Program and system

Abstract

To perform output attractive to a user when acquiring an item in a game.SOLUTION: A program allows a computer to function as storage means capable of storing data showing an action of a character operated by a user within a virtual space as log data, and as output means for outputting special information regarding a predetermined object by inputting unknown log data pertaining to acquisition of the predetermined object to a learned model that completes learning on the basis of learning data including at least the log data when the character obtains the predetermined object within the virtual space, and the output means outputs the special information in a form including a characteristic element when an acquisition mode of the predetermined object is a particular mode based on the characteristic element different from a normal one.SELECTED DRAWING: Figure 14

Description

[Technical field]

[0001] The present invention relates to a program and a system. [Background technology]

[0002] Conventionally, a technique is known in which a user saves a game. In addition, the user inputs any text information (called "memo information") in association with the save data generated by saving. In this way, a technique is known that makes it easy to manage multiple save data (for example, see Patent Document 1, etc.).

[0003] In a game, a first character is generated based on an input from a player. Next, the player sets in advance a second character to be made to appear in the game separately from the first character. Then, a technique is known in which the story development of the game is changed based on the setting related to the second character (for example, see Patent Document 2, etc.). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2022-188862 A [Patent Document 2] JP 2022-184656 A Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above techniques are unable to provide an output that is attractive to the user when an object such as an item is acquired in a game.

[0006] The present invention aims to provide an output that is attractive to a user when the user acquires an object such as an item in a game. [Means for solving the problem]

[0007] In order to solve the above problems, the present invention provides a program comprising: Computer, a storage means capable of storing data indicating actions of a character operated by a user in a virtual space as log data; When the character obtains a predetermined object in the virtual space, unknown log data relating to the obtaining of the predetermined object is input to a trained model trained based on training data including at least the log data, thereby functioning as an output means for outputting special information relating to the predetermined object; The output means includes: When the acquisition mode of the predetermined object is a special mode based on a characteristic element different from usual, the special information is output in a form including the characteristic element. It is a program. Effect of the Invention

[0008] According to the present invention, when an object such as an item is acquired in a game, an output that is attractive to the user can be provided. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of a system configuration according to an embodiment of the present invention. [Diagram 2] FIG. 13 is a diagram illustrating an example of pre-processing. [Diagram 3] FIG. 11 illustrates an example of an execution process. [Figure 4] FIG. 1 is a diagram showing an example of the overall process of AI learning and execution. [Diagram 5] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Figure 6] FIG. 1 is a network diagram showing an example of an AI configuration. [Figure 7] FIG. 11 is a diagram showing an example of input in the first example. [Figure 8] FIG. 13 is a diagram showing an example of a moving image. [Figure 9] FIG. 13 is a diagram showing an example profile. [Figure 10] FIG. 11 is a diagram showing a first example of output data. [Figure 11] FIG. 11 is a diagram showing a second example of output data. [Figure 12] FIG. 13 is a diagram showing a third example of output data. [Figure 13] FIG. 13 is a diagram illustrating an example of the overall process. [Figure 14] FIG. 2 is a diagram illustrating an example of a functional configuration. [Figure 15] FIG. 13 is a diagram showing a configuration example in which an auxiliary device is used. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an embodiment will be described with reference to the drawings.

[0011] [System configuration example] Fig. 1 is a diagram showing an example of a system configuration according to the present embodiment. For example, as shown in Fig. 1, a system 1 mainly includes user terminals 20A, 20B, and 20C (hereinafter, these may be collectively referred to as "user terminal 20") and a server 11.

[0012] Hereinafter, the person who manages the server 11 will be referred to as the "administrator 5." In addition, the people who operate the user terminals 20A, 20B, and 20C will be referred to as the "user 4A," the "user 4B," and the "user 4C" (hereinafter, these may be collectively referred to as the "user 4").

[0013] The administrator 5 is a person who operates the information processing service provided by the system 1. On the other hand, the user 4 is a person who uses the information processing service provided by the system 1. The administrator 5 and the user 4 differ in which information processing device they operate: the server 11, which is an example of a management device, or the user terminal 20. Hereinafter, the user 4 will be a player of the game, and the administrator 5 will manage the game and the server 11.

[0014] Although the example shown in FIG. 1 has three user terminals 20 and one server 11, the number of servers 11, the number of user terminals 20, the number of administrators 5, and the number of users 4 are not important.

[0015] The server 11 and the user terminal 20 are connected to be able to communicate with each other via a communication network 2. For example, the communication network 2 is the Internet, a mobile communication system (for example, a public line based on 4G (4th Generation, fourth generation mobile communication standard) or 5G (5th Generation, fifth generation mobile communication standard)), a wireless network such as Wi-Fi (registered trademark), or a combination of these.

[0016] The user terminal 20 downloads a program for playing a game (hereinafter referred to as a "game program") from the server 11, or accesses the server 11 to provide a game service. Note that communication with the server 11 is not required to play a game. In other words, the user terminal 20 may download a program or install it from a medium to create an environment for playing a game.

[0017] [Examples of AI (Artificial Intelligence) learning and execution] Hereinafter, AI learns through "pre-processing." AI in the learning stage, i.e., "pre-processing," will be referred to as the "learning model A1." Then, once learning has progressed to a certain extent, the learning model A1 becomes the "trained model A2." Hereinafter, the execution stage in which output processing is executed using the trained model A2 will be referred to as the "execution process."

[0018] The "pre-processing" is performed before the "execution processing". However, the "pre-processing", that is, the trained model A2 may continue to train before the "execution processing".

[0019] [Pre-processing example] 2 is a diagram showing an example of pre-processing. For example, the pre-processing is performed by the server 11.

[0020] The learning model A1 performs learning by inputting learning data D1. That is, the learning model A1 performs so-called "supervised" learning.

[0021] The learning data D1 includes known log data and is data associated with a "correct answer" for this log data. Specifically, the learning data D1 includes log data indicating the game play results and the play process, such as the eleventh data D11, the twelfth data D12, the thirteenth data D13, etc. (including a data group that combines a plurality of data such as the eleventh data D11, the twelfth data D12, and the thirteenth data D13. Hereinafter, simply referred to as "log data"), and the correct answer data D20.

[0022] The learning model A1 learns a correspondence relationship that outputs correct answer data D20 for input of log data based on input of learning data D1.

[0023] The log data will be described in detail later.

[0024] Furthermore, it is preferable that the learning model A1 learns using big data D4. For example, the big data D4 is data on the Internet. However, the big data D4 may be data input by an administrator 5 or the like.

[0025] [Example of execution process] 3 is a diagram showing an example of the execution process. For example, the execution process is performed by the user terminal 20, or by the user terminal 20 and the server 11 working together.

[0026] The trained model A2 is in a state where the trained model A1 has been trained by learning through pre-processing. That is, when the pre-processing shown in Fig. 2 is executed, the trained model A2 is generated. In this way, the trained model A2 trained using the big data D4 as training data is a so-called "generative AI."

[0027] When input data D2 is input, the trained model A2 generates output data D3 for the input data D2.

[0028] The input data D2 is unknown log data, that is, data in which the "correct answer" to the log data is unknown at the time of input. Specifically, the input data D2 includes unknown log data indicating the game play results and the play process, such as the twenty-first data D21, the twenty-second data D22, the twenty-third data D23, ... (including the case of a data group that combines multiple data such as the twenty-first data D21, the twenty-second data D22, and the twenty-third data D23). For example, the input data D2 is composed of the same type of data as the learning data D1.

[0029] When the output data D3 is generated, the output data D3 is transmitted to, for example, the user terminal 20. Thereafter, the user terminal 20 outputs an output screen or the like to the user 4 based on the output data D3.

[0030] The output data D3 and the output based on the output data D3 will be described in detail later.

[0031] Fig. 4 is a diagram showing an example of the overall process of AI learning and execution. The relationship between the pre-processing shown in Fig. 2 and the execution process shown in Fig. 3 is as shown in Fig. 4.

[0032] Note that the pre-processing and the execution processing do not have to be executed in the consecutive order as illustrated in the figure. Therefore, it is not essential that the period in which preparation is performed by the pre-processing and the period in which the execution processing is performed thereafter are consecutive. Therefore, the execution processing may be executed after a time has elapsed from the pre-processing, once the trained model A2 has been created. Also, once the trained model A2 has been generated, the execution processing may be executed by repurposing the trained model A2.

[0033] The learning data D1 and input data D2 are different between the learning process and the execution process. Also, the AI ​​is a learning model A1 in the learning stage, but after a certain amount of learning, it becomes a trained model A2.

[0034] The eleventh data D11 included in the learning data D1 and the twenty-first data D21 included in the input data D2 (hereinafter, the twelfth data D12 and the twenty-second data D22, etc. have the same relationship) are the same data type. That is, the eleventh data D11 and the twenty-first data D21 are both log data.

[0035] The learning data D1 is known data, whereas the input data D2 is unknown data. Specifically, the learning data D1 includes the correct answer data D20, whereas the input data D2 does not include the correct answer data D20. Therefore, the relationship between the log data and the correct answer data D20 of the learning data D1 is known.

[0036] On the other hand, the input data D2 does not include the correct answer data D20, and the "correct answer" for the input data D2 is unknown. The trained model A2 generates output data D3 for the input data D2 based on the correlation between the training data D1 trained in pre-processing and the correct answer data D20.

[0037] A part of the execution process may be replaced by a process using a table, etc. In this way, the pre-processing in the process using a table, etc. (so-called rule-based processing) is a process of preparing for inputting a table (also called a look-up table (LUT) or a mathematical expression, etc.).

[0038] [Example of hardware configuration of information processing device] 5 is a hardware configuration diagram of an information processing device. The information processing device is a server 11, a user terminal 20, etc. Hereinafter, the information processing device is assumed to have the same hardware configuration as the server 11. For example, the information processing device is a workstation or a general-purpose computer such as a personal computer. However, each information processing device may have a different hardware configuration.

[0039] The server 11 mainly includes a processor 111, a memory 112, a storage 113, an input / output interface 114, and a communication interface 115. In addition, each component of the server 11 is connected to a communication bus .

[0040] The processor 111 executes a series of instructions contained in a server program 11P stored in the memory 112 or the storage 113, thereby achieving processing and control.

[0041] The processor 111 is, for example, an arithmetic device and a control device such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an MPU (Micro Processing Unit), an FPGA (Field-Programmable Gate Array), an ASIC (Application Specific Integrated Circuit), or a combination thereof.

[0042] The memory 112 is a main storage device (which is volatile) that stores the server program 11P and data, etc. For example, the server program 11P is loaded from the storage 113. The data includes data input to the server 11 and data generated by the processor 111. For example, the memory 112 is a RAM (Random Access Memory) or other volatile memory.

[0043] The storage 113 is an auxiliary storage device (non-volatile) that stores the server program 11P and data, etc. The storage 113 is, for example, a ROM (Read-Only Memory), a hard disk drive, a flash memory, or other non-volatile storage device. The storage 113 may also be a removable storage device such as a memory card. As another example, the storage 113 may be an external storage device. With this configuration, for example, in a scene where multiple user terminals 20 are used, such as an amusement facility, it becomes possible to collectively update the server program 11P or data.

[0044] The input / output interface 114 is an interface that connects external devices, such as a monitor, an input device (for example, a keyboard or a pointing device), an external storage device, a speaker, a camera, a microphone, and a sensor, to the server 11.

[0045] The processor 111 also communicates with external devices via an input / output interface 114. The input / output interface 114 is, for example, a Universal Serial Bus (USB), a Digital Visual Interface (DVI), a High-Definition Multimedia Interface (HDMI (registered trademark)), wireless, or other terminal.

[0046] The communication interface 115 communicates with other devices (e.g., the user terminal 20, etc.) connected to the communication network 2. For example, the communication interface 115 is a wired communication interface such as a Local Area Network (LAN), or a wireless communication interface such as Wireless Fidelity (Wi-Fi), Bluetooth (registered trademark), or Near Field Communication (NFC).

[0047] However, the information processing device is not limited to the above hardware configuration. For example, the user terminal 20 may further include a sensor such as a camera. Various data acquired by the user terminal 20 using the sensor may be transmitted to the server 11.

[0048] [Examples of learning model and trained model configuration] 6 is a network diagram showing an example of the configuration of an AI. The learning model A1 and the trained model A2 are, for example, AIs having a configuration shown in the following network.

[0049] Hereinafter, the learning model A1 and the trained model A2 will be described as being implemented on the server 11, i.e., on the cloud. However, a part or all of the learning model A1 and the trained model A2 may be implemented on the user terminal 20, etc.

[0050] The network 300 has, for example, an input layer L1, an intermediate layer L2 (also called a "hidden layer"), and an output layer L3.

[0051] The input layer L1 is a layer that inputs data.

[0052] The intermediate layer L2 converts the data input in the input layer L1 based on weights (e.g., coefficients used for multiplication) and biases (e.g., adding a constant), etc. The results of processing in this way in the intermediate layer L2 are transmitted to the output layer L3.

[0053] The output layer L3 is a layer that outputs output contents, etc.

[0054] Then, through learning, weight coefficients (for example, coefficients for input characters or images are changed in learning) and parameters changed through learning are optimized. Note that the network 300 is not limited to the network structure shown in the figure. In other words, the AI ​​may be realized by other machine learning.

[0055] For example, the AI ​​may be configured to perform preprocessing such as dimensionality reduction (for example, a process that converts a relationship of three or more dimensions into a relationship that can be obtained by simple calculations of three dimensions or less) using "unsupervised" machine learning. It is desirable to process the relationship between input and output using simple calculations such as linear expressions. Such calculations can reduce calculation costs.

[0056] In addition, the AI ​​may undergo processing to reduce overfitting, such as dropout. In addition, preprocessing such as dimensionality reduction and normalization may be performed.

[0057] The AI ​​may have a network structure such as a CNN (Convolution Neural Network). In addition, for example, the network structure may have a configuration such as an LLM (Large Language Model), an RNN (Recurrent Neural Network), or an LSTM (Long Short-Term Memory). In other words, the AI ​​may have a network structure other than deep learning.

[0058] The AI ​​may also have hyperparameters. In other words, the AI ​​may be configured such that some of the settings are performed by a user, etc. Furthermore, the AI ​​may specify features to be learned, or the user may set some or all of the features to be learned.

[0059] Furthermore, the learning model A1 and the trained model A2 may use other machine learning. For example, the learning model A1 and the trained model A2 may be preprocessed by normalization using an unsupervised model. Furthermore, the learning may be reinforcement learning (a learning method in which an AI is made to make a choice and an evaluation (reward) is given for the choice, resulting in a larger evaluation), etc.

[0060] In the learning, data expansion or the like may be performed. That is, in order to increase the amount of learning data used in the learning of the learning model A1, a single piece of experimental data or the like may be expanded and pre-processed to generate multiple pieces of learning data. In this way, if the amount of learning data can be increased, the learning of the learning model A1 can be further advanced.

[0061] Furthermore, the learning model A1 and the trained model A2 may be configured to perform transfer learning, fine tuning, or the like. That is, since the user terminal 20 often has a different execution environment for each device, the settings may be different for each device according to the execution environment. For example, the basic configuration of the AI ​​is trained on another information processing device. After that, each information processing device may be additionally trained or configured to be optimized for each execution environment.

[0062] [Example of log data] The log data is generated in accordance with the progress of the game when the game is played on the user terminal 20. The input data D2 may be generated based on an operation by the user 4 (for example, an operation to save the game, etc.), or may be automatically generated in the background by the user terminal 20 in accordance with the progress of the game.

[0063] The log data is data that indicates the progress of playing a game.

[0064] In the following, an example will be described in which the eleventh data D11 in the pre-processing and the twenty-first data D21 in the execution processing are the first data.

[0065] The first data is data indicating a profile of a character appearing in the game. Or, the first data is data indicating a profile of a user 4 who performs operations on the game. For example, the first data is input by the user 4 when starting the game. However, the first data may be acquired from the user terminal 20 or another database, etc.

[0066] Specifically, the first data includes setting items such as name, background, occupation, and friendships.

[0067] The name is the real name, abbreviation, pseudonym, etc. of the user 4. The name is also identification information such as a nickname set for the character.

[0068] The background is the user's 4 history or the in-game history set for the character.

[0069] The occupation is the occupation or work history of the user 4. The occupation is an in-game occupation set for the character.

[0070] A friendship is a relationship with another person who is or has been involved with the user 4 or character.

[0071] The second data is information about events that occurred in the game until an item was acquired, or data indicating a process until an item was acquired in the game. For example, the second data is generated in the background while the user 4 is playing the game.

[0072] Specifically, the second data is image data (the image data is a still image or a video) recorded from a screen showing play by user 4 (the screen may be from the viewpoint of user 4, or from the viewpoint of another user, or from another viewpoint such as a bird's-eye view), or related image data, etc. In addition, the second data is process information such as the content of an event played by user 4, play results, achievements, total play time, or other participants.

[0073] For example, if the item is a gem in a game, in a case where a player plays an event to acquire the item, the second data may be information such as "it took 25 hours to acquire the item," "it was acquired together with six friends," and "the size of the gem is 10 carats."

[0074] The third data is data showing an output screen displayed until the user 4 obtains an item in the game. For example, the third data is image data recorded from an output screen displayed when the user 4 plays the game.

[0075] The third data may be image data showing the gameplay process outputted until the item is acquired in the game. In particular, it is preferable that the third data is generated mainly from the gameplay process that is the highlight until the item is acquired in the game.

[0076] [Example of execution process in RPG] In the following, the type of game is assumed to be an RPG (role-playing game). Also, when the game progresses and the player saves, the game will start from the point where the player saved the game.

[0077] In the following, an example will be described in which the object is an "item." However, the object is not limited to an item. For example, an object is equipment such as a weapon, armor, or accessory, or a tool that the player's character can use (such as "medicinal herbs" or "antiven herbs" that have the effect of restoring the character's vitality or status. Or, it may be something that can be used in battle and has the effect of attacking. In addition, it may be something that has the effect of granting or canceling a buff or debuff effect to an enemy or ally).

[0078] Other examples of objects include skins that change the appearance of the player's character (unlike equipment, which only changes the appearance), currency in the game (for example, a unit such as "gold", although currency units and names vary), items necessary to progress through the game, materials used as synthesis materials, or character cards (which make up a deck, or on which a character's profile is written, etc.) Or, an object may be a character that becomes a companion of the player.

[0079] As described above, an object may be called something other than an "item."

[0080] In the following example, the output is made from the time when the game was last saved and resumed to the time when the jewel item was acquired in the event. However, the time period to which the output corresponds may be set by the user 4 or may be the start of the game, etc.

[0081] 7 is a diagram showing an example of input in Example 1. For example, when a game is played on the user terminal 20 with respect to the server 11 on which the trained model A2 generated by the pre-processing shown in FIG.

[0082] The trained model A2 is in a state where it has learned examples of real gemstones through pre-processing, i.e., learning, using big data D4. Therefore, the trained model A2 understands how to introduce gemstones.

[0083] The input data D2 is transmitted to the server 11, for example, when a player plays a game and acquires a jewel item. However, the input data D2 does not have to be transmitted all at once, and the constituent data may be divided and transmitted to the server 11 while the game is in progress.

[0084] In the first example, the input data D2 includes process text data D201 and video data D202, and is transmitted to the server 11.

[0085] The process text data D201 is text information indicating the time taken to acquire an item, such as "25 hours to acquire the item," "acquired together with six friends," and "the size of the gemstone is 10 carats," characteristics of the item, or the number of other participants. For example, the process text data D201 is extracted and generated from parameters input by the user 4 or parameters at the time of acquiring the item.

[0086] Fig. 8 is a diagram showing an example of a moving image For example, the moving image data D202 is an output screen recorded mainly showing scenes of strategy in a RPG, as shown in Fig. 8.

[0087] Specifically, the video data D202 includes scenes in which the player fights an opponent for an event before acquiring an item during the event, such as a so-called "boss battle."

[0088] For example, the video data D202 includes an output screen in which a character is made to perform a special action. In the video data D202, the action name of the executed special action (hereinafter referred to as "special action name D203") is output. The special action name D203 indicates the name of a so-called "special move" or "magic", etc.

[0089] Furthermore, in the video data D202, character highlighting D204 in which a character who performed the action of the special action name D203 is highlighted is output. For example, the character highlighting D204 is output in a so-called "cut-in" format.

[0090] In this way, the video data D202 includes highlight scenes for which special action names D203 or character highlighting D204 or the like are output.

[0091] 9 is a diagram showing an example of a profile. For example, the profile etc. are input to the trained model A2 by setting data D205 etc.

[0092] Specifically, the setting data D205 includes character data D206. The character data D206 is data indicating the name, status, parameters, equipment, and participants of the character. The character data D206 indicates so-called "level", "character strength", and the like.

[0093] The setting data D205 includes the characteristic data D207. For example, the characteristic data D207 is data that indicates a special setting for an event or a characteristic characteristic compared to other players. Specifically, the characteristic data D207 is a device by the user 4, such as equipping a character with new equipment for an event. In addition, the characteristic data D207 extracts a so-called "recommended level" or a low level compared to the "average level" of other players. The "level" is a numerical value indicating the overall strength of a character. For example, if a character is strengthened with experience points or items, the level becomes a high value. The "recommended level" is a level that is set for an event and is set in advance by the administrator 5, etc., and is estimated to be a level at which the event can be conquered based on the difficulty level of the event. The "average level" is an average value, median value, etc. calculated by averaging the data of other players who have conquered the event.

[0094] The profile etc. may include data that is not directly visible to the user 4. For example, data used in the background, such as play time or a character's movement range, may be included as the profile.

[0095] [Example output] Hereinafter, information output in association with an object will be referred to as "special information." For example, the special information is output by the output data shown below. The special information is information that the user 4 can refer to when the character obtains an item in the virtual space (after obtaining the item, the information may be available for reference at any time by user operation, etc.).

[0096] Special information is information related to an object. For example, special information indicates an explanation related to an item in text format. However, the content, amount of information, output format, etc. of the special information may be set (including partial setting) by the user 4 or the administrator 5.

[0097] 10 is a diagram illustrating a first example of output data. For example, the trained model A2 generates first output data D31 through execution processing.

[0098] The first output data D31 is generated, for example, in a text format. The first output data D31 is not limited to the text format, and may be generated in a format including an image. The output is not limited to the text and image, and may be in a format in which sound is output, or in a format that combines these.

[0099] The first output data D31 includes explanatory texts such as a first explanatory text D311, a second explanatory text D312, a third explanatory text D313, and a fourth explanatory text D314.

[0100] The first explanatory text D311 is a text that explains the name of the gemstone, etc. The items explained by the first explanatory text D311 are items included in the big data D4, that is, text that explains the actual gemstone (for example, texts that are written on an explanatory site that actually exists in the real world, history, etc., and explains the historical events in which the gemstone appeared, famous people involved in the gemstone, the origin of the gemstone's name, the whereabouts of the gemstone, or the characteristics of the gemstone, etc., based on the existence of the gemstone). The items differ depending on the type of item. Depending on the type, the item is explained by referring to an explanation of the same or similar type that actually exists. For example, if the item is a weapon, the first explanatory text D311 is generated by referring to an explanatory site or explanatory materials that explain swords, swords, or other weapons that actually exist. In addition, it is desirable to generate the name and specifications of the item together with the first explanatory text D311.

[0101] In this way, if the items that appear in the game can be described in the same way as real items that actually exist, the items in the game can be made more real.

[0102] The second explanatory text D312 is a text that reflects the play results and the play process up to the acquisition of the item based on the log data. Specifically, the second explanatory text D312 is data that is generated by incorporating characteristics such as the number of players who cooperated when acquiring the item indicated by the log data and the time it took to acquire the item. The second explanatory text D312 may also incorporate other characteristics. For example, the second explanatory text D312 may be a characteristic of the difficulty in acquiring the item (for example, in the case of challenging a strong enemy many times to acquire the item, the time it took or the repeated attempts, such as "I finally got it after x attempts!").

[0103] Alternatively, the second explanatory text D312 may be generated by incorporating excellent features, such as acquiring an item in a short time compared to other users. In addition, the second explanatory text D312 is not limited to quantitative expressions, and may be qualitative expressions, such as changing "6 friends" to "many friends." In this way, when features of difficulties in acquiring an item or excellent features are included, the user 4 can recall the difficulties he or she faced before acquiring the item.

[0104] The third explanation text D313 is text that explains the features of the item based on the log data. For example, if the size of the gem changes depending on the profile or play, the third explanation text D313 changes according to the size. Specifically, the name of the gem may change depending on the play. And, if the name of the gem changes, the text explaining the origin of the gem's name also changes according to the name.

[0105] The third explanatory text D313 is preferably generated based on the big data D4, etc., so as to describe the same characteristics as a text that describes an actual gemstone. For example, in the case where big data D4, such as an internet gemstone commentary site, often includes explanatory items such as the name of the gemstone, its size, the origin of the name of the gemstone, and the previous owners of the gemstone, the third explanatory text D313 is preferably generated so as to include items such as the name of the gemstone, its size, the origin of the name of the gemstone, and the previous owners of the gemstone.

[0106] Also, it is preferable that each setting of the gemstone is set with reference to an actual gemstone. For example, it is preferable that the size is set with a weight or dimension within the range of actual gemstones. And it is preferable that the third explanatory text D313 includes expressions such as "big," "heavy," "valuable," or "hard" that make comparisons with actual gemstones.

[0107] In this way, by referring to the descriptions of actual jewels, the realism of the game settings can be enhanced. Also, if the descriptions and settings change each time you play, you can enjoy various patterns because the settings are different each time.

[0108] Moreover, the third explanatory text D313 is preferably generated taking into consideration the environment of the location in the game where the item is obtained. For example, by referring to the log data, the regional characteristics of the game where the item is obtained can be identified. In a cold region such as a snowy country, the item is preferably an item related to "coldness." Therefore, the third explanatory text D313 is preferably generated to reflect the regional characteristics.

[0109] The fourth explanatory text D314 is a commonly output text that explains the characteristics of the item. That is, the fourth explanatory text D314 is a fixed text that is output even if the gem changes depending on the profile or each play. For example, the fourth explanatory text D314 is set in advance by the administrator 5 or the like.

[0110] Specifically, some items may have essential functions for the story. However, some items do not need to have essential functions.

[0111] For example, an item such as a "key" corresponds to a certain type of door, and when the item is obtained, it allows the player to proceed to a new area, or is an item that plays a role in progressing through an event. In this way, items will always have functions that affect later events, no matter how many times the player has played the game. In other words, if the player acquires an item that does not have a required function, it may become an obstacle in the story, such as being unable to overcome subsequent events.

[0112] It is desirable that the text explaining the above essential functions is fixed, that is, always included in the explanation. However, the fourth explanation text D314 may be not only an essential function, but also an important hint for the story, or a sentence showing the administrator 5's particulars, etc.

[0113] The first output data D31 is output to the screen in the description field of the item, or at the timing when the item is acquired, etc.

[0114] 11 is a diagram illustrating a second example of output data. For example, the trained model A2 generates second output data D32 through execution processing.

[0115] Hereinafter, an example will be described in which the second output data D32 is output to the operation screen for performing saving shown in Fig. 11. However, the screen for outputting the second output data D32 is not limited to the operation screen for performing saving, and may be a start screen, a save data loading screen, or the like.

[0116] The second output data D32 includes a summary image D321 and summary text D322 generated based on the video data D202. For example, the summary image D321 is an image showing an excerpt from the video data D202 of a story leading up to the acquisition of an item. The summary image D321 may be a still image or a video.

[0117] The summary image D321 outputs a highlight scene, or a characteristic scene that gives an overview of the events that occurred before the item was acquired, etc. Note that the summary image D321 is preferably a few still images or a video of about a few seconds.

[0118] The summary text D322 indicates a summary, keywords, features, or headings up to the acquisition of the item. That is, the summary text D322 indicates what events occurred before the item was acquired, or features such as the acquisition of a special item. Therefore, it is preferable that the summary text D322 is generated mainly based on keywords.

[0119] The summary text D322 is preferably a few words or a short sentence, and therefore the summary text D322 is preferably generated to have a length similar to that of a heading or summary.

[0120] FIG. 12 is a diagram showing a third example of output data. For example, the trained model A2 generates third output data D33 by execution processing. The third output data D33 is an example of outputting a so-called "play journal" on the screen. The "play journal", i.e., the screen shown in FIG. 12 output based on the third output data D33, is output when, for example, the user 4 performs an operation to open the "play journal", when an event ends, when a certain amount of time has passed during the game, or when a save point is reached. The "play journal" may also be output when the game is otherwise completed.

[0121] A "play journal" is a document that shows the progress of a game. For example, as shown in FIG. 12, a "play journal" is composed of explanatory text. However, a "play journal" may also include images or graphs. Furthermore, a "play journal" may not be structured like a journal, but may instead be composed of bullet points, images only, or a newspaper-like structure.

[0122] Specifically, the "play journal" indicates a summary, hints for progressing through the game, or recent symbolic events, etc., by using a heading D331 or advice D332, etc. The "play journal" may have a different configuration depending on the settings of the user 4, etc. That is, some users 4 do not want to see information in the "play journal". For example, there are cases where the user 4 does not want to see hints for progressing through the game, since these would reveal future developments, etc., and would be so-called "spoilers". In consideration of such cases, the user 4 may be able to set the "play journal" so that hints for progressing through the game are not displayed.

[0123] In addition to information displayed in text, the "play log" may also include statistical information showing play time, game completion rate, or comparison with other players, or analytical information.

[0124] Furthermore, the "play log" will have different contents for each user 4 and each time it is displayed. In other words, the "play log" is generated based on not only standard phrases but also log data and the like so as to reflect the circumstances of each user 4.

[0125] The third output data D33 includes the first output data D31, etc. That is, the third output data D33 indicates, based on the log data, a summary or the like according to a story up to the acquisition of an item for the user 4 or for each play.

[0126] The third output data D33 preferably includes a headline D331 or advice D332.

[0127] The heading D331 is a short sentence of about a few characters that summarizes or indicates a keyword. For example, the heading D331 is generated mainly from the word that appears most frequently in the first output data D31. Specifically, the first output data D31 is a sentence that includes words such as "diamond" and "jewel" and is centered on the item "jewel". Therefore, the heading D331 is generated with "jewel" as a keyword. In this way, the heading D331 indicates the central content of the "play diary" in a short sentence. Note that the heading D331 may be not only a sentence, but also a symbol, an emoticon, or an image. Also, the number of headings D331 is not limited to one, and there may be multiple headings.

[0128] The heading D331 allows user 4 to get a rough idea of ​​the content of the "play diary" without reading the text.

[0129] The advice D332 shows hints for progressing through the game, etc. Specifically, as shown in Fig. 12, the most recent play is analyzed using log data, and if a state of difficulty is recognized, measures to address the difficulty (for example, suggesting an effective item, etc.) are shown.

[0130] Additionally, the advice D332 may indicate items that are easy to obtain in the current situation (for example, location information indicating that the player is in a specific area in a RPG) based on log data.

[0131] When advice D332 is output in this way in accordance with the current situation, the user 4 can resolve any difficulties he or she may be having with the game, and can progress in the game.

[0132] The output data as described above may be generated by limiting the log data to the period from the most recent save to the acquisition of the item.

[0133] [Overall processing example] 13 is a diagram showing an example of the overall process. In the following example, the overall process is a sequence of a pre-process and an execution process. Specifically, the pre-process is step S01. The execution process is steps S02 to S04. However, the overall process may include other processes.

[0134] In step S01, the server 11 inputs the learning data D1 including the log data and the correct answer data to train the learning model A1. Then, when step S01 is performed, a trained model A2 is generated. In this way, when the learning model A1 is trained to become the trained model A2, the execution process is performed using the trained model A2.

[0135] In step S02, the server 11 determines whether or not an item has been acquired. Next, if an item has been acquired (YES in step S02), the server 11 proceeds to step S03. On the other hand, if the item has not yet been acquired (NO in step S02), the server 11 repeats step S02. That is, step S03 and subsequent steps are executed with the acquisition of the item as a trigger.

[0136] In step S03, the server 11 inputs input data D2. For example, step S03 is executed in the background in parallel with the progress of the game, that is, transmitted from the user terminal 20 to the server 11 without the user 4 being aware of it. Note that the input data D2 may include data generated in the server 11.

[0137] In step S04, the server 11 generates the output data D3. For example, the timing of generating the output data D3, that is, the timing of executing step S04, is set in advance.

[0138] In step S05, the server 11 performs output based on the output data D3. For example, the timing of output or the screen to be output, that is, the timing and target of executing step S05, are set in advance.

[0139] The parameters are set, for example, before the game starts, etc. However, depending on the type of parameter, the parameters may be changed, added, deleted, etc. during the game.

[0140] The log data is stored, for example, according to the progress of the game. Note that the user 4 may be able to operate an instruction to store the log data (i.e., the user 4 may select the log data to be used for the special information). In addition, the log data may be stored periodically, or when a scene changes, etc.

[0141] [Example of functional configuration] 14 is a diagram showing an example of a functional configuration. For example, the system 1 is a game story output system including a learning device 31 and a game story output device 32.

[0142] The learning device 31 includes a learning data input means 1F1 and a learning means 1F2.

[0143] The learning data input means 1F1 performs a learning data input procedure for inputting the learning data D1 including the log data and the correct answer data D20. For example, the learning data input means 1F1 is realized by the communication interface 115 or the like.

[0144] The learning means 1F2 performs a learning procedure in which the learning model A1 is trained using the training data D1 to generate a trained model A2. For example, the learning means 1F2 is realized by the processor 111 or the like.

[0145] The game story output device 32 includes a setting means 1F10, a storage means 1F11, an input data input means 1F3, a generating means 1F4, and an output means 1F5. The setting means 1F10 and the storage means 1F11 may be further included in the learning device 31. Alternatively, the setting means 1F10 and the storage means 1F11 may be shared by both the learning device 31 and the game story output device 32.

[0146] The setting means 1F10 performs a setting procedure for setting parameters of a character in a virtual space based on an operation of the user 4. For example, the setting means 1F10 is realized by the communication interface 115 or the like.

[0147] The storage means 1F11 performs a storage procedure for storing the actions of the character in the virtual space as log data. For example, the storage means 1F11 is realized by the storage 113 or the like.

[0148] The input data input means 1F3 performs an input data input procedure for inputting input data D2 including unknown log data to the trained model A2 generated by the training means 1F2. For example, the input data input means 1F3 is realized by the communication interface 115 or the like.

[0149] The generating means 1F4 performs a generating procedure for generating special information when the character obtains an object in the virtual space based on the trained model A2 trained based on the training data D1. For example, the generating means 1F4 is realized by the processor 111 or the like.

[0150] Based on the output data D3, the output means 1F5 performs an output procedure to output to the user terminal 20. For example, the output means 1F5 is realized by the communication interface 115 or the like.

[0151] The study device 31 and the game story output device 32 are, for example, the server 11. However, the study device 31 and the game story output device 32 may be different information processing devices.

[0152] With the above configuration, special information for an object can be changed and generated in the game. For example, if the user 4 is different, the parameters of the character are different for each user 4, so the generated story also changes when the user 4 changes. Even if the user 4 is the same, if the play result and the play process up to the acquisition of the item are different, the log data will be different, so the generated story will also change for each play.

[0153] In this way, a different story is generated for each person, or for each time the game is played. The generated story is then output in accordance with the item.

[0154] If the story of the item is different each time, User 4 can enjoy playing to acquire the item many times, which is attractive. Similarly, if User 4 is different, User 4 can see a different story from others, which is attractive. Also, since the story reflects the play results and the play process, it is original and can provide an attractive output to User 4.

[0155] The type of game is not limited to RPG. As long as items can be acquired in the course of the game, the type of game is not limited. For example, the type of game may include sports, action, puzzle, or fighting games.

[0156] [Variations] The input data D2 may be pre-processed. The pre-processing may include a process of limiting the input data D2. For example, the pre-processing may be such that the video data D202, which is the third data input in the input data D2, is limited to data at important points in time. In particular, the data volume of the video data D202 tends to be large if it is long. Therefore, the data volume can be reduced by limiting the video data D202.

[0157] For example, the input data D2 includes all output screens from the start to the acquisition of an item. Therefore, the preprocessing is a process of excluding data from a predetermined time before the acquisition of an item from the input data D2 in order to narrow it down to a predetermined time before (for example, 10 minutes). In this way, when the preprocessing is performed to narrow down the input data D2 to the most recent data, it is possible to output data that is mainly new information.

[0158] In addition, when extracting highlights, the preprocessing may be processing to narrow down the input data D2 to a point where the profile changed suddenly or a point where the character was in trouble (for example, 10 minutes before and after the specified point). When preprocessing is performed in this way, the amount of data to be processed is reduced, and the execution process can be sped up.

[0159] For example, a point in time when so-called "hit points (HP)" suddenly decrease is often a time when the character is in trouble. In addition, when a so-called "battle" is long, it is often a time when the character is in trouble. If such a point in time can be searched for using log data, etc., it is possible to identify the point in time when the character is in trouble.

[0160] The point in time when the profile changes suddenly is often the point in time when the user 4 makes an effort to temporarily strengthen the parameters of the character. After such a point in time, it is often the highlight when the strengthened character plays an active role. If such a point in time can be searched for in the log data, etc., the point in time when the character plays an active role can be identified.

[0161] In this way, it is desirable to perform preprocessing in which not all of the input data D2 is input to the trained model A2, but rather carefully selected data is input to the trained model A2.

[0162] [Other embodiments] In the above example, the information processing device performs both pre-processing for the learning model and execution processing using the learned model. However, the pre-processing and execution processing do not have to be performed by the same information processing device. Furthermore, the pre-processing and execution processing do not have to be consistently performed by one information processing device. In other words, each process and data storage, etc. may be performed by an information system, etc., composed of multiple information processing devices.

[0163] The learning process may be additionally performed after the execution process or before the execution process.

[0164] The above-mentioned processing may be performed by an information processing device other than the server 11 and the user terminal 20 in an auxiliary manner.

[0165] Fig. 15 is a diagram showing a configuration example using an auxiliary device. Compared to the example shown in Fig. 1, the configuration shown in Fig. 15 differs in that an auxiliary device 60 is added. Note that the auxiliary device 60 may be a configuration that is used temporarily.

[0166] The auxiliary device 60 is an information processing device installed near the user terminal 20 (in this example, it is installed near the user terminal 20A, but it may be installed near other devices), etc. The auxiliary device 60 executes a part or all of a specific process on behalf of the user terminal 20 or the server 11.

[0167] For example, the auxiliary device 60 is provided with a device specialized for graphic processing and performs graphic processing at high speed. In this way, so-called edge computing or the like may be performed by installing the auxiliary device 60 or the like. In this way, the above-mentioned processing may be executed by utilizing the hardware resources of various information processing devices. Therefore, the above-mentioned processing may be executed by an information processing device different from the above-mentioned one.

[0168] The above-described processes and data used in the processes executed in this embodiment may be executed and stored by an information processing system. For example, the information processing system may execute or store the data in a plurality of information processing devices in order to realize redundant, distributed, parallel, or a combination of these processes or storage. Therefore, the present invention may be realized in devices other than the hardware configurations shown above and in systems other than the devices shown above.

[0169] In addition, the program according to the present invention is not limited to a single program, but may be a collection of multiple programs. In addition, the program according to the present invention is not limited to being executed by a single device, but may be executed by multiple information processing devices. Furthermore, the role allocation of each information processing device is not limited to the above example. In other words, a part or all of the above-mentioned processing may be executed by an information processing device different from the above-mentioned information processing device.

[0170] Furthermore, a part or all of the means realized by the program can be realized by hardware such as an integrated circuit. Furthermore, the program may be provided by being recorded on a non-transitory recording medium readable by a computer. The recording medium refers to, for example, a hard disk, an SD card (registered trademark), an optical disk such as a DVD, or a server on the Internet. Therefore, the program may be distributed via a telecommunication line such as the Internet.

[0171] Furthermore, the information processing devices that make up the information processing system may be located overseas.

[0172] The present invention is not limited to the above-mentioned embodiments. Therefore, the present invention can be modified or modified without departing from the technical gist of the invention. Therefore, all technical matters included in the technical ideas described in the claims are the subject of the present invention. The above-mentioned embodiments are preferred examples. Those skilled in the art can realize various modifications from the disclosed contents, and such modifications are included in the technical scope described in the claims. [Explanation of symbols]

[0173] 1: System 1F1: Learning data input means 1F2: Learning methods 1F3: Input data input means 1F4: Generation means 1F5: Output method 1F10: Setting means 1F11: Storage means 4: User 5:Administrator 11: Server 20: User terminal 31: Learning device 32: Game story output device 60: Auxiliary equipment D1: Training data D11: 11th data D12: 12th data D13: 13th data D2: Input data D20: Correct data D201: Process text data D202:Video data D203: Action name D204: Character Emphasis D205: Setting data D206: Character data D207: Feature data D21: 21st data D22: Data No. 22 D23: 23rd data D3: Output data D31: First output data D311: First explanatory text D312: Second explanatory text D313: Third explanatory text D314: 4th explanatory text D32: Second output data D321: Summary image D322 :Summary text D33: Third output data D331 : Heading D332: Advice D4: Big Data A1: Learning model A2: Trained model

Claims

1. Computer, a storage means capable of storing data indicating actions of a character operated by a user in a virtual space as log data; When the character obtains a predetermined object in the virtual space, unknown log data relating to the obtaining of the predetermined object is input to a trained model trained based on training data including at least the log data, thereby functioning as an output means for outputting special information relating to the predetermined object; The output means includes: When the acquisition mode of the predetermined object is a special mode based on a characteristic element different from usual, the special information is output in a form including the characteristic element. program.

2. a storage means capable of storing data indicating actions of a character operated by a user in a virtual space as log data; an output means for, when the character obtains a predetermined object in the virtual space, outputting special information regarding the predetermined object by inputting unknown log data related to the acquisition of the predetermined object into a trained model trained based on training data including at least the log data; Equipped with The output means includes: When the acquisition mode of the predetermined object is a special mode based on a characteristic element different from usual, the special information is output in a form including the characteristic element. system.

Images