WO2021075091A1 - 対価算出装置、制御方法、及びプログラム - Google Patents

対価算出装置、制御方法、及びプログラム Download PDF

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Publication number
WO2021075091A1
WO2021075091A1 PCT/JP2020/025276 JP2020025276W WO2021075091A1 WO 2021075091 A1 WO2021075091 A1 WO 2021075091A1 JP 2020025276 W JP2020025276 W JP 2020025276W WO 2021075091 A1 WO2021075091 A1 WO 2021075091A1
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Prior art keywords
target data
consideration
rarity
data
target
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English (en)
French (fr)
Japanese (ja)
Inventor
廣樹 松浦
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NEC Corp
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NEC Corp
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Priority to JP2021552098A priority Critical patent/JP7393030B2/ja
Priority to US17/765,915 priority patent/US20220374950A1/en
Publication of WO2021075091A1 publication Critical patent/WO2021075091A1/ja
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the present invention relates to a technique for determining a consideration for providing data.
  • the data (subprograms, etc.) used for the development of the application may be provided by a third party. Then, the consideration may be paid to the provider.
  • Patent Document 1 discloses a technique for calculating a consideration for a provider of a module according to the frequency of use of the module when constructing an application by incorporating the module.
  • Patent Document 2 discloses a technique in which when a learning model created by a plurality of contributors is used from a user terminal, the consideration paid in return for the use is distributed to each contributor according to the contribution rate. There is.
  • the present invention has been made in view of the above problems, and one of the purposes thereof is to provide a technique for appropriately determining the consideration for providing the data used for the application.
  • the consideration calculation device of the present invention 1) acquires the rarity of the target data for the target data used in the target application, and 2) uses the target data in the target application based on the acquired rarity. It has a calculation unit that calculates the usage consideration, which is the consideration for what has been done.
  • the target data is a subprogram that realizes a part of the processing performed by the target application, or is data used for creating the subprogram.
  • the control method of the present invention is executed by a computer.
  • the control method is that 1) the target data used by the target application is used by the target application based on the acquisition step of acquiring the rarity of the target data and 2) the acquired rarity. It has a calculation step of calculating the usage consideration, which is the consideration for the above.
  • the target data is a subprogram that realizes a part of the processing performed by the target application, or is data used for creating the subprogram.
  • the program of the present invention causes a computer to execute the control method of the present invention.
  • FIG. It is a figure for demonstrating the outline of the consideration calculation apparatus of this embodiment. It is a figure which illustrates the functional structure of the consideration calculation apparatus of Embodiment 1.
  • FIG. It is a figure which illustrates the calculator for realizing the consideration calculation apparatus. It is a flowchart which illustrates the flow of the process executed by the consideration calculation apparatus of Embodiment 1.
  • FIG. It is a figure which illustrates the target data information in a table format. It is a figure which illustrates the information stored in the rarity information storage device in a table format.
  • each block diagram unless otherwise specified, each block represents a functional unit configuration rather than a hardware unit configuration.
  • FIG. 1 is a diagram for explaining an outline of the consideration calculation device 2000 of the present embodiment. Note that FIG. 1 is an example for facilitating the understanding of the consideration calculation device 2000, and the function of the consideration calculation device 2000 is not limited to that shown in FIG.
  • the target application 10 is an arbitrary application realized by using the target data 20.
  • the target data 20 is data used for realizing the target application 10.
  • the target data 20 is a subprogram that realizes a part of the processing performed by the target application 10.
  • An example of a subprogram is a trained model (AI engine).
  • the target data 20 is learning data used for learning the trained model used by the target application 10.
  • the provider who provided the target data 20 receives compensation according to the use of the target data 20 in the target application 10.
  • the consideration for using the target data 20 for the target application 10 in this way is referred to as a consideration for using the target data 20.
  • the consideration calculation device 2000 calculates the usage consideration for the target data 20.
  • some of the target data 20 are easy to provide, while others are difficult to provide.
  • the target data 20 is a program that can be created only by a person having a high degree of specialized knowledge, or learning data composed of data that is difficult to collect, it can be said that the target data 20 is difficult to provide.
  • the target data 20 is a program that does not require so high specialized knowledge, learning data composed of easily collectable data, or the like, it can be said that the target data 20 is relatively easy to provide. It is preferable that the consideration for using the target data 20 is calculated in consideration of the difficulty of providing the target data 20.
  • the consideration calculation device 2000 acquires an index value indicating the difficulty of providing the target data 20 (difficulty of obtaining or creating the target data 20).
  • this index value is referred to as the rarity of the target data 20.
  • the consideration calculation device 2000 calculates the usage consideration of the target data 20 according to the rarity of the target data 20.
  • the consideration calculation device 2000 calculates the usage consideration of the target data 20 according to the difficulty (rareness) of providing the target data 20 of the target data 20.
  • the consideration for using the target data 20 it is possible to pay the provider of the target data 20 a consideration commensurate with the value of the target data 20 provided by the provider.
  • the provider of the target data 20 can be appropriately given an incentive to provide the target data 20, so that the user or the developer of the target application 10 can provide the application that he / she wants to realize. You will be able to receive it easily.
  • FIG. 2 is a diagram illustrating the functional configuration of the consideration calculation device 2000 of the first embodiment.
  • the consideration calculation device 2000 has an acquisition unit 2020 and a calculation unit 2040.
  • the acquisition unit 2020 acquires the rarity corresponding to the target data 20 used by the target application 10.
  • the calculation unit 2040 calculates the usage consideration for providing the target data 20 to the target application 10 based on the rarity corresponding to the target data 20.
  • Each function component of the consideration calculation device 2000 may be realized by hardware (eg, a hard-wired electronic circuit) that realizes each function component, or a combination of hardware and software (example:). It may be realized by a combination of an electronic circuit and a program that controls it).
  • hardware eg, a hard-wired electronic circuit
  • software e.g., a combination of hardware and software
  • It may be realized by a combination of an electronic circuit and a program that controls it).
  • a case where each functional component of the consideration calculation device 2000 is realized by a combination of hardware and software will be further described.
  • FIG. 3 is a diagram illustrating a calculator 1000 for realizing the consideration calculation device 2000.
  • the computer 1000 is an arbitrary computer.
  • the computer 1000 is a portable computer such as a smartphone or a tablet terminal.
  • the computer 1000 may be a stationary computer such as a PC (Personal Computer) or a server machine.
  • PC Personal Computer
  • the computer 1000 may be a dedicated computer designed to realize the consideration calculation device 2000, or may be a general-purpose computer. In the latter case, for example, by installing a predetermined application on the computer 1000, the function of the consideration calculation device 2000 is realized in the computer 1000.
  • the above application is composed of a program for realizing each functional component of the consideration calculation device 2000. That is, this program causes the computer 1000 to execute the processing performed by the acquisition unit 2020 and the processing performed by the calculation unit 2040, respectively.
  • the computer 1000 has a bus 1020, a processor 1040, a memory 1060, a storage device 1080, an input / output interface 1100, and a network interface 1120.
  • the bus 1020 is a data transmission line for the processor 1040, the memory 1060, the storage device 1080, the input / output interface 1100, and the network interface 1120 to transmit and receive data to and from each other.
  • the method of connecting the processors 1040 and the like to each other is not limited to the bus connection.
  • the processor 1040 is various processors such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and an FPGA (Field-Programmable Gate Array).
  • the memory 1060 is a main storage device realized by using RAM (Random Access Memory) or the like.
  • the storage device 1080 is an auxiliary storage device realized by using a hard disk, an SSD (Solid State Drive), a memory card, a ROM (Read Only Memory), or the like.
  • the input / output interface 1100 is an interface for connecting the computer 1000 and the input / output device.
  • an input device such as a keyboard and an output device such as a display device are connected to the input / output interface 1100.
  • the network interface 1120 is an interface for connecting the computer 1000 to the communication network.
  • This communication network is, for example, LAN (Local Area Network) or WAN (Wide Area Network).
  • the storage device 1080 stores a program (a program that realizes the above-mentioned application) that realizes each functional component of the consideration calculation device 2000.
  • the processor 1040 reads this program into the memory 1060 and executes it to realize each functional component of the consideration calculation device 2000.
  • FIG. 4 is a flowchart illustrating the flow of processing executed by the consideration calculation device 2000 of the first embodiment.
  • the acquisition unit 2020 acquires the rarity of the target data 20 used by the target application 10 (S102).
  • the calculation unit 2040 calculates the usage consideration of the target data 20 based on the rarity of the target data 20 (S104).
  • the consideration calculation device 2000 has one or more target data 20 used by the target application 10 at predetermined timings related to the target application 10, such as the timing when the target application 10 is completed and the timing when the target application 10 is released. Calculate the usage consideration for.
  • the target application 10 it is assumed that an application for analyzing the voice data obtained by recording the voice of a meeting or the like and creating the minutes data (hereinafter, the minutes creation application) is developed.
  • the minutes creation application includes speaker separation processing that separates voice by speaker, authentication processing that identifies the speaker, voice recognition processing that converts voice into characters, and attributes of the speaker from voice (gender, etc.). It may include attribute estimation processing for estimating.
  • the target data 20 can be used as an estimator (trained model) that realizes each of these processes and as learning data used for learning the estimator.
  • a trained model M1 that performs speaker separation processing a trained model M2 that performs authentication processing, a trained model M3 that performs voice recognition processing, and a trained model M4 that performs attribute estimation processing are provided.
  • the consideration calculation device 2000 acquires the rarity of each of these models and calculates the usage consideration of each model based on the rarity. By doing so, the usage consideration to be paid to the provider of each trained model used for the development of the target application 10 can be easily grasped according to the rarity of the provided trained model.
  • a model for performing each of the above-mentioned processes may be prepared by oneself, and learning data for learning each model may be used as the target data 20.
  • learning data D1 used for learning a model that performs speaker separation processing learning data D2 used for learning a model that performs authentication processing, and learning data used for learning a model that performs voice recognition processing.
  • D3 and the learning data DM4 used for learning the model that performs the attribute estimation process are provided.
  • the consideration calculation device 2000 acquires the rarity of each of these learning data, and calculates the usage consideration of each learning data based on the rarity. By doing so, the usage consideration to be paid to the provider of the learning data used for the development of the target application 10 can be easily grasped according to the rarity of the provided learning data.
  • the construction may be performed manually by the developer, or by a device (hereinafter, application building device). It may be done automatically.
  • the application construction device provides the user of the target application 10 with an interface (for example, a selection screen) in which a subprogram to be incorporated in the target application 10 can be specified.
  • the application construction device acquires the target application 10 before the subprogram is incorporated, and completes the target application 10 by incorporating the subprogram specified by the user into the target application 10.
  • the consideration calculation device 2000 can calculate the usage consideration of each subprogram used in the target application 10.
  • the existing technology can be used for the technology itself that automatically incorporates the subprogram specified for the application.
  • the consideration calculation device 2000 calculates the usage consideration of the target data 20 in the case where one target data 20 is used by a plurality of target applications 10.
  • the target data 20 it is assumed that a learned model that performs the speaker recognition process and the authentication process described above is provided on the server. Then, it is assumed that the trained model provided on these servers can be used from various applications that perform voice analysis (such as the above-mentioned minutes creation application).
  • the consideration for using the target data 20 is determined according to the degree of use of the target data 20 (hereinafter referred to as the degree of use).
  • the degree of use can be expressed by the number of times of use, the frequency of use, and the like. Therefore, the consideration calculation device 2000 calculates the usage consideration of the target data 20 based on the usage and rarity of the target data 20.
  • the consideration calculation device 2000 calculates the consideration for the one-time use of the target data 20 (hereinafter referred to as the unit usage consideration) based on the rarity of the target data 20. Then, the consideration calculation device 2000 calculates the usage consideration of the target data 20 by multiplying the unit usage consideration of the target data 20 by a coefficient based on the usage of the target data 20 (for example, the usage itself).
  • the number of times the target data 20 is used is counted for each predetermined period such as one month.
  • the provider of the target data 20 is informed of the usage consideration of the target data 20 according to the number of times the target data 20 is used in the predetermined period and the rarity of the target data 20. It is possible to realize operations such as "pay in bulk".
  • the consideration for using the target data 20 in a predetermined period does not have to be proportional to the degree of use of the target data 20.
  • a plurality of numerical ranges such as "1 time or more and n1 times or less” and “more than n1 times and n2 times or less” are provided, and a unit is provided for each numerical range.
  • the consideration calculation device 2000 multiplies the correction coefficient corresponding to the numerical range to which the number of times the target data 20 is used in a predetermined period belongs to the unit usage consideration calculated based on the rarity of the target data 20.
  • the usage consideration to be paid to the provider of the target data 20 for the predetermined period is calculated.
  • the number of times the target data 20 is used may be counted as the number of target applications 10 using the target data 20, or may be counted as a total number without distinguishing the target applications 10.
  • the number of times the target data 20 is used may be set to 2 or 5.
  • the number of target applications 10 using the target data 20 is defined as the number of times the target data 20 is used.
  • the number of times the target data 20 is used is counted as a total number without considering which target application 10 used the target data 20.
  • the target data 20 is data used for realizing the target application 10.
  • the target data 20 is a subprogram that realizes a part of the processing performed by the target application 10.
  • the developer of the target application 10 develops the target application 10 by using the target data 20 as a subprogram that realizes a part of the functions.
  • the target data 20 may or may not be incorporated as a part of the target application 10.
  • the subprogram is executed independently of the target application 10. Then, the target application 10 requests a specific process from the subprogram, and acquires the process result from the subprogram.
  • the subprogram executed independently of the target application 10 may be executed on the computer on which the target application 10 is executed, or may be executed on a computer different from the computer on which the target application 10 is executed. Good. In the latter case, for example, the subprogram is executed on a server machine communicatively connected to the computer on which the target application 10 is executed. For example, as described above, a form in which the trained model is provided on the server can be considered.
  • the target data 20 which is a subprogram does not necessarily have to be used from the time of development of the target application 10.
  • the target application 10 is configured to cause an external subprogram to execute a part of the processing, the subprogram used by the target application 10 can be easily changed even after development. ..
  • Some programs require a lot of know-how and specialized knowledge to develop. For example, it can be said that a high-performance trained model is highly rare because its construction requires know-how and specialized knowledge regarding model configuration (for example, layer configuration in a neural network) and learning. Therefore, it is appropriate to pay the provider of the subprogram according to the rarity of the subprogram.
  • the target data 20 is not limited to the program.
  • the target data 20 is training data used for learning the trained model used by the target application 10 as described above. Some of the training data used to train the trained model may require a lot of time and effort to collect, or may be difficult to obtain due to limited acquisition methods. Although such learning data is highly scarce, it is appropriate to pay for its provision.
  • the target data 20 is used as the training data, the trained model constructed by using the training data does not have to be the target of compensation payment. For example, in the development of the target application 10, only the target data 20 used for learning the model is procured from the outside, and the development of the program itself can be performed entirely by the developer himself / herself.
  • the consideration calculation device 2000 specifies the target data 20 for which the usage consideration is calculated. Any method can be adopted as the specific method. For example, the consideration calculation device 2000 receives from the user an operation of inputting the identification information of the target data 20 for which the usage consideration is calculated. In this case, the acquisition unit 2020 acquires the identification information specified by the user as the identification information of the target data 20 for which the usage consideration is calculated.
  • the acquisition unit 2020 may acquire a list showing the identification information of each target data 20 for which the usage consideration is calculated. For example, as described above, when the target application 10 is constructed by the user selecting a subprogram, a list of identification information of the subprogram selected by the user is provided to the consideration calculation device 2000. In this case, the consideration calculation device 2000 treats the subprogram (target data 20) specified by each identification information shown in the provided list as the calculation target of the usage consideration.
  • the consideration calculation device 2000 may specify each target data 20 for which the usage consideration is calculated by searching for information related to the target data 20 (hereinafter, target data information).
  • target data information is stored in advance in a storage device accessible from the consideration calculation device 2000.
  • this storage device will be referred to as a target data information storage device.
  • FIG. 5 is a diagram illustrating the target data information in a table format.
  • the table of FIG. 5 is called a table 200.
  • Table 200 shows data identification information 202, provider identification information 204, and application information 206.
  • the data identification information 202 indicates the identification information of the target data 20.
  • the provider identification information 204 indicates the identification information of the provider who provided the target data 20.
  • the application information 206 shows the application identification information 208 and the point of use 210 in association with each other.
  • the application identification information 208 indicates the identification information of the target application 10 using the target data 20.
  • the usage time point 210 indicates a time point when the target data 20 is used by the corresponding target application 10.
  • the consideration calculation device 2000 calculates the usage consideration for each target data 20 provided by a specific provider.
  • the acquisition unit 2020 identifies the identification information of each target data 20 to be calculated for the usage consideration by acquiring the identification information of the target data 20 associated with the identification information of the specific provider. To do.
  • the consideration calculation device 2000 calculates the usage consideration for each target data 20 used by the specific target application 10.
  • the acquisition unit 2020 acquires the identification information of the target data 20 associated with the identification information of the specific target application 10 from the target data information storage device, thereby making the usage consideration calculation target.
  • the identification information of each target data 20 is specified.
  • the consideration calculation device 2000 calculates the usage consideration for each target data 20 used in a specific period.
  • the acquisition unit 2020 calculates the usage consideration by acquiring the identification information of the target data 20 associated with the usage time point included in the specific period from the target data information storage device. The identification information of each target data 20 is specified.
  • the target data 20 may be specified by a combination of the above-mentioned provider, target application 10, and period.
  • the acquisition unit 2020 1) identifies the specific provider from the target data information storage devices. By acquiring the identification information of the target data 20 that meets the two conditions of being associated with the information and 2) being associated with the point of use included in the specific period, the usage consideration can be calculated.
  • the identification information of each target data 20 is specified.
  • the rarity of the target data 20 is an index value indicating the difficulty of creating or obtaining the target data 20. Therefore, it can be said that the rarity of the target data 20 is an index value indicating the high value of the target data 20.
  • the identification information of the target data 20 and the information that can identify the rarity of the target data 20 are associated with each other and stored in a storage device (hereinafter, rarity information storage device). deep.
  • the rarity information may indicate the rarity itself, or may be a parameter used for calculating the rarity.
  • the rarity is represented by a real number of 0 or more and 1 or less.
  • the domain of rarity is not limited to this range.
  • FIG. 6 is a diagram illustrating information stored in the rarity information storage device in a table format.
  • the table of FIG. 6 is called a table 300.
  • Table 300 has data identification information 302 and rarity information 304.
  • the data identification information 302 indicates the identification information of the target data 20.
  • Rarity information 304 indicates the above-mentioned rarity information.
  • the acquisition unit 2020 acquires the rarity information associated with the identification information of the target data 20 for which the usage consideration is calculated from the rarity information storage device (S102).
  • the acquisition unit 2020 calculates the rarity by using the acquired parameter.
  • the rarity of the target data 20 is set to a value specified by the provider.
  • a server machine that accepts the provision of the target data 20 is prepared.
  • the server machine receives a registration request indicating the target data 20 and its rarity.
  • the server machine assigns identification information to the target data 20 indicated in the registration request, associates the identification information with the rarity indicated in the registration request, and stores the identification information in the rarity information storage device. ..
  • the server machine may be the consideration calculation device 2000 or other than the consideration calculation device 2000.
  • the method of acquiring the target data 20 and its rarity is not limited to the method of accepting the request on the server machine.
  • the rarity of the target data 20 may be determined according to the category to which the target data 20 belongs. For example, when the target data 20 is a subprogram, it can be said that the more difficult the program category is, the higher the rarity is. In addition, for example, when the target data 20 is learning data, it can be said that the more difficult the type of learning data is to obtain, the higher the rarity is. For example, it is considered difficult to obtain medical-related learning data (human body images, etc.). Therefore, the learning data corresponding to the category of "medical care" should be highly rare.
  • the rarity information of the target data 20 may directly indicate the rarity corresponding to the category to which the target data 20 belongs, or may indicate the category of the target data 20. In the latter case, a conversion formula for converting the category to rarity is defined.
  • the acquisition unit 2020 acquires the rarity information associated with the identification information of the target data 20 for which the usage consideration is calculated from the rarity information storage device, and uses the above-mentioned conversion formula to acquire the acquired rarity. Convert the category indicated by the information to rarity. By this conversion process, the acquisition unit 2020 acquires the rarity of the target data 20.
  • Example 3 of how to determine the rarity It is assumed that the target data 20 is provided in response to a request from a person, a company, or the like who wants to use the target data 20. In this case, the longer the period from the time when the request for providing the target data 20 is received to the time when the target data 20 is provided (hereinafter, the provision preparation time) is longer, the more the target data 20 is provided. It is considered that the target data 20 has a high degree of rarity because it takes time. Therefore, in such a case, it is preferable to determine the rarity of the target data 20 according to the provision preparation time of the target data 20.
  • a site for example, SNS
  • This site may use an existing SNS or the like, or may be prepared as a dedicated site.
  • a user who wants to use the target data 20 such as a specific program or learning data posts a request for the target data 20 using the site.
  • the provider of the target data 20 views this post and prepares to provide the target data 20.
  • the provider provides the prepared target data 20 by a method such as replying to the above post.
  • the server that manages the site calculates the provision preparation time as the difference between the posting time of the request and the posting time of the target data 20.
  • the rarity information of the target data 20 may directly indicate the rarity determined according to the provision preparation time of the target data 20, or may indicate the provision preparation time of the target data 20. In the latter case, a conversion formula for converting the provision preparation time to rarity is defined.
  • the acquisition unit 2020 acquires the rarity information associated with the identification information of the target data 20 for which the usage consideration is calculated from the rarity information storage device, and uses the above-mentioned conversion formula to acquire the acquired rarity. Convert the provision preparation time indicated by the information to rarity. By this conversion process, the acquisition unit 2020 acquires the rarity of the target data 20.
  • the rarity of the target data 20 may reflect the high evaluation by a person other than the person who requested the provision of the target data 20. For example, if the request for providing the target data 20 (post described above) is supported by many people, the target data 20 has a high degree of rarity that has not been available until now even if many people want to use it. It can be said that it is a thing. Therefore, the higher the evaluation of others for the request for providing the target data 20, the higher the rarity of the target data 20.
  • the high evaluation of others for a request can be specified, for example, as the number of expressions of support for posting a request (for example, the number of Likes on SNS).
  • the rarity information may directly indicate the rarity determined accordingly, or the submission preparation time and the evaluation.
  • An index value (such as the number of Likes) indicating the height of is may be indicated.
  • a conversion formula for calculating the rarity is determined from the index value indicating the submission preparation time and the high evaluation, and the rarity is calculated using this conversion formula.
  • the index value indicating the high evaluation is used to correct the rarity calculated based on the submission preparation time.
  • a reference value for the height of evaluation is set, and the value obtained by dividing the index value indicated by the rarity information by this reference value is calculated as a correction coefficient. Then, the value obtained by multiplying the rarity calculated based on the submission preparation time by this correction coefficient is used as the final rarity.
  • the correction is performed so as to increase the rarity calculated based on the submission preparation time.
  • the correction is performed so as to reduce the rarity calculated based on the submission preparation time. Therefore, the high evaluation of the request of the target data 20 can be reflected in the rarity of the target data 20.
  • the rarity may be determined only by the evaluation height of the target data 20 without using the submission preparation time.
  • the number of views of the request for providing the target data 20 may be recorded, and the rarity may be determined using the number of views. For example, the higher the ratio of the number of approvals to the number of views, the more people who have viewed the request have expressed their support. Therefore, for example, as the index value of the evaluation height of the target data 20 described above, a value obtained by dividing the number of support for the request by the number of views is used.
  • the rarity of the target data 20 may be determined according to the amount of training data provided as the target data 20. For example, it can be said that the greater the number of learning data provided as the target data 20 (such as the number of image files for learning), the higher the rarity. Further, it can be said that the greater the types of learning data provided as the target data 20, the higher the rarity. As the number of types of training data, for example, the number of types of objects covered in the learning data used for learning a model for identifying an object included in an image can be handled.
  • the rarity information may directly indicate the rarity determined according to the amount of training data, or may indicate an index value indicating the amount of learning data.
  • the index value representing the amount of training data is the total size of the training data, the number of data (for example, files) included in the training data, or the number of types of data included in the training data.
  • the acquisition unit 2020 acquires the rarity information associated with the identification information of the target data 20 for which the usage consideration is calculated from the rarity information storage device, and uses the above-mentioned conversion formula to acquire the acquired rarity. Convert the index value indicated by the information to rarity. By this conversion process, the acquisition unit 2020 acquires the rarity of the target data 20.
  • the required amount of training data (hereinafter referred to as the required amount) is defined.
  • the conversion formula calculates the rarity as a ratio (that is, the degree of coverage) to the required amount of the learning data provided as the target data 20.
  • the required amount both the number of required learning data files and the number of types are determined.
  • the above conversion formula is "the ratio of the number of training data files indicated by the rarity information to the number of files defined as the required amount" and "the number of types of training data indicated by the rarity information and necessary.
  • the rarity of the target data 20 is calculated by calculating the "ratio with the number of types of training data defined as the quantity" and using these two ratios (for example, multiplying them).
  • the calculation unit 2040 calculates the usage consideration of the target data 20 according to the rarity of the target data 20 (S104). For example, a conversion formula for converting rarity into consideration for use is defined in advance. The calculation unit 2040 calculates the usage consideration of the target data 20 by inputting the rarity acquired by the acquisition unit 2020 into this conversion formula.
  • this conversion formula can be defined as a monotonous non-decreasing function with the rarity as the input and the usage consideration as the output.
  • the domain of rarity may be divided into a plurality of numerical ranges, and the usage consideration may be determined in association with each range.
  • the conversion formula for calculating the usage consideration specifies the numerical range to which the input rarity belongs, and outputs the usage consideration defined in association with the numerical range.
  • elements other than the rarity of the target data 20 may be further used in calculating the usage consideration of the target data 20.
  • the usage consideration of the target data 20 is calculated based on the usage degree of the target data 20.
  • the standard usage unit price (unit usage unit price described above) is calculated as the usage consideration according to the rarity.
  • the calculation unit 2040 calculates the usage unit price of the target data 20 by multiplying the unit usage unit price calculated according to the rarity by a correction coefficient based on the usage of the target data 20.
  • the correction coefficient is the usage of the target data 20 itself.
  • a plurality of numerical values may be provided for the degree of utilization, and correction coefficients may be determined in association with each numerical range.
  • the calculation unit 2040 calculates the usage consideration of the target data 20 by multiplying the unit usage unit price by a correction coefficient corresponding to the numerical range to which the usage of the target data 20 belongs.
  • the correction coefficient may be the ratio of the utilization of the target data 20 to the predetermined reference value.
  • the calculation unit 2040 calculates the correction coefficient by dividing the usage of the target data 20 by the reference value, and calculates the usage consideration of the target data 20 by multiplying the correction coefficient by the unit usage unit price.
  • the consideration calculation device 2000 outputs the usage consideration of the calculated target data 20.
  • the information indicating the usage consideration of the target data 20 output by the consideration calculation device 2000 is referred to as output information.
  • the output destination of the output information is arbitrary.
  • the output information is displayed on a display device connected to the consideration calculation device 2000.
  • the output information is stored in a storage device accessible from the consideration calculation device 2000.
  • the output information is transmitted to another device communicably connected to the consideration calculation device 2000.
  • the output information may collectively indicate the usage consideration for a plurality of related target data 20.
  • the consideration calculation device 2000 calculates the usage consideration for each target data 20 provided by a specific provider.
  • the output information is information in which the identification information of the target data 20 and the usage consideration of the target data 20 are associated with each target data 20 provided by the specific provider together with the identification information of the specific provider. Is shown.
  • the output information may indicate the total value of the usage consideration of the target data 20. According to the output information described here, it is possible to easily grasp the usage consideration to be paid to a specific provider.
  • the usage consideration is calculated for each target data 20 used by the specific target application 10.
  • the identification information of the specific target application 10 and the identification information of the target data 20 and the usage consideration of the target data 20 are associated with each target data 20 used by the target application 10. Information is shown. Further, the output information may indicate the total value of the usage consideration of the target data 20. According to the output information described here, the cost of using the target data 20 for realizing the specific target application 10 can be easily grasped.
  • the acquisition unit that acquires the rarity of the target data, and Based on the acquired rarity, it has a calculation unit for calculating the usage consideration, which is the consideration for the usage of the target data in the target application.
  • the target data is a consideration calculation device that is a subprogram that realizes a part of the processing performed by the target application or is data used for creating the subprogram.
  • the subprogram is a trained model.
  • the data used for creating the subprogram is the learning data used for learning the model.
  • the rarity of the target data is so high that it is difficult to provide the data. From 3.
  • the consideration calculation device determines the category to which the target data belongs.
  • the rarity of the target data is determined according to the length of the provision preparation time, which is the time from the time when the request for the provision of the target data is made to the time when the target data is provided.
  • the rarity of the target data is determined according to the length of the provision preparation time and the high evaluation of the request for the provision of the target data.
  • the target data is learning data used for learning a model used by the target application.
  • the rarity of the target data is determined according to the amount of learning data included in the target data.
  • the rarity of the target data is determined according to the ratio of the amount of training data contained in the target data to the amount of training data required for training the model.
  • the consideration calculation device described in. 10 The calculation unit calculates the usage consideration of the target data based on the rarity of the target data and the degree of use of the target data by the target application. 9.
  • the consideration calculation device according to any one of 9. 11.
  • a control method performed by a computer For the target data used in the target application, the acquisition step to acquire the rarity of the target data, and Based on the acquired rarity, it has a calculation step of calculating the usage consideration, which is the consideration for the usage of the target data in the target application.
  • a control method in which the target data is a subprogram that realizes a part of processing performed by the target application, or is data used for creating the subprogram.
  • the subprogram is a trained model, 11.
  • the control method described in. 13 The data used for creating the subprogram is the learning data used for learning the model.
  • the rarity of the target data is so high that it is difficult to provide the data.
  • the control method according to any one. 15.
  • the rarity of the target data is determined according to the category to which the target data belongs.
  • the control method described in. 16 The rarity of the target data is determined according to the length of the provision preparation time, which is the time from the time when the request for the provision of the target data is made to the time when the target data is provided.
  • the rarity of the target data is determined according to the length of the provision preparation time and the high evaluation of the request for the provision of the target data.
  • the target data is learning data used for learning a model used by the target application.
  • the rarity of the target data is determined according to the amount of learning data included in the target data.
  • the control method described in. 19 The rarity of the target data is determined according to the ratio of the amount of training data contained in the target data to the amount of training data required for training the model.
  • the consideration for using the target data is calculated based on the rarity of the target data and the degree to which the target data is used by the target application. From 19.
  • the control method according to any one. 21. 11. From 20.
  • Target application 20 Target data 200
  • Table 202 Data identification information 204 Provider identification information 206
  • Application information 208
  • Application identification information 210
  • Time of use 300
  • Table 302 Data identification information 304
  • Rarity information 1000

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