Program and system

Abstract

To reduce the burden of a setting operation related to in-game elements such as items used in a game.SOLUTION: A program causes a computer to function as: generation means for generating output data relating to in-game elements when log data regarding player operations in a game is input, to a learned model learned using learning data including story data and log data; and progression means for making the in-game elements appear in the game on the basis of the output data and enabling the game to progress using the in-game elements.SELECTED DRAWING: Figure 11

Description

[Technical Field]

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

[0002] BACKGROUND ART Conventionally, in a computer-based game (hereinafter simply referred to as a "game"), a technique is known in which items to be used in the game are given to a user in accordance with the user's play.

[0003] For example, when a player wins a casino game in a game, i.e., in a virtual space, the player acquires virtual currency. When the amount of virtual currency exceeds a predetermined amount, the player is notified. The virtual currency can then be exchanged for items that can be used in the game at an exchange office within the game. In this way, a technique for motivating players to acquire virtual currency is known (for example, Patent Document 1, etc.). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-29957 Summary of the Invention [Problem to be solved by the invention]

[0005] However, with conventional technology, when in-game elements such as items are introduced, exchanged, used, granted, bought and sold, improved, or acquired, it is necessary to define in advance settings for each of these situations during the development stage of the game. In other words, for each in-game element, it is necessary to set behavior corresponding to each situation, and the more situations there are, the greater the workload for setting in-game elements in advance.

[0006] The present invention aims to reduce the burden of setting up in-game elements such as items used in the game. [Means for solving the problem]

[0007] In order to solve the above problems, the present invention provides a program that causes a computer to function as a generation means that generates output data related to in-game elements when log data related to player operations in a game is input to a trained model that has been trained using learning data including story data and log data, and as a progression means that makes the in-game elements appear in the game based on the output data and enables the game to progress using the in-game elements. [Effects of the Invention]

[0008] According to the present invention, the load of setting up in-game elements such as items used in the game can be reduced. [Brief explanation 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. [Figure 2] FIG. 10 is a diagram illustrating an example of pre-processing. [Figure 3] FIG. 10 is a diagram illustrating an example of execution processing. [Figure 4] FIG. 1 is a diagram illustrating an example of the overall process of AI learning and execution. [Figure 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] A figure showing an example of the learning process of the first learning model in RPG. [Figure 8] A diagram showing an example of the learning process of the second learning model in RPG. [Figure 9] FIG. 10 is a diagram illustrating an example of execution processing in RPG. [Figure 10] FIG. 10 is a diagram illustrating an example of overall processing. [Figure 11] FIG. 2 is a diagram illustrating an example of a functional configuration. [Figure 12] FIG. 10 is a diagram illustrating a configuration example using an auxiliary device. DETAILED DESCRIPTION OF THE INVENTION

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

[0011] [System configuration example] 1 is a diagram showing an example of a system configuration according to this 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 terminals 20") and a server 11.

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

[0013] The administrator 5 is responsible for managing the information processing service provided by the system 1. On the other hand, the user 4 is a user of 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 the player of the game, and the administrator 5 will manage the game (for example, generating, modifying, and distributing 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 each other so as 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 such as 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, which then provides a game service. Note that communication with the server 11 is not required to play the game. In other words, the user terminal 20 may download the program or install it from media to create an environment for playing the game. The game may also be played on an information processing device other than the user terminal 20.

[0017] [Examples of AI (Artificial Intelligence) learning and execution] Hereinafter, AI learns through "pre-processing" (sometimes referred to as "learning processing"). AI in the learning stage, i.e., "pre-processing", will be referred to as "learning model A1". Hereinafter, there are two learning models A1, which will be referred to as "first learning model A11" and "second learning model A12". Furthermore, "first learning model A11" and "second learning model A12" will sometimes be collectively referred to as "learning model A1".

[0018] 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 performed using the trained model A2 will be referred to as the "execution process." Furthermore, once the "first learning model A11" has trained, it becomes the "first trained model A21." Similarly, once the "second learning model A12" has trained, it becomes the "second trained model A22." Hereinafter, the "first trained model A21" and the "second trained model A22" may be collectively referred to as the "trained model A2."

[0019] The "pre-processing" is performed before the "execution processing." However, the "pre-processing," i.e., the trained model A2 may continue to train after the "execution processing."

[0020] [Pre-processing example] 2 is a diagram showing an example of pre-processing. For example, the pre-processing is performed by the server 11.

[0021] The first learning model A11 receives the first learning data DL1 as input and performs learning. Similarly, the second learning model A12 receives the second learning data DL2 as input and performs learning. In other words, the learning model A1 performs so-called "supervised" learning.

[0022] The first learning data DL1 includes log data DL12, story data DL11, and first correct answer data DL13.

[0023] The first learning data DL1 is data in which the first correct answer data DL13, which is the "correct answer," is associated with the log data DL12 and the story data DL11. In this way, the correct answers of the log data DL12 and the story data DL11 are known data.

[0024] The first learning data DL1, the story data DL11, the log data DL12, and the first correct answer data DL13 will be described in detail later.

[0025] Through the above-described learning process, when the first learning data DL1 is input, the first learning model A11 learns the correlation between the combination of the log data DL12 and the story data DL11 and the "correct answer" indicated by the first correct answer data DL13.

[0026] The second learning data DL2, like the first learning data DL1, includes log data DL12. Therefore, the log data DL12 is used in both the first learning data DL1 and the second learning data DL2 by duplicating or reusing the data.

[0027] In addition, the second training data DL2 includes a keyword DL21 and second supervised answer data DL22.

[0028] The second learning data DL2, the keywords DL21, and the second supervised answer data DL22 will be described in detail later.

[0029] The second learning data DL2 is data in which the second correct answer data DL22, which is the "correct answer," is associated with the log data DL12 and the keyword DL21. In this way, the log data DL12 and the keyword DL21 are data whose correct answers are known.

[0030] The second supervised answer data DL22 is data showing the same content as the story data DL11. Therefore, the second supervised answer data DL22 is used in both the first learning data DL1 and the second learning data DL2 by being duplicated or reused. However, the second supervised answer data DL22 is supervised answer data showing the "correct answer" in the second learning data DL2, whereas the story data DL11 is input data in the first learning data DL1. Thus, even though the data have the same content, the second supervised answer data DL22 and the story data DL11 play different roles in the learning process.

[0031] Furthermore, it is desirable for the learning model A1 to learn using big data D4. For example, the big data D4 is data on the Internet. However, the big data D4 may also be data entered by an administrator 5 or the like. In this way, learning using big data D4 can create an AI that generates natural sentences and data that resemble those written by a human.

[0032] [Execution processing example] 3 is a diagram showing an example of the execution process. For example, the execution process is performed by a plurality of information processing devices, such as the user terminal 20 and the server 11, or the user terminal 20 and the server 11, working together.

[0033] The trained model A2 is in a state where the trained model A1 has been trained through pre-processing. That is, when the pre-processing shown in Figure 2 is executed, the trained model A2 is generated.

[0034] When the first unknown data DU1 is input, the first trained model A21 generates output data D3.

[0035] The first unknown data DU1 is data that is a combination of log data (hereinafter referred to as "unknown log data DU11") and story data (hereinafter referred to as "unknown story data DU12"), the "correct answer" of which is unknown.

[0036] The first unknown data DU1, the unknown log data DU11, the unknown story data DU12, and the output data D3 will be described in detail later.

[0037] When the second unknown data DU2 is input, the second trained model A22 generates unknown story data DU12.

[0038] The second unknown data DU2 is data that is a combination of unknown log data DU11 and a keyword (hereinafter referred to as "unknown keyword DU21"), where the "correct answer" is unknown. Note that, like the log data DL12 in the learning process, the unknown log data DU11 is duplicated or reused as the first unknown data DU1 and the second unknown data DU2, and the same data is used.

[0039] The unknown story data DU12 generated by the second trained model A22 is input to the first trained model A21 as the first unknown data DU1.

[0040] In the above configuration, the story generator 12 performs pre-processing to generate unknown story data DU 12. Note that in the above configuration, the AI ​​is divided into two AIs, one for pre-processing and one for post-processing, but the AIs may be divided into one AI or three or more AIs.

[0041] When the output data D3 is generated, the server 11 distributes the game to the user terminal 20 based on the output data D3. Note that the game may be transmitted as output data D3 to the user terminal 20. As long as the content of the output data D3 is reflected in the game, the configuration for distributing the game and the data configuration for holding the output data D3 are not important.

[0042] 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.

[0043] Note that the pre-processing and execution processing do not have to be performed in the sequential order illustrated in the figure. Therefore, it is not necessary to consecutively perform the period during which preparation is performed by the pre-processing and the subsequent period during which execution processing is performed. Therefore, once the trained model A2 has been created, the execution processing may be performed after a period of time has elapsed since the pre-processing. Furthermore, once the trained model A2 has been generated, the execution processing may be performed by reusing the trained model A2.

[0044] The first learning data DL1 and the first unknown data DU1 are different between the learning process and the execution process. Furthermore, the second learning data DL2 and the second unknown data DU2 are different between the learning process and the execution process. Furthermore, the AI ​​is a learning model A1 in the learning stage, but after a certain amount of learning has progressed, it becomes a trained model A2. In this way, the trained model A2 trained using big data D4 and the like as training data is what is known as a "generative AI."

[0045] The "correct answer" of each piece of learning data is known, whereas the "correct answer" of each piece of unknown data is unknown. Specifically, since the first learning data DL1 includes the first correct answer data DL13, the first correct answer data DL13 is associated with the combination of the log data DL12 and the story data DL11, whereas the first unknown data DU1 does not include the first correct answer data DL13. Therefore, in the learning process, the relationship between the combination of the log data DL12 and the story data DL11 and the first correct answer data DL13 is known. Therefore, when the first learning data DL1 is input, the first learning model A11 can learn the correlation between the combination of the log data DL12 and the story data DL11 and the "correct answer."

[0046] Similarly, since the second learning data DL2 includes the second correct answer data DL22, the second correct answer data DL22 is associated with the combination of the log data DL12 and the keyword DL21, whereas the second unknown data DU2 does not include the second correct answer data DL22. Therefore, in the learning process, the relationship between the combination of the log data DL12 and the keyword DL21 and the second correct answer data DL22 is known. Therefore, when the second learning data DL2 is input, the second learning model A12 can learn the correlation between the combination of the log data DL12 and the keyword DL21 and the "correct answer."

[0047] On the other hand, the first unknown data DU1 does not include the first correct answer data DL13, and the "correct answer" for the first unknown data DU1 is unknown. The first trained model A21 generates output data D3 for the first unknown data DU1 based on the correlation between the combination of the log data DL12 and the story data DL11 trained in pre-processing and the first correct answer data DL13.

[0048] Similarly, the second unknown data DU2 does not include the second correct answer data DL22, and the "correct answer" for the second unknown data DU2 is unknown. The second trained model A22 generates unknown story data DU12 for the second unknown data DU2 based on the correlation between the combination of the log data DL12 and the keyword DL21 learned in pre-processing and the second correct answer data DL22.

[0049] The execution process may be a process in which a part of the execution uses a table, etc. In this way, in a configuration that uses a table, that is, a so-called rule-based configuration, the pre-processing is a process that prepares for inputting a table (also called a look-up table (LUT)), a mathematical formula, etc.

[0050] [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, it is assumed that the information processing device has 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.

[0051] The server 11 mainly includes a processor 111, a memory 112, a storage 113, an input / output interface 114, and a communication interface 115. Furthermore, each component of the server 11 is connected to a communication bus .

[0052] The processor 111 executes a series of instructions contained in a server program 11P stored in the memory 112 or the storage 113, thereby realizing processing and control.

[0053] 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.

[0054] The memory 112 is a main storage device that stores the server program 11P, 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.

[0055] The storage 113 is an auxiliary storage device that stores the server program 11P and data, etc. The storage 113 is, for example, a read-only memory (ROM), 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 situation where multiple user terminals 20 are used, such as an amusement facility, it becomes possible to collectively update the server program 11P or data.

[0056] 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.

[0057] 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)), a wireless terminal, or other terminals.

[0058] The communication interface 115 communicates with other devices (such as the user terminal 20) connected to the communication network 2. For example, the communication interface 115 is a wired communication interface such as a LAN (Local Area Network), or a wireless communication interface such as Wi-Fi (registered trademark) (Wireless Fidelity), Bluetooth (registered trademark), or NFC (Near Field Communication).

[0059] 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.

[0060] [Examples of training model and trained model configurations] 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 with the following network configuration.

[0061] 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, part or all of the learning model A1 and the trained model A2 may be implemented on the user terminal 20, etc.

[0062] The network 300 has, for example, an input layer L1, an intermediate layer L2 (also called a "hidden layer"), and an output layer L3.

[0063] The input layer L1 is a layer that inputs data.

[0064] The intermediate layer L2 converts the data input at the input layer L1 based on weights, biases, etc. The results of processing at the intermediate layer L2 are transmitted to the output layer L3.

[0065] The output layer L3 is a layer that outputs output contents and the like.

[0066] Then, through learning, the weight coefficients and the parameters to be 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 methods.

[0067] 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 determined by simple calculations of three or less dimensions) using "unsupervised" machine learning. The relationship between input and output is preferably processed using simple calculations such as linear expressions. This type of calculation can reduce computational costs.

[0068] In addition, the AI ​​may undergo processes such as dropout to reduce overfitting (also called "overfitting" or "over-fitting"). Other preprocessing such as dimensionality reduction and normalization may also be performed.

[0069] 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.

[0070] The AI ​​may also have hyperparameters. That is, the AI ​​may be configured such that some of its settings are configured by a user. Furthermore, the AI ​​may specify the features to be learned, or the user may set some or all of the features to be learned.

[0071] Furthermore, the learning model A1 and the trained model A2 may use other machine learning methods. For example, the learning model A1 and the trained model A2 may be unsupervised models that undergo preprocessing such as normalization. Furthermore, learning may be reinforcement learning (a learning method in which an AI makes a choice and is given an evaluation (reward) for the choice, resulting in a larger evaluation), etc.

[0072] In the learning, data expansion may be performed. That is, in order to increase the amount of learning data used in learning the learning model A1, preprocessing may be performed to expand one piece of experimental data or the like into multiple pieces of learning data. Increasing the amount of learning data in this way allows for further learning of the learning model A1.

[0073] Furthermore, the learning model A1 and the trained model A2 may be configured to perform transfer learning or fine tuning. 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 ​​may be trained on another information processing device. After that, each information processing device may be trained or configured additionally to optimize it for its respective execution environment.

[0074] [Example of learning process in RPG (Role-Playing Game)] Below, we will explain using an example of an RPG. In an RPG, as you progress through the game, events occur and various elements appear. Hereinafter, elements that appear within the game will be referred to as "in-game elements."

[0075] Specifically, in-game elements are items, characters, skills, magic, etc. that appear in the story of the game. Specifically, in-game elements include various elements that make up the game, such as items, characters, skills, magic, etc. that appear in the story of the game. In-game elements may be objects that can be manipulated during play, such as items or skills, or they may be objects that cannot be manipulated by user 4.

[0076] For example, items are one of the in-game elements, including event items necessary for events that progress the game, attack items that can attack enemies, recovery items that restore a character's stamina or status abnormalities, and auxiliary items that improve various parameters such as a character's attack power, defense power, and speed (producing a so-called buff effect), or that lower various parameters of an enemy (producing a so-called debuff effect, etc.).

[0077] Note that items are not limited to the above, and may include equipment such as weapons, armor, and accessories, as well as cards that make up a deck, various material items used for item synthesis, or for synthesizing monsters, etc. Items may also include items that are not necessarily required to be acquired in order to progress through the game and cannot be used in the game (for example, medals or badges that commemorate the achievement of a predetermined condition in the game, such as defeating a predetermined enemy (meaning defeating a predetermined number of enemies, etc.)).

[0078] Characters are people, animals, robots, etc. in the game, and are elements that trigger events as the game progresses, join the protagonist's party, or contribute to the story. Characters include playable characters that can be controlled by the player, and non-playable characters (so-called NPCs) that cannot be controlled by the player. Characters are not necessarily limited to real-life characters, and may be objects set in accordance with the game's worldview, such as demi-humans, beastmen, and androids.

[0079] Skills (techniques) or magic (the names vary depending on the game) are elements that are activated based on the operation of the user 4 and include commands that allow the user to gain an advantage in battles and other activities in the game. Note that skills and magic are not necessarily limited to those that are advantageous, and include those that strengthen some parameters but weaken others, such as fire resistance +50 but ice resistance -50. They also include so-called "passive skills" that are activated without any operation.

[0080] The following description will be given using an example in which the in-game element is an "item." In the following example, the output data D3 indicates what the item is (for example, specifications such as what effect will occur when used), and it is assumed that the output data D3 can cause the item to appear in the game played on the user terminal 20.

[0081] [Examples of first learning data, story data, log data, and first correct answer data] 7 is a diagram showing an example of the learning process of the first learning model in RPG. For example, to train the first learning model A11, the following data is used for learning.

[0082] For example, when a game is played, the log data DL12 is generated in accordance with the progress of the game. Note that the log data DL12 may be settings and plans for progressing through the game at a point before the game is started. On the other hand, if the game has progressed partway through, the log data DL12 may be a record of the progress from the start of the game to the point at which the game has progressed.

[0083] The log data DL12 has a data configuration including, for example, image data DL121 and profile data DL122. However, each piece of data does not have to be in a separate file, and one piece of data may have a data configuration including multiple contents. The log data DL12 also indicates various settings in the game.

[0084] The image data DL121 is data that indicates, for example, an output screen (including a screen not disclosed to the user 4), the appearance of a character, or the appearance of various items. Note that the image data DL121 may be subjected to image analysis to extract parameters.

[0085] The profile data DL122 indicates a character appearing in the game or a profile of the user 4. Which character or user 4 profile the profile data DL122 indicates varies depending on the game settings, input items, and the like.

[0086] For example, the profile data DL122 is input by the user 4 when the game starts. However, the profile data DL122 may not be set by the user 4 but may be set in advance, or may be acquired from the user terminal 20 or another database, etc.

[0087] Specifically, the profile data DL122 includes setting items such as name, birthplace, sex, age, occupation, friendships, etc. The types of profile items are set in advance.

[0088] The name may be, for example, the real name, abbreviation, or pseudonym of the user 4. The name may also be identification information such as a nickname set for the character. Similarly, the origin, gender, age, occupation, and friendships may be set by the user 4 or may be fictitious settings in the game.

[0089] The story data DL11 is, for example, text data that describes the story in sentences, but the story data DL11 may also include images, etc.

[0090] The first correct answer data DL13 is data indicating an item to be made to appear in the game. Specifically, the first correct answer data DL13 is specifications such as the item's appearance, name, effect when used, etc. The first correct answer data DL13, i.e., the specifications for making an item appear, differ depending on the game. Therefore, the first correct answer data DL13 is data including items required for making an item appear in the game.

[0091] By performing the above-described learning, the first learning model A11 learns the correlation of what kind of item to generate from the story, profile, etc. As described above, when generating an item, if a profile, image, etc. are input as auxiliary data in addition to the story, the information in the auxiliary data is taken into consideration when generating the item.

[0092] [Examples of second training data, keywords, log data, and second correct answer data] 8 is a diagram showing an example of the learning process of the second learning model in RPG. For example, to train the second learning model A12, the following data is used for learning.

[0093] The log data DL12 is, for example, similar to the first learning model A11, i.e., the learning process shown in Figure 7. However, the log data DL12 may have different data content or a different amount of data from the learning process of the first learning model A11.

[0094] The keywords DL21 are words or sentences that are used as references for generating the story data DL11. For example, the keywords DL21 are the meanings of in-game elements, the characteristics of in-game elements, events that occur to characters, elements that are desired to appear in the story, or key points of the story.

[0095] The story data DL11 is, for example, text data that shows the story in sentences. That is, the story data DL11 is similar to the learning process shown in FIG.

[0096] For example, when a keyword DL21 is input, the second learning model A12 learns to generate a story that includes the keyword DL21. Note that the keyword DL21 does not have to be directly included in the story data DL11, and the keyword DL21 may be indirectly expressed in the story.

[0097] The story is also generated taking into account the log data DL12. For example, if the profile parameters such as "gender" or "origin" in the profile data DL122 are different, the story data DL11 is generated so that the effect, specifications, type, name, appearance, etc. of the item change depending on the parameter.

[0098] By performing the above-described learning, the second learning model A12 learns the correlation of what kind of story to generate from the keywords DL21 and the profile, etc. As described above, when generating a story, if peripheral information, images, etc. are input as auxiliary data in addition to the profile data DL122, the information in the auxiliary data is taken into consideration when generating the story.

[0099] Note that story generation may be realized by a configuration other than AI. For example, when fixing the type of item to appear based on "gender" or "age," the fixing conditions (e.g., item type) may be input to the first trained model A21 from the profile data DL122 (e.g., input as auxiliary data) without going through AI. In this way, if fixed conditions can be input, the items to appear can be made to appear under specific conditions corresponding to these conditions in a certain profile.

[0100] Specifically, if the "occupation" is a specific job type, the item will always be a "weapon" item, while if it is a different job type, the item will always be an "armor" item, and so on.

[0101] [Example of execution processing in RPG] 9 is a diagram showing an example of execution processing in RPG. After a trained model A2 is generated by the learning processing shown in FIGS. 7 and 8, the execution processing is performed as follows.

[0102] The correct answers of the second unknown data DU2, i.e., the unknown log data DU11 and the unknown keyword DU21, are unknown at the time the data is input. Then, in the execution process, the second trained model A22 generates unknown story data DU12 based on the correlation between the learned combination of the keyword DL21 and the log data DL12 and the second correct answer data DL22, taking into account the combination of the unknown log data DU11 and the unknown keyword DU21. Then, after being generated by the second trained model A22, the unknown story data DU12 is input to the first trained model A21.

[0103] After the unknown story data DU12 is generated, the administrator 5 or the like may edit the story before it is input to the first trained model A21.

[0104] Next, unknown log data DU11 and unknown story data DU12 are input as first unknown data DU1 to the first trained model A21. The correct answer for the first unknown data DU1, i.e., the combination of unknown log data DU11 and unknown story data DU12, is unknown at the time the data is input.

[0105] Then, in the execution process, the first trained model A21 generates output data D3 based on the combination of unknown log data DU11 and unknown story data DU12, based on the correlation between the trained combination of story data DL11 and log data DL12 and the first correct answer data DL13.

[0106] Then, once the output data D3 is generated, a game featuring the items generated based on the output data D3 can be played thereafter. For example, the game can be played on the user terminal 20 or the like.

[0107] [Overall processing example] 10 is a diagram showing an example of the overall process. In the following example, the overall process is a series of pre-processing and execution processing. Specifically, steps S11 to S14 are learning processing. Steps S21 to S24 are execution processing. However, the overall process may include other processing steps.

[0108] Hereinafter, steps S11 and S12, and steps S13 and S14, i.e., the learning process of the first learning model A11 and the learning process of the second learning model A12, are assumed to be performed in parallel. However, these learning processes may be performed one after the other, rather than in parallel. For example, the second learning model A12 may be trained first to become the second trained model A22, and then the learning process of the first learning model A11 may be performed using the second trained model A22.

[0109] In step S11, the server 11 receives the first learning data DL1.

[0110] In step S12, the server 11 inputs the first training data DL1 and trains the first training model A11 to generate the first trained model A21.

[0111] In step S13, the server 11 receives the second learning data DL2.

[0112] In step S14, the server 11 inputs the second learning data DL2 and trains the second learning model A12 to generate the second trained model A22.

[0113] Performing steps S11 to S14 results in the learning process shown in Figures 7 and 8. As described above, after a certain amount of learning processing has been performed, the learning model A1 is trained to become the trained model A2. Next, the execution process is performed as follows using the trained model A2 generated by the learning processing.

[0114] In step S21, the server 11 inputs the second unknown data DU2 to the second trained model A22.

[0115] In step S22, the server 11 generates unknown story data DU12 using the second trained model A22.

[0116] In step S23, the server 11 generates output data D3 using the first trained model A21. After that, the server 11 provides the game to the user terminal 20 based on the output data D3.

[0117] In step S24, the server 11 progresses the game so that the in-game element appears based on the output data D3.

[0118] [Example of functional configuration] 11 is a diagram illustrating an example of a functional configuration. For example, the system 1 is a generative AI system including a learning device 61 and an execution device 62.

[0119] The learning device 61 includes a story data generating means 1F11, a first learning data input means 1F12, and a learning means 1F13.

[0120] When the keyword DL21 and the log data DL12 are input, the story data generation means 1F11 performs a story data generation procedure for generating the story data DL11. For example, the story data generation means 1F11 is realized by the story generator 12 or the like.

[0121] The first learning data input means 1F12 performs a first learning data input procedure for inputting first learning data DL1 including story data DL11, log data DL12, and first supervised answer data DL13. For example, the first learning data input means 1F12 is realized by the communication interface 115 or the like.

[0122] The learning means 1F13 performs a learning procedure in which the first learning model A11 is trained using the first learning data DL1 to generate the first trained model A21. For example, the learning means 1F13 is realized by the processor 111 or the like.

[0123] The execution device 62 includes unknown story data generation means 1F21, first unknown data input means 1F22, execution means 1F23, and progress means 1F24. The unknown story data generation means 1F21 is an example of first generation means. The execution means 1F23 is an example of second generation means and output data generation means.

[0124] When the unknown keyword DU21 and the unknown log data DU11 are input, the unknown story data generation means 1F21 performs an unknown story data generation procedure to generate the unknown story data DU12. For example, the unknown story data generation means 1F21 is realized by the story generator 12 or the like.

[0125] The first unknown data input means 1F22 performs a first unknown data input procedure for inputting the first unknown data DU1 including the unknown story data DU12 and the unknown log data DU11. For example, the first unknown data input means 1F22 is realized by the communication interface 115 or the like.

[0126] The execution means 1F23 performs an execution procedure to generate output data D3 using the first trained model A21. For example, the execution means 1F23 is realized by the processor 111 or the like.

[0127] Based on the output data D3, the progression means 1F24 performs a progression procedure for progressing the game on the user terminal 20. For example, the progression means 1F24 is realized by the processor 111 or the like.

[0128] The learning device 61 and the execution device 62 are, for example, the server 11. In this way, a single information processing device may serve as both the learning device 61 and the execution device 62. However, the learning device 61 and the execution device 62 may also be different information processing devices.

[0129] With the above configuration, when keywords that form the basis of the story and log data indicating the surrounding settings for generating the story are input, a story is generated that matches the characters, etc. Then, in-game elements such as items are generated that match the story and the surrounding settings, etc. Specifically, a story is generated, and items that appear in the story can actually appear in the game. In this way, even if the in-game elements that appear in the game are arranged differently depending on various factors such as the characters, the items are generated to match the story, so the load of the setting work related to the in-game elements handled in the game can be reduced.

[0130] Furthermore, since items are generated according to the character's profile, etc., they are unique items. For example, if a game is played repeatedly or if different players play, different stories and items can be generated for each play.

[0131] Furthermore, compared to a method in which items are prepared in advance, a wider variety of items can be provided. If the story or profile changes, the items also change, so even if User 4 plays the game repeatedly, User 4 will not get bored of the game. In this way, an attractive game can be provided to User 4.

[0132] [Variations] The type of game is not limited to a novel game. For example, the type of game may be a game including RPG, sports, action, puzzle, or fighting. Furthermore, the game does not have to be one of the above games.

[0133] For example, the in-game element may be a character, etc. Therefore, the characters, etc. appearing in the game may be changed according to the story, profile, etc.

[0134] In the above description, the unknown story data DU12 is generated by the second trained model A22, but the embodiment is not limited to this. For example, the unknown story data DU12 may be generated by an administrator 5 or the like inputting text to create a sentence indicating a story (including a sentence that is improved based on a story generated by an AI), and input to the first trained model A21.

[0135] Therefore, the story generator 12 is not limited to a configuration using AI, and may be a device that executes a program that assists the administrator 5 or the like in editing text.

[0136] The story generator 12 may be the server 11 or the like, or may be an information processing device separate from the server 11 or the like.

[0137] [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 a single information processing device. In other words, each process and data storage may be performed by an information system or the like composed of multiple information processing devices.

[0138] The learning process may be additionally performed after the execution process or before the execution process.

[0139] 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.

[0140] Fig. 12 is a diagram showing an example of a configuration using an auxiliary device. Compared to the example shown in Fig. 1, the configuration shown in Fig. 12 differs in that an auxiliary device 60 is added. Note that the auxiliary device 60 may be a configuration that is used temporarily.

[0141] 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 another device.) The auxiliary device 60 executes a part or all of a specific process on behalf of the user terminal 20 or the server 11.

[0142] For example, the auxiliary device 60 may be equipped with a device specialized for graphic processing and perform graphic processing at high speed. In this way, so-called edge computing may be performed by installing the auxiliary device 60 or the like. In this way, the above-described processing may be performed by utilizing the hardware resources of various information processing devices. Therefore, the above-described processing may be performed by an information processing device different from the above-described one.

[0143] 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 data on multiple information processing devices to achieve redundant, distributed, parallel, or a combination thereof. Therefore, the present invention may be realized in devices with hardware configurations other than those described above and in systems other than those described above.

[0144] Furthermore, the program according to the present invention is not limited to a single program, but may be a collection of multiple programs. Furthermore, 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 in a shared manner. Furthermore, the allocation of roles among the information processing devices is not limited to the above-mentioned example. In other words, some or all of the above-mentioned processes may be executed by information processing devices different from the above-mentioned information processing device.

[0145] Furthermore, some or all of the means implemented 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. Examples of the recording medium include 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 telecommunications line such as the Internet.

[0146] Furthermore, the information processing devices that make up the information processing system may be located overseas. That is, an information processing device that executes some of the processes executed by the information processing system may be located overseas.

[0147] The present invention is not limited to the above-described exemplary embodiments. Therefore, the present invention may be modified or added to within the scope of the technical gist. Therefore, all technical matters included in the technical concept described in the claims are subject to the present invention. The above-described exemplary embodiments are preferred examples. Those skilled in the art will be able to realize various modifications from the disclosed content, and such modifications are included in the technical scope described in the claims. [Explanation of symbols]

[0148] 1: System 1F11: Story data generation method 1F12: First learning data input means 1F13: Learning tools 1F21: Unknown story data generation method 1F22: First unknown data input means 1F23: Means of execution 1F24 :Progress means 4: User 5:Administrator 11: Server 12: Story Generator 20: User terminal 60: Auxiliary equipment 61: Learning device 62: Execution device A1: Learning model A11: First learning model A12: Second learning model A2: Pre-trained model A21: First trained model A22: Second trained model D1: First unknown data D2: Second unknown data D3: Output data D4: Big Data DL1: First training data DL11: Story Data DL12: Log data DL121: Image data DL122: Profile data DL13: First correct data DL2: Second learning data DL21: Keywords DL22: Second correct answer data DU1: First unknown data DU11: Unknown log data DU12: Unknown story data DU2: Second unknown data DU21: Unknown Keywords

Claims

1. Computer, a generation means for generating new output data relating to in-game elements when story data relating to the story of the game, character data of a player in the game, and log data relating to the actions of the player in the game are input as input data to a trained model trained using predetermined training data; making the in-game element appear in the game based on the newly generated output data, and functioning as a progression means for enabling the game to progress using the in-game element; The generating means A program that, when the content of the character data is predetermined, can generate the in-game element of a type corresponding to the content of the character data.

2. a generation means for generating new output data relating to in-game elements when story data relating to the story of the game, character data of a player in the game, and log data relating to the actions of the player in the game are input as input data to a trained model trained using predetermined training data; a progression means for making the in-game element appear in the game based on the newly generated output data and for enabling the game to progress using the in-game element; The generating means A system that, when the content of the character data is predetermined, can generate the in-game element of a type corresponding to the content of the character data.

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