Information processing system, information processing method, and program

Abstract

[Problem] To provide, for example, an information processing system that is capable of increasing motivation to visit a marketplace. [Solution] One aspect of the present invention provides an information processing system comprising at least one processor. In the information processing system, the processor generates, in a generation step, prediction data representing information that is predicted to be made public in the future in a marketplace in which creation data created by creators can be sold and purchased. In the publication step the generated prediction data is made public in the marketplace.

Description

Information processing system, information processing method and program

[0001] The present invention relates to an information processing system, an information processing method, and a program.

[0002] Patent document 1 discloses a technology for acquiring store usage information relating to a user who uses a store that sells goods or services through electronic commerce, and based on the acquired usage information, generating content including a coupon issued by a store that the user has used in the past if the purchase item viewed by the user is sold by that store, and distributing the generated content to the user.

[0003] Japanese Patent Application Laid-Open No. 2020-013575

[0004] The more users visit a marketplace, the more likely it is that products and other items sold there will catch the attention of users, increasing the chances of transactions.

[0005] In view of the above circumstances, the present invention provides an information processing system and the like that can increase motivation to visit a marketplace.

[0006] According to one aspect of the present invention, there is provided an information processing system including at least one processor, in which the processor generates prediction data representing information predicted to be published in the future in a marketplace where creative data created by creators can be bought and sold, and publishes the generated prediction data in the marketplace in the publishing step.

[0007] According to this embodiment, it is possible to increase the motivation to visit the marketplace.

[0008] 1 is a diagram showing the overall configuration of the marketplace system 1. FIG. 2 is a diagram showing the hardware configuration of the server device 10. FIG. 3 is a diagram showing the hardware configuration of the user terminal 20. FIG. 4 is an activity diagram showing an example of display processing. FIG. 5 is a diagram showing an example of a login screen. FIG. 6 is a diagram showing an example of a main screen. FIG. 7 is a diagram showing an example of a predicted data screen. FIG. 8 is a diagram showing an example of a purchased screen. FIG. 9 is a diagram showing an example of a setting change screen. FIG. 10 is a diagram showing an example of a changed main screen. FIG. 11 is a diagram showing another example of a predicted data screen. FIG. 12 is a diagram showing an example of a changed main screen. FIG. 13 is a diagram showing an example of a remuneration table. FIG. 14 is a diagram showing another example of a disclosure mode table. FIG. 15 is a diagram showing another example of a disclosure mode table. FIG. 16 is a diagram showing another example of a disclosure mode table. FIG. 17 is a diagram showing an example of a budget / actual comparison screen.

[0009] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the accompanying drawings. Various features shown in the following embodiments can be combined with each other.

[0010] Incidentally, a program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable recording medium, or may be provided so as to be downloadable from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0011] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0012] In one embodiment, the term "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage or current, high or low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on the circuit in the broad sense.

[0013] Furthermore, a circuit in a broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, a processor, a memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes application specific integrated circuits (ASICs), programmable logic devices (e.g., simple programmable logic devices (SPLDs), complex programmable logic devices (CPLDs), and field programmable gate arrays (FPGAs)), etc.

[0014] <Embodiment> 1. System Configuration The system configuration according to the embodiment will be described below. Fig. 1 is a diagram showing the overall configuration of a marketplace system 1. Fig. 1 shows an overview of each device included in the marketplace system 1 and the users who use those devices. Each overview will be explained as needed, with reference to other figures.

[0015] The marketplace system 1 is an information processing system that executes information processing for providing a marketplace, which is a trading venue for products, services, etc. Hereinafter, the term "marketplace" will be used to refer to the marketplace provided by the marketplace system 1. While the marketplace can handle a variety of products, services, etc., the following describes a case in which NFTs (Non-Fungible Tokens) are handled as trading objects.

[0016] NFTs are non-fungible digital tokens (also known as "non-fungible tokens") issued on a blockchain. Blockchain is a technology that directly connects devices on a network and uses cryptography to process and record transactions in a decentralized manner. In marketplaces, for example, NFTs representing creative data created and listed by creators are traded. Creative data listed on marketplaces includes NFTs representing artwork, games, photographs, music, and the like.

[0017] The marketplace system 1 includes a communication line 2, a blockchain system 3, a server device 10, and multiple user terminals 20. The blockchain system 3 is a system that provides an environment in which blockchain technology can be used. The blockchain system 3 includes a P2P network 4 and multiple blockchain node devices 5.

[0018] The P2P network 4 (P2P = Peer to Peer) is a distributed network in which terminals are directly connected to each other on an equal basis to share each other's data and functions. The communication line 2 includes the Internet, etc., and mediates the exchange of data between devices connected to the line. The P2P network 4 is normally part of the communication line 2, but is shown separately in Figure 1 for ease of explanation. Multiple BC node devices 5 (BC = Block Chain) are connected to each other via the P2P network 4 to form a BC system 3.

[0019] The BC system 3 divides transaction data, which indicates the history of past processing, into blocks of a certain capacity, and generates a blockchain by arranging and linking these blocks in order. The BC system 3 assigns each block the hash value of the previous block, and distributes and stores the generated blockchain among multiple BC node devices 5. In this way, the BC system 3 improves the tamper-resistance of the blockchain.

[0020] The server device 10 is an information processing device that executes information processing to operate the marketplace. The server device 10 performs processes such as authenticating marketplace users, presenting information about the NFTs that are the subject of the transaction, and making payments resulting from NFT transactions. The server device 10 executes NFT-related processes by connecting to the P2P network 4 and participating in a network that shares a blockchain.

[0021] The server device 10 trades rights related to the trading subject, such as NFTs, via an exchange connected to the blockchain system 3. The exchange is a system that mediates transactions using blockchain. The server device 10 purchases virtual currency used at the exchange, stores it in a wallet, and connects the wallet to the exchange to pay costs required for registering NFTs, etc., with the virtual currency.

[0022] The user terminal 20 is a terminal for users of the marketplace system 1, and is, for example, a personal computer, a smartphone, or a tablet terminal. More specifically, the user terminal 20 is an information processing device that has a display means and an operation reception means, and performs information processing such as displaying a marketplace screen on the display means and receiving operations performed by the user on the operation reception means. Users operate the user terminal 20 to access the marketplace, purchase products such as NFTs sold there, and register and sell their own creations on the marketplace.

[0023] The server device 10, for example, executes an authentication process to authenticate a user who uses the user terminal 20. The server device 10, for example, stores authentication information (such as a user ID and a password) for authenticating a user who uses the marketplace system 1, and authenticates a user who inputs the authentication information. By authenticating a user, the server device 10 can restrict access to data, assign identification information to data input by the user to make the data identifiable, and save settings made by the user.

[0024] The server device 10 also executes a display control process for displaying images on the user terminal 20. The server device 10 performs processes such as generating and transmitting an HTML (Hyper Text Markup Language) file as the display control process, and causes the user terminal 20 to display a web page showing a system screen using a browser function. Note that the user terminal 20 may install an application program for using the marketplace system 1, and the server device 10 may perform processes such as generating and transmitting display data for that application as the display control process. By performing these display control processes, the server device 10 causes the user terminal 20 to display various images. In other words, the images displayed on the display means of the user terminal 20 can be rephrased as images that the server device 10 causes the user terminal 20 to display.

[0025] 2. Hardware Configuration The hardware configuration according to the embodiment will be described below. Fig. 2 is a diagram showing the hardware configuration of the server device 10. The server device 10 includes a control unit 11, a storage unit 12, a communication unit 13, and a bus 14. The bus 14 electrically connects the various units included in the server device 10.

[0026] (Control Unit 11) The control unit 11 is, for example, a central processing unit (CPU) not shown. The control unit 11 realizes various functions related to the marketplace system 1 by reading out predetermined programs stored in the storage unit 12. In other words, information processing by software stored in the storage unit 12 is specifically realized by the control unit 11, which is an example of hardware, and can be executed as each functional unit included in the control unit 11. These will be described in further detail in the next section. Note that the control unit 11 is not limited to being single, and multiple control units 11 may be provided for each function. A combination of these may also be used.

[0027] (Memory Unit 12) The memory unit 12 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the marketplace system 1 executed by the control unit 11, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to program calculations. The memory unit 12 stores various programs, variables, etc. related to the marketplace system 1 executed by the control unit 11.

[0028] (Communication Unit 13) The communication unit 13 is configured to be able to transmit various electrical signals from the server device 10 to external components. The communication unit 13 is also configured to be able to receive various electrical signals from the external components to the server device 10. More preferably, the communication unit 13 has a network communication function, which may enable communication of various information between the server device 10 and external devices via the P2P network 4 or the communication line 2.

[0029] 3 is a diagram showing the hardware configuration of the user terminal 20. The user terminal 20 includes a control unit 21, a storage unit 22, a communication unit 23, an input unit 24, an output unit 25, and a bus 26. The bus 26 electrically connects the various units included in the user terminal 20.

[0030] (Input Unit 24) The input unit 24 has keys, buttons, a touch screen, a mouse, etc., and receives input from the user. The input unit 24 may also have a microphone and have a function of receiving voice input from the user.

[0031] (Output unit 25) The output unit 25 has a display, a speaker, etc., and displays visual information generated in a manner that is visible to the user, such as a screen, an image, an icon, text, etc., on the display surface of the display, and outputs sound including voice.

[0032] 3. Information Processing The following describes information processing according to the embodiment. In the following description, the server device 10 and the user terminal 20 are described as the subjects of each information processing, but this information processing is executed by at least one processor included in the marketplace system 1, i.e., a processor included in the control unit of each device. The marketplace system 1 executes, for example, a display process that displays a usage screen for using the marketplace.

[0033] 4 is an activity diagram showing an example of the display process. The display process is initiated when a login screen for using the marketplace provided by the marketplace system 1 is displayed on the user terminal 20 and a user registered as a user of the marketplace system 1 performs a login operation.

[0034] 5 is a diagram showing an example of a login screen. The login screen C1 shown in FIG. 5 displays a character string "Please enter your login information," a user ID input field D11, a password input field D12, and a login button B11. The user terminal 20 accepts input into the input fields D11 and D12 and an operation on the login button B11 as a login operation, and transmits login data indicating the entered user ID and password to the server device 10 (activity A11).

[0035] The server device 10 authenticates the user based on the transmitted login data (activity A12). Next, the server device 10 acquires user information of the authenticated user (activity A13). The user information may include, for example, screen settings, but here we will explain the case where the user is logging in for the first time. Next, the server device 10 acquires information about transactions taking place in the marketplace (activity A14).

[0036] The transaction information includes information about the object being traded (e.g., NFT) and a history of past transactions (e.g., the purchased NFT, the seller, the buyer, the purchase date and time, and the purchase price). Based on the acquired transaction history, the server device 10 generates first prediction data (activity A21) and second prediction data (activity A22). The first prediction data is data indicating the object of trade predicted to be traded in the future, and the second prediction data is data indicating future transaction trends. The first prediction data and the second prediction data will be described in detail later with reference to the figures.

[0037] The server device 10 generates a main screen of the marketplace using the generated first prediction data and second prediction data (activity A23). The server device 10 transmits screen data representing the generated main screen to the user terminal 20. The user terminal 20 displays the main screen represented by the transmitted screen data (activity A24).

[0038] 6 is a diagram showing an example of a main screen C2, which displays a category selection tab B21, a display condition button B22, a display field D21 for first forecast data, a display field D22 for second forecast data, and a display field D23 for a list of trading objects.

[0039] The category selection tab B21 displays tabs for selecting NFT categories: "All," "Art," "Games," "Photos," and "Music." The categories are not limited to these and may include various categories that can be traded with NFTs, such as profile pictures, in-game items, or virtual real estate. The display field D23 displays a list of collections in the category selected in the category selection tab B21. A collection refers to a series associated with one or more NFT works (i.e., items).

[0040] Display field D23 displays the collection's "Rank," "Collection," "Lowest Price," and "Total Transaction Volume." "Rank" displays the ranking of the total transaction volume of the NFT. "Collection" displays the name and logo image of the collection. "Lowest Price" displays the lowest price of the items in the collection. "Total Transaction Volume" displays the total amount of virtual currency paid when items in the collection have been traded so far.

[0041] The display condition button B22 is an image for selecting the display conditions of collections to be preferentially displayed in the display field D23. For example, when the "Trending" button is operated, collections with many recent transactions are preferentially displayed, and when the "Top" button is operated, collections with the largest total transaction volume since being put up for sale are preferentially displayed. Furthermore, when the time period buttons "1h," "6h," "24h," and "7d" are operated, the display conditions are determined based on the transaction volume over the past hour, 6 hours, 24 hours, and 7 days. When the "View All" button is operated, a list of all collections listed in the selected category is displayed.

[0042] Display field D21 displays first predicted data E21. The first predicted data E21 is creative work data that indicates creative works that are predicted to be released in the marketplace in the future. In the example of FIG. 6 , display field D21 displays an image of a picture of a lizard with four legs and six hands as the first predicted data E21, representing an NFT that is predicted to be put up for sale in the future.

[0043] The server device 10 generates the first prediction data using, for example, a first prediction AI (Artificial Intelligence). The first prediction AI is an AI that has the function of predicting future events from past events. The first prediction AI, for example, inputs NFT transaction history older than a certain point in the past, and performs machine learning that determines NFTs traded after that point in time and with a large trading volume as correct answers, thereby realizing a prediction function that predicts NFTs whose transactions will increase in the future.

[0044] The server device 10 inputs the latest NFT transaction history conducted on the marketplace into the first prediction AI that has undergone machine learning as described above, and generates the transaction objects output by the first prediction AI as first prediction data. Note that the method of generating the first prediction data is not limited to this. The server device 10 may be equipped with a first algorithm that predicts, based on past transaction history, which NFTs will be traded frequently in the future based on a rules-based method, and generate the first prediction data based on the first algorithm and the latest NFT transaction history.

[0045] The display field D22 displays second forecast data E22. The second forecast data E22 is data showing a forecast of future transactions, and in the example of Fig. 6, the forecast results are shown as a line graph with the vertical axis representing transaction volume and the horizontal axis representing time. In detail, the second forecast data E22 represents the transition of the forecast value of the transaction volume of a specified transaction object (hereinafter referred to as the "forecast object") during a specific future period (hereinafter referred to as the "forecast period").

[0046] In the example of Figure 6, the second forecast data E22 has a forecast period of "June" and a forecast object of "collection," and shows forecast lines F21 and F22 indicating the trading volume from the past to the forecast period (six months into the future) for the collection with the most current transactions, forecast lines F23 and F24 indicating the trading volume from the past to the forecast period for the collection predicted to have the most future trading volume, and logo images of those collections.

[0047] The server device 10 realizes a prediction function that predicts future changes in the transaction volume of a collection, for example, by having the second prediction AI perform machine learning using the transaction history of a collection older than a certain point in the past as input and the transition in the transaction volume of a collection newer than that point in time as a correct answer. Note that the method of generating the second prediction data is not limited to this. The server device 10 may be equipped with a second algorithm that predicts future transitions in the transaction volume of each collection based on a rule from past transaction history, and generate the second prediction data based on the second algorithm and the latest transaction history of each collection.

[0048] If a user logs in more than once on the same day, the server device 10 displays the first and second prediction data generated initially on the second and subsequent logs. If the user logs in on a different day, the server device 10 generates new first and second prediction data and displays them on the main screen C2. Thus, the first and second prediction data are updated every day. Note that the server device 10 may generate the first and second prediction data in advance each day at the timing of the date change, rather than when the user logs in.

[0049] Furthermore, the server device 10 displays the same first prediction data and second prediction data for the same day regardless of which user logs in. Therefore, when a second or subsequent user logs in, the server device 10 displays the first prediction data and second prediction data generated when the first user logged in. Note that the server device 10 may generate and store the first prediction data and second prediction data in advance when the date changes, and read and display them each time a user logs in.

[0050] The server device 10 may generate and display first and second prediction data for each user. In this case, since the transaction history changes depending on the timing of the user's login, different first and second prediction data may be generated. In either case, the server device 10 stores the generated first and second prediction data in association with the date (or with the user if prediction data is generated for each user).

[0051] When an operation to select either the first predicted data E21 or the second predicted data E22 is performed, the user terminal 20 accepts this as a selection operation of predicted data and transmits request data to the server device 10 requesting the display of a predicted data screen related to the selected predicted data (activity A31). Upon receiving the request data, the server device 10 generates the requested predicted data screen and transmits screen data showing the generated predicted data screen to the user terminal 20 (activity A32). The user terminal 20 displays the predicted data screen shown by the transmitted screen data (activity A33).

[0052] First, the predicted data screen for the first predicted data will be described. FIG. 7 is a diagram showing an example of the predicted data screen. The predicted data screen C3 shown in FIG. 7 displays a character string "Details of the first predicted data will be explained," a display field D31 for the first predicted data, a display field D32 for detailed information about the first predicted data, a save link button B31, a display button B32 for the previous day's first predicted data, a purchase button B33 for the first predicted data, and a change settings button B34. The display field D31 displays an enlarged version of the first predicted data E21.

[0053] Additionally, the display field D32 displays a description of the first predicted data E21 and the price for purchasing the first predicted data E21. For example, when the server device 10 causes the first prediction AI to output the first predicted data, it instructs the AI ​​to also output a description of the predicted first predicted data. If the NFT is a work of art, this description may include a description of the depicted object. For example, when performing the above-described machine learning, the first prediction AI also learns the description of the NFT included in the transaction history, thereby realizing the function of generating the description.

[0054] The price of the first prediction data may be a fixed price or a variable price. For example, the server device 10 may increase the price of the first prediction data as the number of users who display the prediction data screen C3 increases. Furthermore, the server device 10 may increase the price of the first prediction data as the number of users who operate the save button B31 (described later) increases.

[0055] When the save button B31 is operated, the server device 10 stores a link to the predicted data screen C3 in association with the user information. As a result, even if the first predicted data displayed on the main screen C2 changes with the change of day, the user who saved the link can display the predicted data screen C3 showing the first predicted data E21. Furthermore, when the display button B32 is operated, the server device 10 reads and displays the first predicted data displayed the previous day. The server device 10 also stores detailed information, allowing it to display past detailed information in the same way as the first predicted data.

[0056] Furthermore, when the purchase button B33 is operated, the user terminal 20 accepts this as a purchase operation for the displayed first prediction data E21, and transmits request data requesting a purchase process for the first prediction data E21 to the server device 10 (activity A41). Upon receiving the transmitted request data, the server device 10 executes a purchase process in which the price of the first prediction data E21 for which the purchase operation was performed is paid from the user's wallet, and once the payment is completed, grants the user ownership of the first prediction data E21 (activity A42).

[0057] Next, the server device 10 generates a purchased screen indicating that the purchase has been completed, and transmits screen data indicating the generated purchased screen to the user terminal 20 (activity A43). The user terminal 20 displays the purchased screen indicated by the transmitted screen data (activity A44).

[0058] 8 is a diagram showing an example of a purchased screen. The purchased screen C4 shown in FIG. 8 displays a character string "Purchase of the first prediction data has been completed," a display field D31 for the first prediction data, a display field D32 for detailed information about the first prediction data, which are the same as those in FIG. 7, and a display setting button B41 for the main screen. The display field D31 displays the purchased first prediction data E21 and an image F41 indicating that the first prediction data E21 has been sold.

[0059] When the display setting button B41 is operated, the server device 10 can set, for example, the first prediction data E21 purchased by the user to always be displayed on the user's main screen C2 (even on other days). The server device 10 may also simultaneously display the first prediction data for that day. The server device 10 may also display the first prediction data E21 as is on the user terminal 20 of the purchaser, and display the first prediction data E21 with an image F41 indicating that the data has been sold superimposed on the user terminals 20 of other users. This configuration can make it widely known that the first prediction data is available for purchase, and can also emphasize that the more purchases there are, the higher the value of the first prediction data.

[0060] When the setting change button B34 shown in Fig. 7 is operated, the server device 10 displays a screen for changing the settings. Fig. 9 is a diagram showing an example of the setting change screen. The setting change screen C5 shown in Fig. 9 displays an input field D51 for the type of first prediction data to be displayed, an input field D52 for the number, an input field D53 for the frequency, and an input field D54 for the future time point to be predicted (hereinafter referred to as the "prediction time point").

[0061] The input field D51 is used to input the type of first predicted data to be displayed on the main screen C2. The types of first predicted data may include, for example, the categories of art, games, photos, and music shown in FIG. 6 , as well as profile images, in-game items, or virtual real estate. These types may be input by the user, or may be switched using the up and down buttons on the right. It should be noted that the input field D51 may be able to input multiple types.

[0062] The number of first prediction data items to be displayed on the main screen C2 is input in the input field D52. The input field D52 is initially set to "1", but any number greater than 1 can be input. The user may input a numerical value for these numbers, or may increase or decrease the number using the plus and minus buttons on the right.

[0063] The input field D53 is used to input the frequency at which the first prediction data displayed on the main screen C2 changes. The input field D53 is initially set to "daily," but other frequencies such as every 6 hours, every 12 hours, every 2 days, or every week can also be input. These frequencies may be input by the user, or may be increased or decreased using the plus and minus buttons on the right.

[0064] The input field D54 is used to input a future time point represented by the first prediction data displayed on the main screen C2. For example, the input field D54 initially has "six months later" input, but other times, such as one month later, three months later, one year later, or three years later, can also be input. These future times may be input by the user, or may be increased or decreased using the plus and minus buttons on the right.

[0065] When the user terminal 20 displays the setting change screen C5 and accepts input into each input field as a setting operation (activity A51), it transmits request data requesting the display of a main screen reflecting the settings to the server device 10. Upon receiving the request data, if additional first predicted data needs to be generated to display the set settings, the server device 10 generates the necessary first predicted data (activity A52).

[0066] The necessary first prediction data may be, for example, additional first prediction data, a different type of first prediction data, or first prediction data with a different prediction time point. The server device 10 generates a main screen including the newly generated first prediction data (activity A53) and transmits screen data showing the generated main screen to the user terminal 20. The user terminal 20 displays the main screen shown in the transmitted screen data (activity A54).

[0067] Fig. 10 is a diagram showing an example of a changed main screen. In the main screen C2 shown in Fig. 10, four first predicted data E21, E23, E24, and E25 are displayed in the first predicted data display field D21. That is, the example of Fig. 10 shows a display in which the number "4" is set in the input field D52 shown in Fig. 9.

[0068] The method of displaying the first prediction data is not limited to this. For example, the server device 10 may arrange as many display columns as the number of first prediction data, each having the same size as the display column D21, and display the first prediction data in each column. Alternatively, the server device 10 may display one piece of first prediction data in the same size as the display column D21, and switch the first prediction data to be displayed each time the display column D21 is operated or after a certain period of time has elapsed. Furthermore, the first prediction data may be configured in more detail. For example, when multiple pieces of first prediction data are displayed, the type, frequency, and prediction time point of each piece of first prediction data may be individually settable.

[0069] Next, the predicted data screen for the second predicted data will be described. Fig. 11 is a diagram showing another example of the predicted data screen. The predicted data screen C6 shown in Fig. 11 displays a character string "You can change the settings of the second predicted data," a display field D61 for the second predicted data, a button B61 for changing the prediction period, a button B62 for changing the prediction target, and a button B63 for changing the indicator on the vertical axis of the graph. The display field D61 displays an enlarged version of the second predicted data E22.

[0070] By operating the change button B61, it is possible to change the prediction period for which the prediction results are displayed by the second prediction data. In the example of Fig. 11, the change button B61 is in the form of a slider, and by moving the slider left or right, the prediction period can be changed from one month to two years. Note that the range of the changeable prediction period is not limited to this, and the prediction period may be changeable from a few days to several decades, for example.

[0071] The change button B62 is an operation image for changing the prediction target predicted by the second prediction data. In the example of FIG. 6, the prediction target is a collection, but it can be changed to, for example, an individual NFT or creator, or a specific category of NFTs. The number of prediction targets may also be changed. The change button B63 is an operation image for changing the indicator on the vertical axis of the graph shown by the second prediction data. In the example of FIG. 6, the indicator on the vertical axis of the graph is the trading volume, but it can be changed to, for example, the average price, maximum price, or trading period of NFTs belonging to the collection.

[0072] 11 is accepted as a setting operation in A51, the server device 10 generates second forecast data based on the set forecast period, forecast target, and vertical axis index (activity A52). The server device 10 then displays a main screen including the generated second forecast data on the user terminal 20 (activities A53 and A54).

[0073] 12 is a diagram showing an example of a changed main screen. In the main screen C2 shown in FIG. 12, the second prediction data display field D22 displays second prediction data E26, in which the prediction period has been changed to two years and the collection to be predicted has been changed. In this way, the server device 10 displays the first prediction data and the second prediction data based on the user's settings. The server device 10 may store these settings in association with the user, and the next time the user displays the main screen, the first prediction data and the second prediction data may be displayed according to the stored settings.

[0074] As described above, the server device 10 functions as an example of a generator that generates prediction data representing information predicted to be released in the future in a marketplace where creative data created by creators can be bought and sold. The server device 10 generates first prediction data, for example, at A21 shown in FIG. 4. As described above, the first prediction data is NFTs predicted to be put up for sale in the future, i.e., predicted to be released in the marketplace in the future, and is an example of prediction data.

[0075] The server device 10 functions as an example of a publishing unit that publishes the generated prediction data in the marketplace. As shown in FIG. 6 and other figures, the server device 10 publishes the generated first prediction data E21 on the main screen C2 of the marketplace. By publishing the first prediction data E21, users can learn not only about items currently listed on the marketplace but also about items that are likely to be listed in the future, thereby increasing the information they need to make a purchase decision.

[0076] Furthermore, since anyone is interested in what will be put up for sale on the marketplace in the future, the motivation to visit the marketplace can be increased compared to when the first prediction data is not made public. Note that, since creative work data traded on the marketplace includes not only images but also data on videos, music, games, etc., the manner in which the first prediction data is made public can include not only displaying the data but also playing videos, outputting sound, displaying demo screens, etc.

[0077] Furthermore, the prediction data includes the same type of data as the creative work data handled on the marketplace, such as the first prediction data. Making such first prediction data public can serve as a reference for creators when creating creative works and inspire them. The prediction data also includes data indicating predictions of future transactions, such as the second prediction data. Making such second prediction data public can serve as a reference for creators when purchasing creative works and can increase interest in the marketplace itself compared to when the second prediction data is not made public.

[0078] Furthermore, the server device 10 functions as an example of a purchasing unit that executes a purchasing process to allow a user to purchase the published prediction data when the user desires to purchase the prediction data. For example, the server device 10 executes a purchasing process to allow a user to purchase the first prediction data E21 at A42 shown in FIG. 4. By making the first prediction data available for purchase in this way, interest in the prediction results can be increased compared to when the prediction data is simply published.

[0079] Furthermore, the server device 10 publishes the first prediction data and the second prediction data according to settings made by the user. According to this aspect, the prediction data can be published in a format (type, number, frequency, prediction time point, prediction period, prediction target, and vertical axis index) that suits the interests of each user, compared to when the prediction data is published uniformly to all users.

[0080] <Modification: Prediction Source Data> When generating the first prediction data, the transaction history is used as described above. More specifically, the first prediction data that is likely to be traded in the future is predicted based on the trends of traded creative data. Among such traded creative data, there may be data that has had a particular impact on the first prediction data. The server device 10 may identify such creative data that is the basis for the prediction of the first prediction data as prediction source data and make the identified prediction source data public.

[0081] When using the first prediction AI, for example, the server device 10 instructs the first prediction AI to output the influence of each transaction history on the first prediction data when inputting the transaction history. The server device 10 identifies the creative data with the highest output influence as the prediction source data. Furthermore, the server device 10 may identify, from among the creative data whose transaction history has been input, the data with the highest similarity to the first prediction data as the prediction source data. The prediction source data made public in this manner can give the impression of being creative data that is connected to the first prediction data, thereby increasing the credibility of the first prediction data compared to when the prediction source data is not made public.

[0082] <Variation: Secondary Use> A purchaser who purchases the first prediction data may make secondary use of the first prediction data. Secondary use includes selling the first prediction data, transferring it free of charge, granting permission to use the first prediction data, posting the first prediction data on one's own SNS (social networking service) or the like, and creating a new work based on the first prediction data. The marketplace system 1 may provide a reward for such secondary use of the first prediction data.

[0083] First, the server device 10 functions as a detection unit that detects secondary use of sold prediction data by a purchaser. The server device 10 detects secondary use, for example, when the first prediction data is traded on a marketplace. Furthermore, the server device 10 manages the first prediction data as an NFT, and both free transfer and setting of usage rights are performed via the server device 10, so that secondary use can also be detected. Furthermore, by having the purchaser register their SNS at the time of purchase, the server device 10 can also detect posts on SNS.

[0084] Next, the server device 10 functions as an example of a provider that, when secondary use of prediction data is detected, provides a reward to the purchaser who performed the secondary use. The server device 10 provides a reward for the secondary use by, for example, transferring a predetermined reward amount from a wallet managed by the marketplace to the purchaser's wallet. By providing a reward in this manner, it is possible to promote the spread of prediction data compared to when no reward is provided.

[0085] The reward for secondary use may be determined according to the type of secondary use. In this case, the server device 10 functions as an example of a detection unit that detects the type of secondary use by the purchaser. The types of secondary use include, for example, "listing and selling" through a marketplace, "transfer" without going through a marketplace, "permission of usage rights" to another person, and "posting on social media." The server device 10 detects the type of secondary use according to a secondary use detection method.

[0086] The server device 10 (an example of a provider) then provides the purchaser with a reward according to the detected form of secondary use. The server device 10 provides the reward based on, for example, a reward table that associates the form of secondary use with a method for setting the reward. The reward is set, for example, as a percentage of a predetermined base price (hereinafter referred to as the "reward percentage"). The base price is, for example, the purchase price of the first prediction data.

[0087] FIG. 13 is a diagram showing an example of a remuneration table. In the remuneration table TB1 shown in FIG. 13, the remuneration rates "N1%, N2%, N3%, and N4%" are associated with the secondary use forms of "sale," "free transfer," "licensing of usage rights," and "SNS posting." N1 to N4 are, for example, values ​​greater than 0 and less than 100. The server device 10 stores the purchase price of the first prediction data, and when secondary use is detected, determines the remuneration amount by multiplying the purchase price by the remuneration rate associated with the detected secondary use form in the remuneration table TB1.

[0088] However, the method of determining the reward is not limited to this. For example, a fixed amount may be set for each form of secondary use. Furthermore, the base price may not be the purchase price of the first prediction data, but may be the price at which the first prediction data is "listed for sale," "transferred," or "licensed for use." Furthermore, the reward may be increased or decreased depending on the period of time the first prediction data has been held (for example, the longer the data has been held, the larger the reward may be, or the shorter the period of holding, the larger the reward). In either case, it is possible to further promote diffusion in a specific form compared to when the reward is set uniformly.

[0089] <Modification: Event Data> The prediction data is not limited to the first prediction data and the second prediction data described above. The prediction data may include data indicating an event to be held in the marketplace as third prediction data. An event is an event that can be participated in from the marketplace and is held for a specific period only, such as an event that offers a discount on the price of specific creative data, an event that makes creative data available to a limited audience, an event that distributes videos by creators, or an exhibition event of previously released prediction data.

[0090] The server device 10 realizes a prediction function for predicting future events, for example, by having the third prediction AI perform machine learning using an event history older than a certain point in the past as input and an event history newer than that point in time as a correct answer. Note that the method for generating the third prediction data is not limited to this. The server device 10 may be provided with a third algorithm that predicts future events based on rules from the history of past events, and generate the third prediction data based on the third algorithm and the history of the most recent events.

[0091] Since the information about a predicted event is merely predicted content, it is easier to link to the image of the event that the user is expecting compared to information about a confirmed event, and can increase interest in the event.

[0092] The server device 10 may also execute a process of acquiring a questionnaire regarding the published third prediction data. The server device 10 executes a process of, for example, displaying an "anticipation button" indicating anticipation of the event along with the third prediction data, counting the number of times the anticipation button is pressed, and notifying, for example, the event administrator. According to this aspect, the predicted event can be made more likely to occur through the user's own actions, further increasing user interest.

[0093] 9 and 11, the user sets the method for disclosing the first prediction data and the second prediction data, but the server device 10 may disclose the prediction data in accordance with the user. In this case, the server device 10 first functions as an example of an acquisition unit that acquires user information about users who visit the marketplace.

[0094] The user information includes, for example, the user's listing history, browsing history, and purchase history on the marketplace. The browsing history includes the browsing history of NFTs and collections, as well as the browsing history of the first predicted data, the second predicted data, and the third predicted data. The server device 10 (an example of a publishing unit) then publishes the predicted data in a format corresponding to the acquired user information. The format corresponding to the user information is, for example, the type of predicted data. The server device 10 uses publishing format data that associates the user information with a publishing format.

[0095] 14 is a diagram illustrating an example of a disclosure mode table. In the disclosure mode table TB2 illustrated in FIG. 14 , "browsing time × 1 pt + number of purchases × 20 pt + number of items listed × 40 pt" is associated as a disclosure mode with the user information "maximum art pt," "maximum games pt," "maximum photography pt," and "maximum music pt" ("pt" indicates points), and the predicted data types "art," "games," "photos," and "music." Based on the acquired user information, the server device 10 calculates points for each NFT category according to the NFT browsing time, the number of NFTs purchased, and the number of NFTs listed.

[0096] The server device 10 then determines the category that will result in the greatest number of calculated points as the type of predicted data, and generates NFTs of the determined type as first predicted data. For example, the server device 10 generates and displays NFTs of game works as first predicted data on the main screen of a user who will earn the greatest number of points from game NFTs, and generates and displays NFTs of music works as first predicted data on the main screen of a user who will earn the greatest number of points from music NFTs.

[0097] According to this aspect, compared to when the disclosure mode is uniform, the first prediction data of the type that users are more interested in is disclosed, which can attract users' attention more strongly. As a result, compared to when the disclosure mode of the prediction data is uniform, it is possible to further increase users' motivation to visit the marketplace.

[0098] The mode according to the user information may be the number of predicted data items. In this case, the server device 10 uses disclosure mode data in which the number of predicted data items is associated with the user information as a disclosure mode. FIG. 15 is a diagram showing another example of a disclosure mode table. In the disclosure mode table TB3 shown in FIG. 15 , the numbers of predicted data items "N11," "N12," "N13," and "N14" (N11 to N14 are natural numbers; N11<N12<N13<N14) are associated as disclosure modes with the user information in which "viewing time×1 pt+number of purchases×20 pt+number of items listed×40 pt" is "less than Th1," "at least Th1 but less than Th2," "at least Th2 but less than Th3," and "at least Th3."

[0099] Based on the acquired user information, the server device 10 calculates points according to the browsing time for NFTs across all categories, the number of NFTs purchased, and the number of NFTs put up for sale. The server device 10 then generates the number of first prediction data items associated with the calculated points in the disclosure mode table TB3. According to this configuration, compared to when the disclosure mode is uniform, the more frequently a user uses the marketplace, the more first prediction data items are made public. This can encourage users to visit the marketplace because they want to see more predictions.

[0100] The mode according to the user information may be the frequency at which the prediction data is updated. In this case, the server device 10 uses disclosure mode data in which the update frequency of the prediction data is associated with the user information as a disclosure mode.

[0101] 16 is a diagram showing another example of a disclosure mode table. In the disclosure mode table TB4 shown in FIG. 16, the user information in which "viewing time × 1 pt + number of purchases × 20 pt + number of items listed × 40 pt" is "less than Th11," "at least Th11 but less than Th12," "at least Th12 but less than Th13," and "at least Th13" is associated with the update frequencies of the predicted data "once every N21 days," "once every N22 days," "once every N23 days," and "once every N24 days" (N21 to N24 are natural numbers, N21>N22>N23>N24) as disclosure modes.

[0102] Based on the acquired user information, the server device 10 calculates points according to the browsing time for NFTs across all categories, the number of NFTs purchased, and the number of NFTs listed for sale. The server device 10 then updates the first prediction data to be published at the update frequency associated with the calculated points in the publication mode table TB5. According to this mode, compared to when the publication mode is uniform, the more frequently a user uses the marketplace, the more frequently the first prediction data is updated, which can encourage users to visit the marketplace by wanting to see new predictions one after another.

[0103] The mode according to the user information may be the length of a future period represented by the prediction data (hereinafter referred to as the "predicted period length"). In this case, the server device 10 uses disclosure mode data in which the predicted period length is associated with the user information as a disclosure mode.

[0104] 17 is a diagram showing another example of a disclosure mode table. In the disclosure mode table TB5 shown in FIG. 17, the user information in which "viewing time × 1 pt + number of purchases × 20 pt + number of items listed × 40 pt" is "less than Th21," "21 to less than Th22," "22 to less than Th23," and "23 or more" is associated with the prediction period lengths of the prediction data, "N31 months," "N32 months," "N33 months," and "N34 months" (N31 to N34 are natural numbers, N31<N32<N33<N34), as disclosure modes.

[0105] Based on the acquired user information, the server device 10 calculates points according to the browsing time for NFTs across all categories, the number of NFTs purchased, and the number of NFTs put up for sale. The server device 10 then generates first prediction data for the future for the length of the prediction period associated with the calculated points in the disclosure mode table TB5. According to this mode, compared to when the disclosure mode is uniform, the more frequently a user uses the marketplace, the further into the future first prediction data is made public. This can encourage users to visit the marketplace because they want to see predictions for the future.

[0106] <Variation: Publication of Forecast and Actual Results> Information indicating the results of the prediction, i.e., the forecast and actual results, may be published to users. In this case, when the future time indicated by previously published forecast data arrives, the server device 10 (an example of a publishing unit) publishes the forecast data and the published information for that future time so that they can be compared. For example, the server device 10 displays a link to a screen showing a comparison of forecast and actual results on the main screen, and generates a forecast and actual results comparison screen when the link is operated.

[0107] 18 is a diagram showing an example of a forecast / actual comparison screen. The forecast / actual comparison screen C7 shown in FIG. 18 displays a character string "We have compared the forecast and actual results of the first forecast data published six months ago," a display field D71 for first forecast data published in the past, and a display field D72 for current creative product data. The display field D71 displays the first forecast data E21 shown in FIG. 6 as a forecast published six months ago. The display field D72 displays the currently most traded creative product data E72 as a predicted future point in time, i.e., as current performance.

[0108] Note that, since the server device 10 predicts the first prediction data for "NFTs" in the "art" category, the server device 10 displays NFTs in the same category as actual results alongside the first prediction data, thereby making the predictions and actual results publicly comparable. In this manner, interest in whether the predictions are correct or incorrect can also motivate visitors to visit the marketplace.

[0109] 1 and the like (overall configuration, hardware configuration, functional configuration, etc.) are merely examples, and other configurations may be used as long as they are not inconvenient for implementation. For example, the server device 10 may be distributed across two or more devices, or may be provided in the form of SaaS (Software as a Service) or a cloud computing system. In short, as long as the necessary information processing is performed throughout the marketplace system 1, the devices that perform that information processing may have any configuration.

[0110] The output destination of information or data (hereinafter referred to as "information, etc.") may be another device, a display, a memory unit (including an internal memory unit and an external memory unit), an email address, an account of another system, etc. Acquisition of information, etc. includes acquiring information, etc. transmitted from another device, as well as acquiring information, etc. generated by the device itself. The table, etc. (table, database, etc.) in which parameters are associated is not limited to the illustrated table, etc., and the number of parameters may be reduced or increased. Furthermore, information, etc. corresponding to parameters may be obtained using a mathematical formula, a conditional formula, etc., without using a table, etc.

[0111] The aspects of the above-described embodiment are information processing devices such as the server device 10 and the user terminal 20, and information processing systems such as the marketplace system 1 including the server device 10 and the user terminal 20, but may also be information processing methods. The information processing methods include the same steps as those executed by the information processing system. Furthermore, the aspects of the above-described embodiment may also be programs. The programs cause a computer to execute the same steps as those executed by the information processing system.

[0112] <Additional Notes> Furthermore, the present invention may be provided in the following aspects.

[0113] (1) An information processing system having at least one processor, wherein the processor generates, in a generation step, predictive data representing information predicted to be made public in the future in a marketplace where creative data created by creators can be bought and sold, and, in a publication step, publishes the generated predictive data in the marketplace.

[0114] According to this embodiment, it is possible to increase the motivation to visit the marketplace.

[0115] (2) In the information processing system described in (1) above, the prediction data includes data of the same type as the creative data.

[0116] According to this aspect, it is possible to provide motivation to creators.

[0117] (3) In the information processing system described in (2) above, in the purchase step, the processor executes a purchase process to allow the purchase of the published prediction data if there is a request to purchase the prediction data.

[0118] According to this embodiment, it is possible to increase interest in the results of the prediction.

[0119] (4) In the information processing system described in (3) above, the processor, in the detection step, detects that the sold prediction data has been used secondary by the purchaser, and in the provision step, if the secondary use is detected, provides a reward to the purchaser who performed the secondary use.

[0120] According to this aspect, it is possible to promote the spread of prediction data.

[0121] (5) In the information processing system described in (4) above, the processor detects the type of secondary use by the purchaser in the detection step, and provides the purchaser with a reward according to the detected type of secondary use in the provision step.

[0122] According to this embodiment, diffusion in a specific form can be further promoted.

[0123] (6) In the information processing system described in any one of (1) to (5) above, the forecast data includes data indicating events to be held in the marketplace.

[0124] According to this aspect, interest in the event can be increased.

[0125] (7) In the information processing system described in any one of (1) to (6) above, the processor acquires user information about users visiting the marketplace in the acquisition step, and publishes the prediction data in a manner corresponding to the acquired user information in the publishing step.

[0126] This embodiment can further increase the motivation to visit the marketplace.

[0127] (8) In the information processing system described in (7) above, the aspect is the type of the prediction data.

[0128] According to this aspect, it is possible to attract the user's attention more strongly.

[0129] (9) In the information processing system described in (7) above, the aspect is the number of the prediction data.

[0130] According to this embodiment, the motivation to see more predictions can be linked to the motivation to visit the marketplace.

[0131] (10) In the information processing system described in (7) above, the aspect is a frequency at which the prediction data is updated.

[0132] According to this embodiment, the motivation to see new predictions one after another can be linked to the motivation to visit the marketplace.

[0133] (11) In the information processing system described in (7) above, the aspect is the length of the period into the future represented by the prediction data.

[0134] According to this embodiment, the motivation to see predictions for the future can be linked to the motivation to visit the marketplace.

[0135] (12) In the information processing system described in any one of (1) to (11) above, in the disclosure step, when the future time indicated by the prediction data previously disclosed arrives, the processor discloses the prediction data so that the prediction data can be compared with the information disclosed at that time.

[0136] According to this embodiment, interest in whether the predictions turn out to be correct or incorrect can also serve as a motivation for visiting the marketplace.

[0137] (13) An information processing method, in which a processor included in an information processing system executes each step of the information processing system described in any one of (1) to (12) above.

[0138] According to this embodiment, it is possible to increase the motivation to visit the marketplace.

[0139] (14) A program that causes a computer to execute each step of the information processing system described in any one of (1) to (12) above.

[0140] According to this aspect, it is possible to increase motivation to visit the marketplace. Of course, this is not a limitation. Furthermore, the above-described embodiments and modifications may be implemented in any combination.

[0141] Finally, while various embodiments of the present invention have been described, these are presented by way of example only and are not intended to limit the scope of the invention. The novel embodiments may be embodied in various other forms, and various omissions, substitutions, and modifications may be made without departing from the spirit of the invention. The embodiments and their modifications are intended to be included within the scope and spirit of the invention, as well as within the scope of the inventions and their equivalents as defined in the appended claims.

[0142] 1: Marketplace system, 2: Communication line, 3: BC system, 4: P2P network, 5: BC node device, 10: Server device, 11: Control unit, 20: User terminal, 21: Control unit

Claims

1. An information processing system comprising at least one processor, wherein the processor: in a generation step, generates prediction data representing information predicted to be made public in the future in a marketplace where creative data created by creators can be bought and sold; and in a publication step, publishes the generated prediction data in the marketplace.

2. An information processing system according to claim 1, wherein the prediction data includes data of the same type as the creative work data.

3. An information processing system according to claim 2, wherein the processor, in the purchasing step, executes a purchasing process to allow the purchase of the published prediction data if there is a desire to purchase the prediction data.

4. An information processing system according to claim 3, wherein the processor, in the detection step, detects that the sold prediction data has been used secondary by a purchaser, and, in the provision step, if the secondary use is detected, provides a reward to the purchaser who made the secondary use.

5. An information processing system according to claim 4, wherein the processor, in the detection step, detects the type of secondary use by the purchaser, and in the provision step, provides the purchaser with a reward according to the detected type of secondary use.

6. An information processing system according to any one of claims 1 to 5, wherein the forecast data includes data indicating events to be held in the marketplace.

7. An information processing system according to any one of claims 1 to 6, wherein the processor, in the acquisition step, acquires user information relating to users who visit the marketplace, and in the publishing step, publishes the prediction data in a manner according to the acquired user information.

8. An information processing system according to claim 7, wherein the aspect is a type of the prediction data.

9. An information processing system according to claim 7, wherein the aspect is the number of the prediction data.

10. An information processing system according to claim 7, wherein the aspect is a frequency at which the prediction data is updated.

11. An information processing system according to claim 7, wherein the aspect is the length of a period into the future represented by the prediction data.

12. An information processing system according to any one of claims 1 to 11, wherein the processor, in the disclosure step, when the future time indicated by the prediction data previously disclosed arrives, discloses the prediction data and information disclosed at that time so that they can be compared.

13. An information processing method, in which a processor included in an information processing system executes each step of the information processing system according to any one of claims 1 to 12.

14. A program that causes a computer to execute each step of the information processing system according to any one of claims 1 to 12.

Images