.

[0031] Exemplary embodiment 2 A second exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the first exemplary embodiment are given the same reference numerals and will not be described repeatedly.

[0032] <Outline of the data distribution system> FIG. 3 is a diagram showing an example of a data distribution system 100 to which the information processing device 1 of the present disclosure is applied.

[0033] The data distribution system 100 is a system for distributing public data held by a seller. The data distribution system 100 includes at least the information processing device 1 of the present disclosure.

[0034] Entities that use the data distribution system 100 include buyers who purchase public data, sellers who sell public data, and service providers who act as intermediaries between buyers and sellers.

[0035] As an example, the service provider may be a service provider P that provides an analysis service to a buyer. Specifically, the above-mentioned analysis service provided by the service provider P is a service that combines target data D1 held by the buyer with public data D2, analyzes the combined data, and provides the buyer with analysis result data D3 that is more useful to the buyer.

[0036] Furthermore, the service provider P owns an information processing device 1. The information processing device 1 has a function of evaluating the market value of the public data of a seller in order to distribute the public data in the data distribution system 100, that is, a function of appraising the public data.

[0037] The information processing device 1 determines the selling price of the above-mentioned analysis service, i.e., the selling price of the public data D2, using at least one processor. The information processing device 1 determines the selling price based on the profit that the buyer will gain when the target data D1 to be analyzed, which is held by the buyer, is expanded and analyzed using the public data D2 held by the seller that has been published or is scheduled to be published. The information processing device 1 may be, for example, the information processing device 1 according to the exemplary embodiment 1, or may be the information processing device 1 according to each exemplary embodiment described later.

[0038] The buyer may be, for example, a buyer company B that wishes to carry out more advantageous economic activities by utilizing the analysis results obtained by analyzing the target data D1 of the buyer company. The buyer company B may own a buyer terminal 2 as necessary.

[0039] For example, the seller may be a seller company S that desires to make a profit from the sale of its public data D2 by having its public data D2 utilized as analysis support data. The seller company S may own a seller terminal 3 as necessary.

[0040] The service provider P supplies the buyer company B with analysis result data D3, which is the result of analyzing the target data D1 extended using the public data D2, in return for a remuneration equivalent to the selling price determined by the information processing device 1. The remuneration equivalent to the determined selling price may be paid from the buyer company B to the service provider P as, for example, a usage fee for the analysis service, as shown in FIG.

[0041] Then, the service provider P pays the seller a remuneration equivalent to a part of the selling price as compensation for using the public data D2 as analysis support data. The remuneration equivalent to a part of the selling price may be paid from the service provider P to the seller company S as a usage fee for the public data, for example, as shown in FIG.

[0042] According to the above-mentioned data distribution system 100, the buyer company B can obtain more substantial and useful analysis result data D3 than if the buyer company B had analyzed the target data D1 in-house, in exchange for paying the above-mentioned sales price, and can therefore carry out advantageous economic activities. The sales price is determined based on the profits the buyer is expected to obtain by utilizing the public data D2, and is therefore considered to be a reasonable sales price for the buyer company B.

[0043] Service provider P can determine a reasonable selling price and, as an intermediary between buyer company B and seller company S, earn a profit from the difference between the fee for the analysis service received from buyer company B and the fee for the use of the public data paid to seller company S.

[0044] By having buyer company B utilize public data D2, which is made up of existing content that can be made public, as analysis support data, seller company S can obtain profits without incurring additional costs or leaking confidential information to other companies.

[0045] As described above, it will be possible to create a market in which public data can be circulated as a commodity.

[0046] <Data distribution method processing flow> 4 is a flowchart showing the flow of a data distribution method executed in the data distribution system 100. The following data distribution method is executed by at least one processor functioning as the information processing device 1, for example.

[0047] In step S11 (sales price determination process), the information processing device 1 determines the sales price of the service of analyzing the expanded data based on the profit that the buyer will obtain when the buyer's target data is expanded and analyzed using the public data held by the seller. The profit that the buyer will obtain is determined by the information processing device 1 based on the information processing method described in the first exemplary embodiment.

[0048] In step S12 (supply process), the information processing device 1 supplies the buyer company with analysis result data, which is the result of analyzing the extended data extended using the public data, in return for a fee equivalent to the determined selling price.

[0049] In step S13 (payment process), the information processing device 1 pays the seller a remuneration equivalent to a part of the selling price as consideration for the use of the above-mentioned public data.

[0050] As described above, the data distribution method according to this exemplary embodiment has a configuration including: determining, by at least one processor, a sales price for a service of analyzing the expanded target data based on the profit that the buyer will gain when the target data to be analyzed, held by the buyer, is expanded and analyzed using public data held by the seller that has been made public or is scheduled to be made public; supplying the buyer with analysis result data that is the result of analyzing the target data expanded using the public data in return for a remuneration equivalent to the sales price; and paying the seller a remuneration equivalent to a portion of the sales price in return for the use of the public data.

[0051] Therefore, according to the data distribution method of this exemplary embodiment, in addition to the effect of the information processing device 1 of exemplary embodiment 1, the effect of being able to create a market for distributing public data as a commodity can be obtained.

[0052] (Variations in Data Distribution Method) The exchange of analysis result data in S12 described above may be performed electronically between the information processing device 1 and the buyer terminal 2, or may be performed physically between the service provider and the buyer company using other means.

[0053] The process in which the service provider receives the above-mentioned remuneration equivalent to the selling price from the buyer company may be executed before or after S12. Moreover, the above-mentioned remuneration may be exchanged electronically between the buyer terminal 2 and the information processing device 1, or may be physically executed between the buyer company and the service provider using other means.

[0054] The process in which the information processing device 1 acquires the public data of the seller company is assumed to be executed at any timing before S12. The information processing device 1 does not have to acquire the public data directly from the seller terminal 3. The information processing device 1 may acquire the public data from another storage device that stores the public data that has already been made public.

[0055] The payment process in S13 may be executed before or after the process of acquiring the public data described above. In addition, the payment process in S13 may be executed electronically between the information processing device 1 and the seller terminal 3, or may be executed physically between the service provider and the seller company using other means.

[0056] The public data D2 may be, for example, web page data constituting the website of seller company S. In addition to utilizing the web page data as a means of public relations, seller company S can make a profit by selling the web page data, which has useful value as analysis support data, to buyer company B. Since the web page data has already been widely released to the public, seller company S is not expected to bear any particular risk in selling it to buyer company B. Furthermore, seller company S has the advantage that it only needs to provide existing web page data to service provider P, and can make a profit without incurring additional costs, such as the cost of processing the web page data for sale.

[0057] The analysis tool used by the service provider P to obtain the analysis result data D3 may be owned by the service provider P itself, or may be owned by another company. The service provider P may also outsource the analysis process to obtain the analysis result data D3 to another company. In this case, the information processing device 1 may estimate the degree of value improvement of the analysis result data D3 for each analysis processing company, and select an analysis processing company that provides the optimal analysis tool or analysis process as the outsourcing company. The service provider P may pay a part of the usage fee for the analysis service received from the buyer company B to the analysis processing company as compensation for receiving the analysis result data D3 from the analysis processing company to provide to the buyer company B.

[0058] Exemplary embodiment 3 A third exemplary embodiment of the present invention will be described in detail with reference to the drawings. Note that components having the same functions as those described in the above exemplary embodiments are denoted by the same reference numerals, and the description thereof will not be repeated.

[0059] In this exemplary embodiment, as an example, the information processing device 1 is applied to the data distribution system 100 described in the exemplary embodiment 2. That is, the information processing device 1 belongs to a service provider P. Also, the information processing device 1 is connected to a buyer terminal 2 belonging to a buyer company B and a seller terminal 3 belonging to a seller company S so as to be able to communicate with each other via a communication network such as the Internet.

[0060] In this exemplary embodiment, the data distribution system 100 can receive an instruction to start evaluating the public data of the seller from a user of the data distribution system 100 and present the evaluation results to the user. The data distribution system 100 can also receive an instruction from the user to search for public data that will increase the value of the analysis results of the buyer's target data and present the search results to the user.

[0061] Users of the data distribution system 100 are assumed to be an operator of service provider P who directly operates the information processing device 1, an operator of buyer company B who operates buyer terminal 2, and an operator of seller company S who operates seller terminal 3.

[0062] <About the user interface> (Configuration of information processing device) 5 is a block diagram showing the configuration of the information processing device 1 according to this exemplary embodiment. As shown in FIG. 5, the information processing device 1 includes a control unit 10, a storage unit 11, an operation unit 12, a communication unit 13, and a display unit 14.

[0063] The control unit 10 is configured with, for example, a calculation device such as a CPU (central processing unit) or a dedicated processor. Each part of the control unit 10, which will be described later with reference to FIG. 5, is configured by the above-mentioned calculation device operating in conjunction with a storage device (for example, a memory Department This can be achieved by reading the program stored in the microcomputer 11) into RAM (random access memory) or the like and executing it.

[0064] The storage unit 11 stores various data used by the control unit 10. In this exemplary embodiment, the storage unit 11 stores a database of target data (hereinafter, target data DB 111) in a non-volatile manner. The storage unit 11 may also store a database of public data (hereinafter, public data DB 112) in a non-volatile manner. The storage unit 11 may also store purchase history information 113 in a non-volatile manner. The storage unit 11 may be configured as an external storage device accessible by the information processing device 1.

[0065] The operation unit 12 is an input device for receiving an input operation by a user who operates the information processing device 1. The operation unit 12 inputs, to the control unit 10, an instruction signal corresponding to the received input operation.

[0066] The display unit 14 is an output device that presents information processed by the control unit 10 in a visually recognizable manner to a user of the information processing device 1. For example, the display unit 14 is configured with a liquid crystal display (LCD; Liquid Crystal Display) or an organic EL (Electro-Luminescence) display. As an example, the display unit 14 may form a touch panel together with the operation unit 12.

[0067] With the above-mentioned configuration, an operator of the service provider P who directly operates the information processing device 1 can perform various operations and obtain information from the information processing device 1 as a user of the data distribution system 100. It is assumed that there is no operator of the service provider P who directly operates the information processing device 1, and the information processing device 1 operates as a server device for client devices such as the buyer terminal 2 and the seller terminal 3. In such a case, the operation unit 12 and the display unit 14 may be omitted as appropriate. In this case, the operator who operates the buyer terminal 2 or the seller terminal 3 can be regarded as a user of the data distribution system 100.

[0068] The communication unit 13 is a communication device for communicating with other devices such as the buyer terminal 2 and the seller terminal 3 via a communication network such as the Internet.

[0069] As an example, in addition to the extension unit 21 and the estimation unit 22 described in exemplary embodiment 1, the control unit 10 may further include one or more of a data acquisition unit 23, an output control unit 24, a price calculation unit 25 and an analysis unit 26.

[0070] The data acquisition unit 23 is configured to realize a data acquisition means in this exemplary embodiment. The output control unit 24 is configured to realize an output control means in this exemplary embodiment. The price calculation unit 25 is configured to realize a price calculation means in this exemplary embodiment. The analysis unit 26 is configured to realize an analysis means in this exemplary embodiment. The analysis unit 26 may be configured to realize, as an analysis means, a statistical causal search means described in Non-Patent Document 2, for example.

[0071] The data acquisition unit 23 acquires data specified by the user according to an input instruction from the user. For example, the data acquisition unit 23 may acquire public data 40 specified by the user. As an example, the public data 40 may be webpage data constituting the website of the seller company S. In this case, the data acquisition unit 23 acquires the webpage data based on a uniform resource locator (URL) specified by the user.

[0072] The output control unit 24 outputs the degree of value improvement of the second analysis result obtained by the target data after it has been expanded using the acquired web page data, for each buyer who holds the target data.

[0073] In this exemplary embodiment, multiple target data for each of multiple buyer companies B may be registered in advance in the target data DB 111. The estimation unit 22 can estimate the degree of value improvement of the second analysis result based on the web page data acquired as described above for each registered target data of the buyer company B.

[0074] The output control unit 24 controls each unit responsible for output operations so that at least the value improvement degree for each target data of the buyer company B is presented to the user as the assessment result of the web page data. For example, the output control unit 24 may present the assessment result to the user of the information processing device 1 by displaying the value improvement degree for each target data on the display unit 14. In another example, the output control unit 24 may transmit the assessment result to the seller terminal 3 via the communication unit 13, and present the assessment result to the user of the seller terminal 3 by displaying the assessment result on the display unit of the seller terminal 3.

[0075] In another example, the data acquisition unit 23 may acquire target data designated by a user. For example, the data acquisition unit 23 may acquire target data from a storage location based on the storage location of the target data designated by a user, and register the target data in the target data DB 111. Alternatively, the data acquisition unit 23 may receive target data transmitted from the buyer terminal 2, and register the target data in the target data DB 111. Furthermore, the data acquisition unit 23 may read out the target data designated by the user from the target data DB 111.

[0076] The output control unit 24 outputs the degree of value improvement of the second analysis result obtained by expanding the acquired target data using the web page data for each seller who owns the web page data.

[0077] In the other example described above, a plurality of web page data for each of a plurality of seller companies S may be registered in advance in the public data DB 112. For example, the data acquisition unit 23 may periodically crawl the website of each seller company S using a crawler technique or the like, acquire the web page data constituting the website, and register the acquired web page data in the public data DB 112. The estimation unit 22 can estimate the degree of value improvement of the second analysis result obtained by expanding the target data acquired as described above using the web page data for each registered web page data of the seller company S.

[0078] The output control unit 24 controls each unit responsible for output operations so that at least the value improvement degree for each web page data of the seller company S is presented to the user as a search result of the web page data. For example, the output control unit 24 may present the search result to the user of the information processing device 1 by displaying the value improvement degree for each web page data on the display unit 14. In another example, the output control unit 24 may transmit the search result to the buyer terminal 2 via the communication unit 13, and present the search result to the user of the buyer terminal 2 by displaying the search result on the display unit of the buyer terminal 2.

[0079] The estimation unit 22 may estimate the value improvement degree for each of all target data registered in the target data DB 111 based on the specified web page data. Alternatively, the estimation unit 22 may estimate the value improvement degree for each of some target data extracted based on a predetermined rule from among the target data registered in the target data DB 111. The estimation unit 22 may, for example, extract target data having a similarity to the specified web page data of a predetermined value or more to estimate the value improvement degree. The similarity between the web page data and the target data may be determined based on the number of matching keywords among the keywords included in each data. The web page data and the target data may include data of any format, such as image data and audio data, without being limited to text data. For example, the similarity may be determined to be high based on the fact that the content depicted in the image data included in the web page data and the keywords included in the target data refer to the same thing.

[0080] The estimation unit 22 may estimate the value improvement degree for each of all web page data registered in the public data DB 112 based on the specified target data. Alternatively, the estimation unit 22 may estimate the value improvement degree for each part of web page data extracted based on a predetermined rule from among the target data registered in the public data DB 112. The estimation unit 22 may, for example, extract web page data having a similarity to the specified target data that is equal to or greater than a predetermined value, and estimate the value improvement degree.

[0081] (Screenshot) 6 is a diagram showing an example of an assessment request screen 50 presented to a user. The output control unit 24 generates the assessment request screen 50 and presents the generated assessment request screen 50 to the user. The assessment request screen 50 is a user interface (UI) for a user to perform an operation to request an assessment of the public data 40 from the information processing device 1. The output control unit 24 may display the assessment request screen 50 on the display unit 14 of the information processing device 1 in order to provide the UI to a user of the service provider P. The output control unit 24 may communicate with the seller terminal 3 via the communication unit 13 and display the assessment request screen 50 on the display unit of the seller terminal 3 in order to provide the UI to a user of the seller company S.

[0082] As an example, the appraisal request screen 50 may include an area 60 for inputting the URL of a website having web page data to be appraised. For example, the operator of the seller terminal 3 inputs the URL of the company's website for which the selling price is desired in the area 60, and presses a button 61 for instructing the start of appraisal. In response to this press, the seller terminal 3 transmits an appraisal request including the URL input in the area 60 to the information processing device 1.

[0083] In this manner, the user can operate the appraisal request screen 50 to make an appraisal request for web page data to the information processing device 1.

[0084] 7 is a diagram showing an example of an appraisal result screen 51 presented to a user. The output control unit 24 generates the appraisal result screen 51 and presents the generated appraisal result screen 51 to the user. The appraisal result screen 51 is a UI for providing the user with information about the appraisal result. The output control unit 24 may cause the appraisal result screen 51 to be displayed on the display unit 14 of the information processing device 1, or may cause the appraisal result screen 51 to be displayed on the display unit of the seller terminal 3.

[0085] As an example, the appraisal result screen 51 may display the value improvement degree 46 estimated by the estimation unit 22 arranged for each buyer company B. The sales price calculated based on the value improvement degree 46 may also be displayed together with the value improvement degree 46.

[0086] In this way, the user can know potential buyers of the web page data via the appraisal result screen 51. Furthermore, the user can grasp the market value of the web page data based on the value improvement degree 46 or the selling price displayed together with the potential buyers.

[0087] 8 is a diagram showing an example of a search request screen 52 presented to a user. The output control unit 24 generates the search request screen 52 and presents the generated search request screen 52 to the user. The search request screen 52 is a UI for the user to perform an operation to request the information processing device 1 to search for public data 40 that can be used for analysis in combination with target data. The output control unit 24 may cause the search request screen 52 to be displayed on the display unit 14 of the information processing device 1, or on the display unit of the seller terminal 3.

[0088] As an example, the search request screen 52 may include an area 62 for inputting information specifying the target data to be analyzed. For example, the operator of the buyer terminal 2 drags and drops the target data to be analyzed into the area 62 and presses a button 63 for instructing the start of a search. In response to this press, the buyer terminal 2 transmits a search request including the target data dropped into the area 62 to the information processing device 1. If the target data is registered in advance in the information processing device 1, the area 62 may be an area for inputting identification information for identifying the target data or the buyer company B that is the requester of the search. The information processing device 1 can read out the target data of the requester, the buyer company B, from the target data DB 111 based on the identification information included in the search request.

[0089] In this manner, the user can operate the search request screen 52 to issue a search request to the information processing device 1 for public data 40 that can be combined with the target data.

[0090] 9 is a diagram showing an example of a search result screen 53 presented to a user. The output control unit 24 generates the search result screen 53 and presents the generated search result screen 53 to the user. The search result screen 53 is a UI for providing the user with information about the search results. The output control unit 24 may cause the search result screen 53 to be displayed on the display unit 14 of the information processing device 1, or may cause the search result screen 53 to be displayed on the display unit of the buyer terminal 2.

[0091] As an example, the search result screen 53 may display the value improvement degrees 46 estimated by the estimation unit 22 arranged for each seller company S. Although not shown in the figure, the selling price calculated based on the value improvement degrees 46 may also be displayed together with the value improvement degrees 46.

[0092] In this way, the user can know the candidate sellers of the public data 40 to be combined with the target data via the search result screen 53. Furthermore, the user can quantitatively grasp, for each public data 40, the degree of merit for a buyer when the public data 40 is combined with the target data, based on the value improvement degree 46 displayed together with the candidate sellers.

[0093] (Variations in the way results are displayed) The output control unit 24 may execute the following display method on at least one of the assessment result screen 51 shown in FIG. 7 and the search result screen 53 shown in FIG.

[0094] For example, when displaying a list of candidates for buyer company B or seller company S, the output control unit 24 may display the list of candidates in descending order of similarity between the matched target data of buyer company B and the public data of seller company S. As described above, the similarity may be determined based on the frequency or proportion of text, images, sounds, etc. contained in each data indicating the same thing.

[0095] Alternatively, for example, when displaying a list of candidates for buyer company B or seller company S, the output control unit 24 may display the list of the candidates in order of the estimated degree of value improvement for each candidate.

[0096] Alternatively, for example, when displaying a list of candidates for buyer company B or seller company S, the output control unit 24 may display the list of candidates in descending order of the selling price calculated for each candidate.

[0097] Alternatively, for example, when displaying a list of candidates for buyer company B, the output control unit 24 may display the list of candidates in descending order of the price at which the candidates have previously purchased public data, or in descending order of the number of times that the candidates have purchased public data.

[0098] Alternatively, for example, when displaying a list of candidates for seller company S, the output control unit 24 may display the list of candidates in descending order of the price at which the candidate's public data was previously purchased, or in descending order of the number of times the public data has been sold.

[0099] As described above, the information processing device 1 according to this exemplary embodiment is configured to further include a data acquisition unit 23 that acquires the web page data based on a URL specified by a user, and an output control unit 24 that outputs the degree of value improvement of the second analysis result obtained by the target data after it has been expanded using the acquired web page data, for each buyer who holds the target data.

[0100] In addition, the information processing device 1 according to this exemplary embodiment is configured to further include a data acquisition unit 23 that acquires the target data specified by a user, and an output control unit 24 that outputs, for each seller who holds the web page data, the degree of value improvement of the second analysis result obtained by expanding the acquired target data using the web page data.

[0101] Therefore, according to the information processing device 1 according to this exemplary embodiment, in addition to the effects of the information processing device 1 according to each of the above-mentioned exemplary embodiments, an effect is obtained in that excellent operability can be provided to the user when the user inputs instructions to the information processing device 1. Furthermore, an effect is obtained in that excellent visibility can be provided to the user when the user obtains information from the information processing device 1.

[0102] <About expanding target data> (Configuration of extension part 21) FIG. 10 is a diagram showing an example of a detailed configuration of the extension unit 21 included in the control unit 10 of the information processing device 1. As an example, the extension unit 21 according to this exemplary embodiment extends the target data by adding information related to the target data among information included in the public data 40 to the target data. Hereinafter, information related to the target data among information included in the public data 40 is referred to as related information. For example, as shown in the figure, the extension unit 21 may have an extraction unit 211 and a combination unit 212. The extraction unit 211 is a configuration that realizes an extraction means in this exemplary embodiment. The combination unit 212 is a configuration that realizes a combination means in this exemplary embodiment.

[0103] The extraction unit 211 compares pre-extension data 41 , which is the target data before extension, with the public data 40 , and extracts related information 42 that is related to the pre-extension data 41 from the information included in the public data 40 .

[0104] The combining unit 212 combines the pre-enlargement data 41 with the related information 42 to generate post-enlargement data 43 .

[0105] (Data Structure) 11 is a diagram showing an example of the data structure of various data input and output in the extension unit 21. It is assumed that the data acquisition unit 23 has acquired public data 40 shown in FIG. 11 from the website of the seller company S based on the URL of the seller company S. As an example, the public data 40 is webpage data including multiple pieces of text data as shown in the figure. The public data 40 is input from the data acquisition unit 23 to the extension unit 21.

[0106] On the other hand, pre-expansion data 41 shown in Fig. 11 is input to the expansion unit 21 as target data read from the target data DB 111. As an example, the target data is sales record information of the buyer company B. At the stage of the pre-expansion data 41, this sales record information is assumed to be composed of, for example, two data items, "product name" and "sales" indicating the sales amount.

[0107] The extraction unit 211 of the expansion unit 21 may extract, from the public data 40, information that appears adjacent to a keyword included in the pre-expansion data 41 with a frequency exceeding a predetermined value as related information 42. Then, the combining unit 212 may combine the extracted related information 42 with the pre-expansion data 41 as a new data item.

[0108] As a specific example, the extraction unit 211 extracts keywords from the pre-extension data 41. The extraction unit 211 may extract predetermined morphemes as keywords from the pre-extension data 41 according to a predetermined dictionary, for example. The pre-extension data 41 includes character names that appear in anime or games, such as "Taro Tanaka" and "Piyoyan," in the data item of the product name. The extraction unit 211 may extract such character names as keywords.

[0109] On the other hand, the public data 40 includes text information of "comic" that frequently appears in close proximity to the keyword "Taro Tanaka." The extraction unit 211 can extract the text information of "comic" that frequently appears in close proximity to the keyword "Taro Tanaka" as information related to the pre-expansion data 41. The extraction unit 211 may also extract text information that frequently appears in close proximity to multiple keywords in the pre-expansion data 41 from the public data 40.

[0110] Furthermore, the extraction unit 211 may calculate the adjacent appearance frequency in the public data 40 for each pair of the keyword in the pre-expansion data 41 and the text information included in the public data 40. The extraction unit 211 may output the text information that frequently appears adjacent to the keyword in the pre-expansion data 41 to the combination unit 212 as the related information 42. In the illustrated example, the result is that the text information "comic" frequently appears adjacent to "Taro Tanaka" and the text information "suit" frequently appears adjacent to "Piyoyan". For example, each piece of text information may be determined to have frequently appeared based on the adjacent appearance frequency being 7 or more. Based on this result, the extraction unit 211 may output the text information "comic" and "suit" to the combination unit 212 as the related information 42.

[0111] The combining unit 212 combines the related information 42 input from the extracting unit 211 with the pre-extension data 41 as new data items of the target data. Specifically, the combining unit 212 adds each of the text information "comic" and "suit" as a new data item to the pre-extension data 41. As a result, the expanded target data, i.e., the post-extension data 43, has the data items "comic" and "suit" in addition to "product name" and "sales".

[0112] Furthermore, as shown in the figure, the combination unit 212 may associate, in the expanded data 43, with a pair of a product name including a keyword and text information newly added as a data item, determination flag information on whether or not the pair appears frequently based on the close appearance frequency. For the pair of "Tanaka Taro" and "Comic", the combination unit 212 associates determination flag information of "1", which means "frequently appears", with this pair, based on the close appearance frequency being 7 or more. For the pair of "Tanaka Taro" and "Suit", the combination unit 212 associates determination flag information of "0", which means "not frequently appears", with this pair, based on the close appearance frequency being less than 7.

[0113] (Modification of the extension portion 21) The extraction unit 211 of the expansion unit 21 may extract information in the public data 40 that has a similarity to a keyword included in the pre-expansion data 41 that exceeds a predetermined value as related information 42 from the public data 40. Then, the combining unit 212 may combine the extracted related information 42 with the pre-expansion data 41 as a new data item.

[0114] For example, if text information or image information such as “Yonan Tanaka Jiro” or “Yonan Nakata Saburo” is included in the public data 40, the extraction unit 211 may extract this as related information 42 that is highly similar to “Yonan Tanaka Taro” in the pre-expansion data 41.

[0115] The combining unit 212 may add the related information 42 extracted by the extracting unit 211 as a record by forming the same data item configuration as the pre-extension data 41. For example, the pre-extension data 41 is sales record information consisting of two data items, "product name" and "sales", and is a database having 100 records for 100 types of anime-related products. In response to this, the extracting unit 211 extracts sales information for another 10 types of anime-related products from the public data 40 as related information 42.

[0116] In this case, the combining unit 212 may create sales record information consisting of two data items, "product name" and "sales," for another 10 anime-related products, and add it to the pre-extension data 41. As a result, the post-extension data 43 is expanded into a database having 110 records for 110 anime-related products. Increasing the number of samples contained in the target data, such as the number of records in a database or the number of rows in a table, leads to the output of meaningful analysis results.

[0117] As described above, in the information processing device 1 according to this exemplary embodiment, the extension unit 21 is configured to add information related to the target data from the information contained in the public data to the target data.

[0118] Therefore, according to the information processing device 1 according to this exemplary embodiment, it is possible to enrich the contents of the target data by adding related information that has a high affinity with the target data before expansion. As a result, in addition to the effects of the information processing device 1 according to each of the exemplary embodiments described above, it is possible to obtain an effect that makes it possible to obtain a second analysis result that is more valuable to a buyer. The increase in the value of the second analysis result for a buyer leads to an increase in the market value of the public data, which in turn leads to the revitalization of the data distribution market.

[0119] Furthermore, in the information processing device 1 according to this exemplary embodiment, the expansion unit 21 is configured to include an extraction unit 211 that extracts information from the public data that appears in proximity to keywords contained in the target data with a frequency exceeding a predetermined value, and a combination unit 212 that combines the extracted information as a data item of the target data.

[0120] Therefore, according to the information processing device 1 according to the present exemplary embodiment, it is possible to enrich the contents of the target data by adding related information that frequently appears in close proximity to the keywords included in the target data before expansion. As a result, it is possible to obtain an effect that the value of the second analysis result for the buyer is further increased.

[0121] Alternatively, in the information processing device 1 according to this exemplary embodiment, the expansion unit 21 is configured to include an extraction unit 211 that extracts information from the public data that has a similarity to keywords contained in the target data that exceeds a predetermined value, and a combination unit 212 that combines the extracted information as a data item of the target data.

[0122] Therefore, according to the information processing device 1 according to the present exemplary embodiment, it is possible to enrich the contents of the target data by adding related information similar to the keywords included in the target data before expansion. As a result, it is possible to obtain an effect that the value of the second analysis result for the buyer can be further increased.

[0123] <Estimation of value increase> (Configuration of Estimation Unit 22) 12 is a diagram showing an example of a detailed configuration of the estimation unit 22 included in the control unit 10 of the information processing device 1. As an example, the estimation unit 22 according to this exemplary embodiment estimates the value improvement degree 46 based on at least one of the following differences. The following differences are, that is, (1) the difference between the pre-expansion data 41 and the post-expansion data 43, and (2) the difference between a first analysis result that is a result of analyzing the pre-expansion data 41 and a second analysis result that is a result of analyzing the post-expansion data 43. Specifically, the estimation unit 22 performs estimation such that the value improvement degree 46 increases as the above-mentioned difference increases.

[0124] In this exemplary embodiment, the analysis unit 26 analyzes the target data and outputs an analysis result that is a result of the analysis. The analysis unit 26 may analyze the pre-augmented data 41 and output a first analysis result 44 that is a result of the analysis. The analysis unit 26 may analyze the augmented data 43 and output a second analysis result 45 that is a result of the analysis.

[0125] For example, the analysis unit 26 may be a predictor that analyzes the target data and outputs a prediction result indicating a predetermined event. In this case, the first analysis result 44 output from the analysis unit 26 is a first prediction result obtained by analyzing the pre-augmentation data 41, and the second analysis result 45 is a second prediction result obtained by analyzing the augmentation data 43.

[0126] The estimation unit 22 may obtain a first predicted-actual error by comparing the first prediction result with an event that actually occurred. The estimation unit 22 may obtain a second predicted-actual error by comparing the second prediction result with an event that actually occurred. The estimation unit 22 then estimates the degree of value improvement such that the smaller the second predicted-actual error is compared with the first predicted-actual error, the higher the degree of value improvement of the second analysis result 45 relative to the first analysis result 44. In other words, the estimation unit 22 performs estimation such that the higher the accuracy of prediction in the second prediction result is, the higher the degree of value improvement is.

[0127] Also, for example, the analysis unit 26 may be a classifier that analyzes the target data, determines a predetermined event, and outputs a determination result. In this case, the first analysis result 44 output from the analysis unit 26 is a first determination result obtained by analyzing the pre-augmentation data 41, and the second analysis result 45 is a second determination result obtained by analyzing the augmentation data 43.

[0128] The estimation unit 22 may obtain a first accuracy rate by comparing the first judgment result with the correct answer data. The estimation unit 22 may obtain a second accuracy rate by comparing the second judgment result with the correct answer data. The estimation unit 22 then estimates the degree of value improvement such that the higher the second accuracy rate is compared with the first accuracy rate, the higher the degree of value improvement of the second analysis result 45 relative to the first analysis result 44. That is, the estimation unit 22 performs an estimation such that the higher the accuracy of the judgment in the second judgment result is, the higher the degree of value improvement is.

[0129] For example, the estimation unit 22 may increase the degree of value improvement the greater the increase in at least one of the number of data items and the number of samples in the expanded data 43 compared to the pre-expansion data 41. As described above, the greater the increase in at least one of the number of data items and the number of samples in the target data, the easier it is to obtain a meaningful analysis result.

[0130] Alternatively, for example, the analysis unit 26 may be a BI (business intelligence) tool that analyzes target data and outputs visually recognizable visualization information as an analysis result. In this case, the first analysis result 44 output from the analysis unit 26 is first visualization information obtained by analyzing the pre-augmentation data 41, and the second analysis result 45 is second visualization information obtained by analyzing the augmented data 43.

[0131] The estimation unit 22 may compare the first visualized information with the second visualized information, and may increase the degree of value improvement as the significance of the second visualized information increases. For example, the visualized information may be determined to have high significance if it is excellent in terms of listability, ability to understand important points at a glance, and a way of presenting results that provides excellent insight to support the viewer in making decisions.

[0132] The estimation unit 22 may include a significance determination unit 221 that uses visualization information as an input value and an index value representing the significance of the visualization information as an output value. In this exemplary embodiment, the significance determination unit 221 is configured to realize a significance determination means. The estimation unit 22 may increase the degree of value improvement as the index value output by the significance determination unit 221 indicates a higher significance. The significance determination unit 221 may be realized using, for example, the technology described in Non-Patent Document 3.

[0133] (Variation; Statistical Causal Discovery) Alternatively, for example, the analysis unit 26 may be a statistical causal search unit that analyzes the target data using a statistical causal search technique. The analysis unit 26 as a statistical causal search unit is configured to realize a statistical causal search means in this exemplary embodiment. The analysis unit 26 takes the target data and a target variable included in the target data as input values, and outputs causal relationship data including at least a plurality of keywords included in the target data and information indicating a causal relationship between each keyword as an output value. The analysis unit 26 as a statistical causal search unit may be realized using, for example, the technology described in Non-Patent Document 2.

[0134] The analysis unit 26 takes the pre-augmentation data 41 as an input value and outputs the first causal relationship data as a first analysis result 44 as an output value. The analysis unit 26 takes the post-augmentation data 43 as an input value and outputs the second causal relationship data as a second analysis result 45 as an output value. In both of these two analyses, the same keyword in the target data is specified as the objective variable.

[0135] The estimation unit 22 may increase the degree of value improvement as the second analysis result 45 includes more combinations of keywords having a causal relationship than the first analysis result 44.

[0136] 13 is a diagram showing an example of the data structure of pre-extension data 41 in this modified example. As an example, the target data is sales record information of buyer company B, and in the stage of pre-extension data 41, it is assumed that the target data is composed of, for example, four data items, "product ID", "product name", "price", and "rating". For example, if it is known that the key success factor (KSF) of the above-mentioned four data items in the pre-extension data 41 is "rating", then "rating" may be input to the analysis unit 26 together with the pre-extension data 41 as the objective variable.

[0137] Fig. 14 is a diagram showing an example of the data structure of first causal relationship data (first analysis result 44) output by the analysis unit 26 as an output value using the pre-augmentation data 41 and the objective variable "evaluation" shown in Fig. 13 as input values. As shown in the diagram, the causal relationship data includes a plurality of keywords extracted from the pre-augmentation data 41 and information indicating the causal relationships between the keywords.

[0138] The first analysis result 44 obtained from the pre-expansion data 41 only reveals the causal relationship between price and valuation, and it is difficult to say that the first analysis result 44 is valuable and significant information for buyer company B.

[0139] 15 is a diagram showing an example of the data structure of the expanded data 43 in this modified example. The expanded data 43 may be obtained, for example, by the expansion unit 21 adding new data items to the pre-expansion data 41 using the public data 40, as described above. In the illustrated example, the data items "smart watch", "fitness", "manufactured by company G", and "enthusiast" are newly added.

[0140] Fig. 16 is a diagram showing an example of the data structure of the second causal relationship data (second analysis result 45) output by the analysis unit 26 using the expanded data 43 and the objective variable "evaluation" shown in Fig. 15 as input values. The information indicating the causal relationship may include information such as the strength of the causal relationship, the direction of the causal relationship, and whether the connection is positive or negative.

[0141] In the second analysis result 45 obtained from the expanded data 43, multiple keywords other than price that are determined to be correlated with evaluation are extracted compared to the first analysis result 44. Based on such second analysis result 45, buyer company B is more likely to focus on important elements of the product that lead to a high evaluation, or conversely, negative elements that lower the evaluation. It can be said that the second analysis result 45 is valuable and significant information for buyer company B compared to the first analysis result 44.

[0142] The estimation unit 22 compares the first analysis result 44 with the second analysis result 45, and estimates that the more combinations of keywords having a causal relationship there are, the higher the degree of value improvement of the second analysis result 45 will be.

[0143] As described above, in the information processing device 1 according to this exemplary embodiment, the estimation unit 22 is configured to increase the degree of value improvement based on at least one of the difference between the target data before expansion and the target data after expansion, and the difference between the first analysis result and the second analysis result, as the difference is larger.

[0144] Therefore, according to the information processing device 1 of this exemplary embodiment, in addition to the effects achieved by the information processing device 1 of each of the exemplary embodiments described above, the effect of being able to quantitatively evaluate the value of the seller's public data from the perspective of the benefits brought to the buyer is obtained.

[0145] Furthermore, in this exemplary embodiment, the first analysis result and the second analysis result may each indicate a result of predicting or determining a predetermined event using the target data before and after the expansion. In this case, the estimation unit 22 is configured to increase the value improvement degree as the accuracy of the prediction or determination of the second analysis result improves.

[0146] Therefore, according to the information processing device 1 of this exemplary embodiment, the benefit to the buyer can be obtained by quantitatively evaluating the value of the seller's public data from the perspective of the extent to which prediction accuracy or determination accuracy is improved.

[0147] In the information processing device 1 according to this exemplary embodiment, the estimation unit 22 is configured to increase the degree of value improvement the greater the increase in at least one of the number of data items and the number of samples in the target data after expansion compared to before expansion.

[0148] Therefore, according to the information processing device 1 of this exemplary embodiment, the benefit to the buyer can be obtained by quantitatively evaluating the value of the seller's public data from the perspective of how much the amount of information in the target data increases.

[0149] Furthermore, in this exemplary embodiment, the first analysis result and the second analysis result may each include visually recognizable visualization information converted from the target data before and after the expansion. In this case, the estimation unit 22 is configured to increase the value improvement degree as the significance of the visualization information included in the second analysis result increases.

[0150] In the information processing device 1 according to this exemplary embodiment, the estimation unit 22 includes a significance determination unit 221 that uses the visualization information as an input value and an index value representing the significance of the visualization information as an output value, and is configured to increase the degree of value improvement as the index value indicates a higher significance.

[0151] Therefore, according to the information processing device 1 of this exemplary embodiment, the benefit to the buyer is that the value of the seller's public data can be quantitatively evaluated from the perspective of how much the analysis results obtained from the target data present attractive information that attracts the buyer's interest.

[0152] The information processing device 1 according to this exemplary embodiment may further include an analysis unit 26 that applies a statistical causal search technique that uses the target data and a target variable included in the target data as input values ​​and outputs a plurality of keywords included in the target data and information indicating a causal relationship between each keyword as output values. In the estimation unit 22 of such an information processing device 1, a configuration is adopted in which the second analysis result output as the output value by the analysis unit 26 using the expanded target data as an input value includes more combinations of keywords having a causal relationship compared to the first analysis result output as the output value by the analysis unit 26 using the target data before the expansion as an input value.

[0153] Therefore, according to the information processing device 1 of this exemplary embodiment, the benefit brought to the buyer is that the value of the seller's public data can be quantitatively evaluated from the viewpoint of how many important elements correlated with the objective variable the causal relationship data obtained from the target data contains.

[0154] <Calculation of sales price> The control unit 10 of the information processing device 1 may further include a price calculation unit 25 that calculates the selling price of the seller's public data 40 according to the value improvement degree estimated by the estimation unit 22. The price calculation unit 25 is configured to realize a price calculation means in this exemplary embodiment.

[0155] The price calculation unit 25 determines the selling price so that the selling price is higher in proportion to the estimated degree of value improvement, for example. For example, the price calculation unit 25 may calculate, as the selling price, the analysis service usage fee (FIG. 3) paid by the buyer company B to the information processing device 1, or may calculate the public data usage fee received by the seller company S from the information processing device 1. Alternatively, the price calculation unit 25 may calculate both the analysis service usage fee and the public data usage fee. In this case, the output control unit 24 may use the analysis service usage fee when presenting the selling price to the buyer company B, and may use the public data usage fee when presenting the selling price to the seller company S.

[0156] The price calculation unit 25 may refer to purchase history information that associates the purchase price when the buyer company B purchased the public data 40 with the value improvement degree 46 based on the public data 40. Then, the price calculation unit 25 may revise the selling price upward as the purchase price increases.

[0157] (Purchase record information 113) 17 is a diagram showing an example of a data structure of purchase history information 113 stored in storage unit 11. Purchase history information 113 may have, for example, four data items: purchaser ID, purchaser name, purchase amount, and value improvement degree. Purchase history information 113 may further have four data items (not shown): seller ID, seller name, URL, and purchase date, as necessary.

[0158] The purchaser ID indicates identification information that uniquely identifies the buyer company B. The purchaser name indicates the name of the buyer company B. The purchase amount indicates the purchase amount that the buyer company B paid for purchasing the public data 40. The value improvement degree indicates the value improvement degree of the second analysis result 45 obtained by analyzing the target data by using the purchased public data 40.

[0159] The seller ID indicates identification information that uniquely identifies the seller company S, which is the holder of the public data 40 purchased by the buyer company B. The seller name indicates the name of the seller company S. The URL indicates information that identifies the purchased public data 40 (web page data). The purchase date indicates the date and time when the public data 40 was purchased.

[0160] The price calculation unit 25 can correct the selling price calculated based on the value improvement degree, based on the purchase record information 113. For example, when the purchase price for the value improvement degree of a certain buyer company B is higher than that of other buyers, the price calculation unit 25 may correct the selling price presented to the buyer company B upward. Alternatively, when the purchase price for the value improvement degree when the public data 40 of a certain seller company S is purchased is lower than that of other sellers, the price calculation unit 25 may correct the selling price of the public data 40 of the seller company S downward.

[0161] As described above, the information processing device 1 according to this exemplary embodiment is configured to further include a price calculation unit 25 that calculates the selling price of the seller's public data in accordance with the degree of value improvement estimated by the estimation unit 22.

[0162] Therefore, according to the information processing device 1 of this exemplary embodiment, in addition to the effects achieved by the information processing device 1 of each of the exemplary embodiments described above, the effect of enabling rational price setting based on the degree of value improvement is obtained.

[0163] In the information processing device 1 according to this exemplary embodiment, the price calculation unit 25 refers to purchase history information 113 that associates the purchase price when the buyer purchased the public data with the degree of value improvement based on the public data, and is configured to revise the selling price upward as the purchase price is higher.

[0164] Therefore, according to the information processing device 1 of this exemplary embodiment, in addition to the effects achieved by the information processing device 1 of each of the exemplary embodiments described above, the effect of enabling rational price setting based on the degree of value improvement and buyer demand is obtained.

[0165] <Processing flow> Fig. 18 is a sequence diagram showing an example of the information processing method of the present exemplary embodiment executed in the data distribution system 100. The information processing method shown in Fig. 18 is, as an example, a method for evaluating, i.e., assessing, the market value of public data of a seller in order to distribute the public data by the data distribution method of exemplary embodiment 2. Note that the information processing method of the present disclosure is not limited to the example shown in Fig. 18, and can also be applied to a method for searching for public data that increases the value of an analysis result in which a buyer's target data is analyzed.

[0166] In the illustrated example, a case will be described in which the seller terminal 3 of the seller company is caused to input an instruction to start an appraisal and to display the appraisal result. However, the information processing method disclosed herein is not limited to this example and may be applied to a case in which all steps, including the input of an instruction to start an appraisal and the display of the appraisal result, are executed by the information processing device 1 of the service provider. In the former case, the "user" refers to the operator of the seller company who operates the seller terminal 3, and in the latter case, the "user" refers to the operator of the service provider who operates the information processing device 1.

[0167] In step S101, the seller terminal 3 displays the appraisal request screen 50 shown in Fig. 6 on the display unit of the seller terminal 3. For example, the output control unit 24 of the information processing device 1 may receive a message requesting the appraisal request screen 50 from the seller terminal 3 and return information related to the appraisal request screen 50 so that the appraisal request screen 50 is displayed on the display unit of the seller terminal 3.

[0168] In step S102, the seller terminal 3 accepts an input operation from the user to instruct the start of an appraisal. For example, the seller terminal 3 may accept the instruction to start an appraisal in response to pressing of the button 61 with the URL of the public data being entered in the area 60 of the appraisal request screen 50. When the seller terminal 3 accepts the instruction to start an appraisal, it proceeds from YES in S102 to S103.

[0169] In step S103, the seller terminal 3 transmits an appraisal request to the information processing device 1. The seller terminal 3 transmits the appraisal request including at least the URL entered in the area 60 to the information processing device 1. The appraisal request may further include an ID of the seller company S that identifies the seller company S that holds the public data pointed to by the URL.

[0170] In step S104, the data acquisition unit 23 of the information processing device 1 receives the assessment request from the seller terminal 3.

[0171] In step S105, the data acquisition unit 23 acquires web page data of the website indicated by the URL included in the received assessment request.

[0172] In step S106, the data acquisition unit 23 acquires target data from the target data DB 111 stored in advance in the storage unit 11. The data acquisition unit 23 may extract only target data of the buyer company B that meets a predetermined condition so as to match the requesting seller company S. Here, the data acquisition unit 23 may acquire multiple candidates of target data to be matched with the public data for which an appraisal has been requested. When multiple candidates of target data have been acquired, the data acquisition unit 23 then reads out one of the candidates to be subjected to a process for estimating the degree of value improvement. The process group from S106 to S111 is repeated the number of times equal to the number of acquired target data.

[0173] In step S107, the extension unit 21 extracts related information related to the target data from the information included in the web page data.

[0174] In step S108, the expansion unit 21 expands the target data based on the extracted related information.

[0175] In step S109, the estimation unit 22 estimates the degree of value improvement of the second analysis result obtained when an analysis is performed using the expanded data.

[0176] In step S110, the price calculation unit 25 calculates the selling price when the above-mentioned web page data is sold from the requesting seller company S to the buyer company B which holds the above-mentioned target data, based on the estimated degree of value improvement.

[0177] In step S111, the data acquisition unit 23 judges whether or not there remains any target data among the multiple target data acquired in S106 for which the estimation of the value improvement degree and the calculation of the selling price have not been completed. If there remains any unassessed target data, the process returns from YES in S111 to the process of S106, and the subsequent processes are repeated. If the assessment of all the target data acquired in S106 has been completed, the process proceeds from NO in S111 to S112.

[0178] In step S112, the output control unit 24 transmits the assessment result to the requesting seller terminal 3. The output control unit 24 transmits the assessment result including, for example, a candidate buyer company B and the degree of value improvement brought to the buyer company B to the seller terminal 3. The output control unit 24 may further include a selling price calculated based on the degree of value improvement in the assessment result.

[0179] In step S113, the seller terminal 3 receives the appraisal result from the information processing device 1.

[0180] In step S114, the seller terminal 3 displays the value improvement degree and the selling price included in the received assessment result on the display unit of the seller terminal 3 for each buyer company B. For example, the seller terminal 3 displays the above-mentioned assessment result on the display unit of the seller terminal 3. reflectionThe assessment result screen 51 shown in FIG. 7 may be displayed on the display unit.

[0181] [Software implementation example] Some or all of the functions of the information processing device 1 may be realized by hardware such as an integrated circuit (IC chip), or may be realized by software.

[0182] In the latter case, the information processing device 1 is realized, for example, by a computer that executes instructions of a program, which is software that realizes each function. An example of such a computer (hereinafter, referred to as computer C) is shown in FIG. 19. The computer C includes at least one processor C1 and at least one memory C2. The memory C2 stores a program P for operating the computer C as the information processing device 1. In the computer C, the processor C1 reads and executes the program P from the memory C2, thereby realizing each function of the information processing device 1.

[0183] The processor C1 may be, for example, a central processing unit (CPU), a graphic processing unit (GPU), a digital signal processor (DSP), a micro processing unit (MPU), a floating point number processing unit (FPU), a physics processing unit (PPU), a microcontroller, or a combination of these. The memory C2 may be, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), or a combination of these.

[0184] The computer C may further include a RAM (Random Access Memory) for expanding the program P during execution and for temporarily storing various data. The computer C may further include a communication interface for transmitting and receiving data to and from other devices. The computer C may further include an input / output interface for connecting input / output devices such as a keyboard, a mouse, a display, and a printer.

[0185] Furthermore, the program P can be recorded on a non-transitory tangible recording medium M that can be read by the computer C. Such a recording medium M can be, for example, a tape, a disk, a card, a semiconductor memory, or a programmable logic circuit. The computer C can obtain the program P via such a recording medium M. Furthermore, the program P can be transmitted via a transmission medium. Such a transmission medium can be, for example, a communication network or broadcast waves. The computer C can also obtain the program P via such a transmission medium.

[0186] [Additional Note 1] The present invention is not limited to the above-described embodiment, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining the technical means disclosed in the above-described embodiment are also included in the technical scope of the present invention.

[0187] [Additional Note 2] Some or all of the above-described embodiments can be described as follows. However, the present invention is not limited to the following described aspects.

[0188] (Appendix 1) An expansion means for expanding the target data to be analyzed held by the buyer using public data held by the seller that has been published or is scheduled to be published; and an estimation means for estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been expanded, relative to a first analysis result, which is a result of analyzing the target data before it has been expanded.

[0189] (Appendix 2) 2. An information processing device according to claim 1, wherein the public data is web page data constituting the seller's website.

[0190] (Appendix 3) a data acquisition means for acquiring the web page data based on a uniform resource locator (URL) designated by a user; The information processing device according to claim 2, further comprising an output control means for outputting the degree of value improvement of the second analysis result obtained by the target data after it has been expanded using the acquired web page data for each buyer who holds the target data.

[0191] (Appendix 4) A data acquisition means for acquiring the target data designated by a user; The information processing device according to claim 2 or 3, further comprising an output control means for outputting, for each seller who holds the web page data, the degree of value improvement of the second analysis result obtained by expanding the acquired target data using the web page data.

[0192] (Appendix 5) 5. The information processing device according to claim 1, wherein the extension means adds information related to the target data, among information included in the public data, to the target data.

[0193] (Appendix 6) The expansion means is an extraction means for extracting, from the public data, information that appears in proximity to a keyword included in the target data with a frequency exceeding a predetermined value; and a combining means for combining the extracted information as a data item of the target data.

[0194] (Appendix 7) The expansion means is an extraction means for extracting information from the public data, the information having a similarity to a keyword included in the target data exceeding a predetermined value; and a combining means for combining the extracted information as a data item of the target data.

[0195] (Appendix 8) The estimation means includes: A difference between the target data before and after the extension, and A difference between the first analysis result and the second analysis result 8. The information processing device according to claim 1, wherein the value improvement degree is increased as the difference is larger based on at least one of the above.

[0196] (Appendix 9) the first analysis result and the second analysis result each indicate a result of predicting or determining a predetermined event using the target data before and after the expansion, The information processing device according to claim 8, wherein the estimation means increases the degree of value improvement as the accuracy of the prediction or judgment of the second analysis result improves.

[0197] (Appendix 10) The information processing device of claim 8, wherein the estimation means increases the degree of value improvement the greater the increase in at least one of the number of data items and the number of samples in the target data after the expansion compared to before the expansion.

[0198] (Appendix 11) The first analysis result and the second analysis result each include visually perceptible visualization information converted from the target data before and after the augmentation, and The information processing device according to claim 8, wherein the estimation means increases the degree of value improvement as the significance of the visualization information included in the second analysis result improves.

[0199] (Appendix 12) The estimation means includes: a significance determination means for determining, as an input value, the visualization information and, as an output value, an index value representing the significance of the visualization information; The information processing device according to claim 11, wherein the higher the significance of the index value, the higher the degree of value improvement.

[0200] (Appendix 13) A statistical causal search means is further provided for receiving the target data and a response variable included in the target data as input values, and outputting a plurality of keywords included in the target data and information indicating a causal relationship between each of the keywords as output values, The estimation means includes: The information processing device described in Appendix 8, wherein the second analysis result output as the output value by the statistical causal search means using the expanded target data as an input value includes a greater number of combinations of keywords having a causal relationship compared to the first analysis result output as the output value by the statistical causal search means using the target data before the expansion as an input value, and the greater the degree of value improvement.

[0201] (Appendix 14) 14. The information processing device according to any one of claims 1 to 13, further comprising a price calculation means for calculating a selling price of the public data of the seller according to the degree of value improvement estimated by the estimation means.

[0202] (Appendix 15) The price calculation means includes: referring to purchase record information that associates the purchase price when the buyer purchased the public data with the value improvement degree based on the public data; The information processing device of claim 14, wherein the higher the purchase amount, the more the selling price is revised upward.

[0203] (Appendix 16) At least one processor determines a sales price for a service of analyzing the expanded target data based on a profit that the buyer will obtain when the target data to be analyzed is expanded using public data held by the seller that has been published or is to be published; Supplying the buyer with analysis result data, which is a result of analyzing the target data expanded using the public data, in return for a remuneration equivalent to the selling price; and and paying the seller a remuneration equivalent to a portion of the selling price as consideration for use of the public data.

[0204] (Appendix 17) The data distribution method described in Appendix 16, wherein the public data is web page data constituting the seller's website.

[0205] (Appendix 18) At least one processor augmenting the buyer's target data for analysis with the seller's publicly available or planned publicly available data; and estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been expanded, relative to a first analysis result, which is a result of analyzing the target data before it has been expanded.

[0206] (Appendix 19) Computer, An extension means for extending the target data to be analyzed, held by the buyer, with public data held by the seller that has been published or is to be published; and A control program that functions as an estimation means for estimating the degree of value improvement of a second analysis result, which is the result of analyzing the target data after it has been expanded, relative to a first analysis result, which is the result of analyzing the target data before it is expanded.

[0207] The output control means may control the display unit so that, when displaying potential buyers or sellers on the display unit together with the degree of value improvement, they are displayed in order of decreasing similarity between the buyer's target data and the seller's public data.

[0208] The output control means may control the display unit so that, when displaying potential buyers or sellers together with the degree of value improvement on the display unit, the candidates are displayed in descending order of the degree of value improvement.

[0209] The output control means may control the display unit so that, when displaying potential buyers or sellers together with the value improvement degree on the display unit, they are displayed in descending order of selling price.

[0210] The output control means may control the display unit so that, when displaying potential buyers together with the degree of value improvement on the display unit, the candidates are displayed in order of the price at which they previously purchased public data, or in order of the number of times they have purchased public data.

[0211] The output control means may control the display unit so that, when displaying candidate sellers together with the degree of value improvement on the display unit, the candidates' public data are displayed in order of highest price at which they were previously purchased, or in order of most frequently purchased public data.

[0212] [Additional Note 3] A part or all of the above-described embodiments can be further expressed as follows.

[0213] An information processing device comprising at least one processor that executes an extension process for extending target data held by a buyer to be analyzed using public data held by a seller that has been made public or is scheduled to be made public, and an estimation process for estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been extended, relative to a first analysis result, which is a result of analyzing the target data before it is extended.

[0214] The information processing device may further include a memory, and the memory may store a program for causing the processor to execute the extension process and the estimation process. The program may be recorded in a computer-readable, non-transitory, tangible recording medium. [Explanation of symbols]

[0215] 1. Information processing device 2. Buyer terminal 3. Seller terminal 10 Control section 11 Storage section 12 Control section 13. Communications Department 14 Display section 21 Expansion section (expansion means) 22 Estimation part (estimation means) 23 Data acquisition unit (data acquisition means) 24 Output control unit (output control means) 25 Price Calculation Department (Price Calculation Method) 26 Analysis Department (Statistical causal search means) 40 Public Data (Webpage Data) 41 Pre-expansion data 42 Related Information 43 Expanded Data 44 First analysis results 45 Second analysis results 46 Value improvement 50 Appraisal request screen 51 Assessment result screen 52 Search request screen 53 Search results screen 100 Data Distribution System 111 Target Data DB 112 Public Data DB 113 Purchase History Information 211 Extraction part (extraction means) 212 Coupling part (coupling means) 221 Significance determining unit (significance determining means)

Claims

1. An expansion means for expanding the target data to be analyzed held by the buyer using public data held by the seller that has been published or is scheduled to be published; and an estimation means for estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after the extension, relative to a first analysis result, which is a result of analyzing the target data before the extension; the target data includes at least one of data items and data samples; the extension means extracts information related to the target data from information included in the public data, and extends the target data by adding the extracted information as at least one of a new data item and a data sample of the target data; The information processing device, wherein the estimation means estimates the degree of value improvement from an increase in at least one of the number of data items and the number of samples of the target data before and after the expansion by the expansion means.

2. An expansion means for expanding target data to be analyzed, which is held by a buyer, using public data held by a seller that has been published or is scheduled to be published; and an estimation means for estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after the extension, relative to a first analysis result, which is a result of analyzing the target data before the extension; the target data and the public data include text data; the extension means extracts information related to the target data from text data included in the public data, and adds the extracted information to the target data to extend the target data; The estimation means estimates the degree of value improvement from the difference between the number of combinations of causally related keywords among the multiple keywords included in the target data before the expansion and the number of combinations of causally related keywords among the multiple keywords included in the target data after the expansion.

3. The information processing apparatus according to claim 1 , wherein the public data is web page data constituting a website of the seller.

4. a data acquisition means for acquiring the web page data based on a uniform resource locator (URL) designated by a user; The information processing device according to claim 3, further comprising an output control means for outputting the degree of value improvement of the second analysis result obtained by the target data after it has been expanded using the acquired web page data for each buyer who holds the target data.

5. A data acquisition means for acquiring the target data designated by a user; The information processing device according to claim 3 or 4, further comprising an output control means for outputting the degree of value improvement of the second analysis result obtained by expanding the acquired target data using the web page data for each seller who holds the web page data.

6. The expansion means is an extraction means for extracting, from the public data, information that appears in proximity to a keyword included in the target data with a frequency exceeding a predetermined value; 3. The information processing apparatus according to claim 1, further comprising: a combining unit for combining the extracted information as a data item of the target data.

7. The expansion means is an extraction means for extracting information from the public data, the information having a similarity to a keyword included in the target data exceeding a predetermined value; 3. The information processing apparatus according to claim 1, further comprising: a combining unit for combining the extracted information as a data item of the target data.

8. The information processing apparatus according to claim 1 , further comprising a price calculation means for calculating a selling price of the public data of the seller in accordance with the degree of value improvement estimated by the estimation means.

9. The price calculation means includes: referring to purchase record information that associates the purchase price when the buyer purchased the public data with the value improvement degree based on the public data; The information processing device according to claim 8 , wherein the selling price is revised upward as the purchase amount increases.

10. At least one processor augmenting the buyer's target data to be analyzed with the seller's publicly available or planned publicly available data; and estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after the extension, relative to a first analysis result, which is a result of analyzing the target data before the extension; the target data includes at least one of data items and data samples; In the expansion of the target data, the at least one processor extracts information related to the target data from information included in the public data, and expands the target data by adding the extracted information as at least one of a new data item and a data sample of the target data; In estimating the degree of value improvement, the at least one processor estimates the degree of value improvement from an increase in at least one of the number of data items and the number of samples of the target data before and after the expansion.

11. At least one processor, augmenting the buyer's target data to be analyzed with the seller's publicly available or planned publicly available data; and estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after the extension, relative to a first analysis result, which is a result of analyzing the target data before the extension; the target data and the public data include text data; In the expansion of the target data, the at least one processor extracts information related to the target data from text data included in the public data, and expands the target data by adding the extracted information to the target data; An information processing method in which, in estimating the degree of value improvement, the at least one processor estimates the degree of value improvement from the difference between the number of combinations of causally related keywords among the multiple keywords included in the target data before it is expanded and the number of combinations of causally related keywords among the multiple keywords included in the target data after it is expanded.

12. Computer, An extension means for extending the target data to be analyzed, held by the buyer, with public data held by the seller that has been published or is to be published; and and functioning as an estimation means for estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been expanded, relative to a first analysis result, which is a result of analyzing the target data before it has been expanded; the target data includes at least one of data items and data samples; the extension means extracts information related to the target data from information included in the public data, and extends the target data by adding the extracted information as at least one of a new data item and a data sample of the target data; The estimation means estimates the degree of value improvement from an increase in at least one of the number of data items and the number of samples of the target data before and after the expansion by the expansion means.

13. A computer comprising: An extension means for extending the target data to be analyzed, held by the buyer, with public data held by the seller that has been published or is to be published; and and functioning as an estimation means for estimating a degree of value improvement of a second analysis result, which is a result of analyzing the target data after it has been expanded, relative to a first analysis result, which is a result of analyzing the target data before it has been expanded; the target data and the public data include text data; the extension means extracts information related to the target data from text data included in the public data, and adds the extracted information to the target data to extend the target data; The estimation means is a control program that estimates the degree of value improvement from the difference between the number of combinations of causally related keywords among the multiple keywords included in the target data before the expansion and the number of combinations of causally related keywords among the multiple keywords included in the target data after the expansion.

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