Program and system

Abstract

To prevent fall in amusement of a game by preventing a user from getting bored with the game even in the case of repeated play of the game such as replaying after finishing the game.SOLUTION: A program makes a computer function as: first event data inputting means for inputting first event data that indicates a first event made to occur in a game; unknown data inputting means for inputting unknown data that indicates a play in which a correct answer is unknown, to a trained model that is trained with the first event data and training data including log data; execution means for generating second event data that indicates a second event made to occur in the game separately from the first event, on the basis of the unknown data, and using the first event data and the second event data to generate output data that indicates a story of the game; and playing means for making a user play the game on a user terminal used by the user playing the game on the basis of the output data.SELECTED DRAWING: Figure 12

Description

[Technical Field]

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

[0002] 2. Description of the Related Art Conventionally, techniques for supporting the creation of computer-based games (hereinafter simply referred to as "games") have been known.

[0003] For example, an image showing an object such as a main character, enemy, or item in a game is generated, and the images of the multiple objects are synthesized. Then, the synthesized image for the display screen is displayed in synchronization with the display cycle. Also, the speed at which the object acts is specified. In this way, a technique for adding novelty to existing image processing is known (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2003-340145 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in conventional technology, events that occur in a game are predetermined by a program or the like. That is, for example, when playing the same game repeatedly (so-called "loop play"), the user must complete the same events. Therefore, there is a problem that users who play repeatedly tend to get bored and lose interest in the game.

[0006] The present invention aims to prevent a decrease in the interest of a game by preventing a user from getting bored with the game even when the game is played repeatedly, such as in a loop play. [Means for solving the problem]

[0007] In order to solve the above problems according to the present invention, a program Computer, a first event data input means for inputting first event data indicating a first event to be generated in the game; an unknown data input means for inputting unknown data indicating a play whose correct answer is unknown to a trained model trained using training data including the first event data and log data; an execution means for generating second event data indicating a second event to be caused to occur in the game separately from the first event based on the unknown data, and for generating output data indicating a story of the game using the first event data and the second event data; Based on the output data, the user terminal used by the user playing the game is made to function as a playing means for allowing the user to play the game. [Effects of the Invention]

[0008] According to the present invention, even when a game is played repeatedly, such as in a loop play, it is possible to prevent the user from getting bored with the game, thereby preventing the game's interest from decreasing. [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]FIG. 10 is a diagram illustrating an example of a game configuration. [Figure 8] FIG. 10 is a diagram illustrating an example of a learning process in a novel game. [Figure 9] FIG. 10 is a diagram illustrating an example of execution processing in a novel game. [Figure 10] FIG. 10 is a diagram showing an example of output from a novel game. [Figure 11] FIG. 10 is a diagram illustrating an example of overall processing. [Figure 12] FIG. 2 is a diagram illustrating an example of a functional configuration. [Figure 13] 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 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. Furthermore, 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 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 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." The 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 performed 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," i.e., the trained model A2 may continue to train after 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 receives learning data D1 as input and performs learning. That is, the learning model A1 performs so-called "supervised" learning.

[0021] The learning data D1 includes known log data and the like, and is data to which a "correct answer" is associated with this known log data. Specifically, the learning data D1 includes log data indicating the game play results and play process, and for which the "correct answer" is known (hereinafter simply referred to as "log data D11"), as well as correct answer data D12. In addition, first event data DE1 is input.

[0022] The learning model A1 learns the correspondence between inputs such as log data D11 and outputs indicated by answer data D12, based on the input of learning data D1.

[0023] The log data D11, the first event data DE1, and the correct answer data D12 will be described in detail later.

[0024] 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 the big data D4 can create an AI that generates natural sentences that sound like they are written by a human.

[0025] [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.

[0026] 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.

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

[0028] The unknown data D2 is log data whose "correct answer" is unknown (hereinafter referred to as "unknown log data D21").

[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 unknown data D2, the unknown log data D21, 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 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.

[0033] The learning data D1 and unknown data D2 are different between the learning process and the execution process. Also, in the learning stage, the AI ​​is a learning model A1, but after a certain amount of learning, 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 "generative AI."

[0034] In the learning data D1, the "correct answer" is known, whereas in the unknown data D2, the "correct answer" is unknown. Specifically, since the learning data D1 includes the correct answer data D12, the correct answer data D12 is associated with the log data D11, whereas the unknown data D2 does not include the correct answer data D12. Therefore, in the learning process, the relationship between the log data D11 and the correct answer data D12 is known. Therefore, when the learning data D1 is input, the learning model A1 can learn the correlation between the log data D11 and the "correct answer."

[0035] On the other hand, the unknown data D2 does not include the correct answer data D12, and the "correct answer" for the unknown data D2 is unknown. The trained model A2 generates output data D3 for the unknown data D2 based on the correlation between the training data D1 learned in pre-processing and the correct answer data D12.

[0036] The execution process may be partly a process that uses a table, etc. In this way, in a configuration that uses a table, 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.

[0037] [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.

[0038] 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 .

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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 (Wireless Fidelity), Bluetooth (registered trademark), or NFC (Near Field Communication).

[0046] 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.

[0047] [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 a configuration shown in the following network.

[0048] 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.

[0049] The network 300 has, for example, an input layer L1, an intermediate layer L2 (also called a "hidden layer"), and an output layer L3.

[0050] The input layer L1 is a layer that inputs data.

[0051] 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.

[0052] The output layer L3 is a layer that outputs output contents and the like.

[0053] 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.

[0054] 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 calculation costs.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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.

[0059] 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.

[0060] 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.

[0061] [Example of overall game structure, example of first event, and example of second event] FIG. 7 is a diagram showing an example of the structure of a game. For example, a game may be composed of multiple events. Note that a game may also include elements other than events. However, for the sake of simplicity, the following description will be given using an example in which the game is composed only of events.

[0062] An event is an object that the player must overcome within the game, such as a trouble, opportunity, incident, occurrence, happening, or occasion that occurs within the game as the game progresses.

[0063] Hereinafter, there are two types of events: a "first event E1" and a "second event E2." Therefore, a game is made up of a combination of the first event E1 and the second event E2.

[0064] The content of the first event E1 is set by the first event data DE1. For example, the first event data DE1 is set in advance by the administrator 5 or the like. That is, the first event E1 is an event for which a so-called "plot" (also called a "scenario plot") is prepared in advance. Furthermore, the first event E1 occurs in every play (when the same user 4 plays repeatedly, or when different users 4 play, etc.), and the content is the same. Furthermore, the first event E1 is a fixed event that is always set so that the user 4 will play it.

[0065] In this way, the first event E1 is a "mandatory event" that must be overcome in every playthrough. Therefore, a mandatory event is, for example, an event that includes an important scene, the appearance of an important character, or the acquisition of a required item.

[0066] The first event E1 does not have to be an event completely set in advance. For example, the first event E1 may be changed depending on the content of the second event E2 input before and after it to ensure consistency between the connecting parts of the events.

[0067] Hereinafter, the game will be described as having a plurality of first events E1, such as a first required event E11, a second required event E12, etc. The conditions, timing, and order of occurrence of each first event E1, such as the first required event E11, the second required event E12, etc., are set by the first event data DE1.

[0068] The content of the second event E2 varies depending on the log data D11. For example, the second event E2 is an event that occurs between the first required event E11 and the second required event E12.

[0069] However, the second event E2 is not limited to an event occurring between the first required event E11 and the second required event E12, but may also be an event occurring within the first required event E11 (sometimes called a "sub-event" or "small event").

[0070] In this way, the second event E2 is a variable event whose content is set by the log data and which can be changed by the user 4.

[0071] For example, the second event E2 is a "variable event" that varies depending on the results of play up to that point (for example, the results of selection of options in the game), the characters appearing in the game, or the profile of the user 4. Furthermore, not only the content of the second event E2 but also the timing of its occurrence may vary. Alternatively, the second event E2 may be an event that occurs before the first required event E11. However, the timing of the occurrence of the second event E2, its occurrence probability, etc. may be set (including partially set) in advance by the administrator 5, etc.

[0072] The second event E2 may be an "optional" event where the user 4 can choose whether or not to participate in the event, or an event where participation is required. Two or more second events E2 may be generated.

[0073] [Example of learning process in a novel game] 8 is a diagram showing an example of learning processing in a novel game. The following describes an example of a novel game.

[0074] In a novel game, as the game progresses, a sentence is first output on the screen. Next, the user 4 performs an operation of selecting one option from a plurality of options (hereinafter referred to as a "selection operation"). In this way, a novel game is a game in which the story that progresses thereafter branches depending on the selection operation, and the ending of the story changes depending on the selection operation. For example, a novel game is a game in which the objective of the game is to identify the culprit in the story of a mystery novel.

[0075] [Example of log data] When a game is played, the log data D11 is generated in accordance with the progress of the game. That is, the log data D11 is data showing the progress of the game. The log data D11 may be generated as the game progresses, or when a save or the like is performed, data from the previous save point to the most recent save point may be generated all at once.

[0076] The log data D11 has a data configuration including, for example, play data D111, image data D112, and profile data D113. 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.

[0077] The play data D111 is data that indicates the content of a gameplay. For example, the play data D111 may include the content of a selection operation, i.e., an option selected from multiple options. The play data D111 may also include related information such as items obtained, parameter changes, events that occurred, event details, play results, performance, time spent, other participants, or characters that appeared. The play data D111 may also include parameters that are not visible to the user 4.

[0078] The image data D112 is data such as an image (a still image or a video image) recorded from an output screen showing play by the user 4 (the output screen may be from the viewpoint of the user 4 or another person). In addition, image analysis processing may be performed on the image data D112, and various data may be extracted and used as auxiliary data. Specifically, the auxiliary data is what is called a tag. For example, as an image analysis processing, an object shown in the image data D112 is recognized, and the name of the object is output as a tag. Note that the image analysis processing is not limited to object recognition, and other types of analysis may also be performed.

[0079] Furthermore, the auxiliary data may be associated with the log data D11, etc. In other words, the log data D11 does not need to be one type of data, but may be a data configuration including associated auxiliary data, etc.

[0080] For example, in the case of image data D112, particularly in the case of a moving image, the subject changes from frame to frame. Therefore, the contents of the corresponding log data D11 and the auxiliary data including the results of the image analysis process also change. Furthermore, the correspondence between the generated auxiliary data and the multiple log data D11 and the multiple image data D112 may become more complex as the number of data increases. Therefore, management of the auxiliary data can be facilitated if the auxiliary data is associated with the corresponding data. For example, the association may be achieved by including the name of the data to be analyzed in the auxiliary data.

[0081] The profile data D113 indicates a character appearing in the game or a profile of the user 4. Which character or user 4 profile the profile data D113 indicates varies depending on the game settings, input items, and the like.

[0082] For example, the profile data D113 is input by the user 4 when the game starts. However, the profile data D113 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.

[0083] Specifically, the profile data D113 includes setting items such as name, birthplace, sex, age, occupation, friendships, etc. The types of profile items are set in advance.

[0084] The name is, 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.

[0085] Similarly, the origin, gender, age, occupation, and friendships may be the settings of the user 4 or may be fictitious settings in the game.

[0086] When the log data D11 as described above is input and is input as correct answer data D12 including the second event E2 that is the correct answer, the learning model A1 can learn the correlation between the log data D11 and the correct answer, i.e., the second event E2.

[0087] By performing such learning, a trained model A2 can be generated that generates a second event E2 based on the log data D11. Furthermore, when the first event data DE1 is input, the first required event E11 and the second required event E12 can be set to occur in the game. Therefore, a trained model A2 that generates a game that combines the first required event E11, the second required event E12, and the second event E2 is generated by learning.

[0088] [Example of execution process in a novel game] 9 is a diagram showing an example of an execution process in a novel game. After a trained model A2 is generated by the learning process shown in FIG. 8, the execution process is performed as follows.

[0089] In the execution process, unknown log data D21 is input. Compared to the learning process, the execution process differs in that unknown log data D21 is input and there is no supervised answer data D12. In addition, the trained model A2 outputs output data D3 and the like.

[0090] The correct answer for the unknown log data D21 is unknown at the time the data is input. Then, in the execution process, the trained model A2 generates second event data DE2 based on the correlation between the trained log data D11 and the correct answer data D12, and taking the unknown log data D21 into consideration. Then, the trained model A2 uses the second event data DE2 to create a game that is a story in which a second event E2 occurs.

[0091] [Example of output from a novel game] 10 is a diagram showing an example of output from a novel game. For example, the game progresses in the order of a first required event E11, a second event E2, and a second required event E12. The profile is assumed to have been entered in advance at the start of the game.

[0092] For example, in the first required event E11, a selection operation is performed to select one option from options 30. Specifically, the options 30 are determined by the operation of the user 4, such as "option A" and "option B," and the lines or actions of the character in the game are determined. The result of the selection operation is then input to the trained model A2 as unknown log data D21.

[0093] The second event E2 is composed of, for example, a first sentence 31, a second sentence 32, and a third sentence 33. The first sentence 31, the second sentence 32, and the third sentence 33 are generated so as to change depending on the unknown log data D21, that is, the result of the selection operation, the profile, etc.

[0094] Specifically, the first sentence 31 is a sentence that reflects the "Origin: Country X" in the profile. Also, as the first sentence 31 indicates, a setting is generated in which the "Origin: Country X" of the character "Character X◇" that appears in the profile is "Origin: Country X." In particular, if the GUI (Graphical User Interface) for entering "Origin" in the profile is in text format rather than a selection format such as a pull-down menu, it is difficult to prepare a sentence like the first sentence 31 in advance. Therefore, the first sentence 31 that uses the "Origin: Country X" and setting in the profile is used in the second event E2.

[0095] The second sentence 32 is a sentence that reflects the result of the selection operation for the option 30 in the first required event E11. Specifically, if the selection operation to select "option B" is not performed in the first required event E11, the second sentence 32 is not output.

[0096] The third sentence 33 is an example of a sentence that indicates a "clue" that is advantageous for clearing the game. For example, the "clue" indicated by the third sentence 33 can only be obtained if the profile is set to "Origin: Country X" and "Option B" is selected in the first required event E11.

[0097] The second event E2 is an event in which a character named "Character XXX" appears, who only appears in events where game hints can be obtained, such as the third sentence 33. In other words, the character "Character XXX" does not appear in other stories, and is not a character prepared in advance by a program or the like.

[0098] A character called "Character◆◆" is generated for each story. Specifically, even in the same scene (for example, the same scene in the first and second playthroughs of a loop), a character called "Character◆◆" will have different names, lines spoken, personalities, or the timing of their appearance. Furthermore, there may be stories in which a character called "Character◆◆" does not appear. In this way, a character called "Character◆◆" is a character that the AI ​​creates and appears in each story.

[0099] Furthermore, the first sentence 31 and the second sentence 32, which are conversations between the user 4 and the "protagonist XXX", do not appear in other stories, and are not sentences prepared in advance by a program or the like.

[0100] The first sentence 31 and the second sentence 32 are generated for each story. Specifically, even in the same scene, the lines spoken by the characters "protagonist XXX" and "character ◆◆" in the first sentence 31 and the second sentence 32, i.e., "protagonist XXX" and "character ◆◆," vary. For example, even in lines with the same content, the tone or ending of the lines may vary depending on the flow of the story, gender, age, etc.

[0101] In this way, the first sentence 31, the second sentence 32, and the third sentence 33 are not generated by applying a profile or the like to a sentence prepared in advance (hereinafter referred to as a "format"), but are generated from scratch, without a format. Furthermore, the first sentence 31, the second sentence 32, and the third sentence 33 are generated so as to be consistent with the sentences before and after them.

[0102] As described above, the second event E2 results in a reward, such as information useful for clearing the game. Note that the result of the second event E2 is not limited to the acquisition of information, and other rewards may also be available. For example, the result of the second event E2 may be a reward such as the acquisition of an item or the addition of a specific character to the team.

[0103] The second event E2 may also include options. That is, the second event E2 may be generated so that the ending varies depending on the selection of options within the event. For example, the second event E2 may not have an ending in which useful information is obtained by clearing the game as shown in FIG. 10, but may have an ending in which useful information is not obtained even if the second event E2 is cleared, depending on the options selected.

[0104] After the second event E2 is completed, a second required event E12 occurs. For example, the second required event E12 is an event that can be completed without passing through the second event E2.

[0105] Therefore, it is desirable that the second required event E12 be cleared regardless of the second event E2. In other words, since the content of the second event E2 varies, it is conceivable that specific information or items may or may not be available by the time the second required event E12 is reached, depending on the play. Therefore, it is desirable that the second required event E12 be configured so that it can be cleared, at the very least, with the content of the first required event E11.

[0106] [Overall processing example] FIG. 11 is a diagram showing an example of overall processing. In the following example, the overall processing is performed successively as a pre-processing and an execution processing. Specifically, the pre-processing is steps S01 to S03. The execution processing is steps S04 to S07. However, the overall processing may include other processing steps.

[0107] In step S01, the server 11 receives the first event data DE1.

[0108] In step S02, the server 11 receives the learning data D1.

[0109] In step S03, the server 11 trains the learning model A1 using the first event data DE1 and the learning data D1 to generate a trained model A2.

[0110] Steps S01 to S03 constitute the learning process shown in Fig. 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.

[0111] In step S04, the server 11 inputs the first event data DE1. For example, step S04 is performed in the same manner as step S01. Note that if the content of the first event data DE1 is the same as that during the learning process, the first event data DE1 input in step S01 may be reused, and step S04 may be omitted.

[0112] On the other hand, if the content of the first event E1 differs between the learning process and the execution process, in step S04, new first event data DE1 having content different from that in step S01 is input.

[0113] In step S05, the server 11 receives the unknown data D2.

[0114] In step S06, the server 11 generates output data D3.

[0115] In step S07, the server 11 causes the user 4 to play the game on the user terminal 20 based on the output data D3.

[0116] Steps S04 to S07 constitute the execution process shown in Fig. 9. As described above, when the execution process is performed, the trained model A2 outputs output data D3 based on the unknown data D2. By using such output data D3, for example, a game (as a whole) such as that shown in Fig. 7 is constructed, and when the game is distributed to the user terminal 20, the user 4 can play the game.

[0117] In the above example of the overall processing, the server 11 is mainly responsible for the processing, but the overall processing may be configured to be executed in whole or in part by another information processing device such as the user terminal 20. Therefore, the overall processing may be executed by an information processing device other than the server 11.

[0118] [Example of functional configuration] 12 is a diagram showing an example of a functional configuration. For example, the system 1 is an AI game event generation system including a learning device 61 and an execution device 62.

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

[0120] The first event data input means 1F11 performs a first event data input procedure for inputting first event data DE1 indicating the first event E1. For example, the first event data input means 1F11 is realized by the input / output interface 114 or the like.

[0121] The learning data input means 1F12 performs a learning data input procedure for inputting the learning data D1 including the log data D11 and the correct answer data D12. For example, the 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 learning model A1 is trained using the training data D1 to generate a trained model A2. For example, the learning means 1F13 is realized by the processor 111 or the like.

[0123] The execution device 62 includes a first event data input means 1F11, an unknown data input means 1F21, an execution means 1F22, and a play means 1F23.

[0124] The first event data input means 1F11 in the execution device 62 is the same as the first event data input means 1F11 in the learning device 61, for example.

[0125] The unknown data input means 1F21 performs an unknown data input procedure for inputting unknown data D2 including unknown log data D21 to the trained model A2 generated by the training means 1F13. For example, the unknown data input means 1F21 is realized by the communication interface 115 or the like.

[0126] When the unknown data D2 is input, the execution means 1F22 generates second event data DE2 based on the unknown data D2. Then, the execution means 1F22 performs an execution procedure to generate output data D3 showing the story of the game using the first event data DE1 and the second event data DE2. For example, the execution means 1F22 is realized by the processor 111 or the like.

[0127] Based on the output data D3, the play means 1F23 performs a play procedure to have the user 4 play the game on the user terminal 20. For example, the play means 1F23 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. Of course, the learning device 61 and the execution device 62 may be different information processing devices.

[0129] With the above configuration, it is possible to generate a different second event E2 for each play, relative to the first event E1, which is a required event. Furthermore, since the second event E2 is an event that reflects the personality of the user 4 based on the profile, etc., it can be an event unique to the user 4. Furthermore, if the second event E2 is varied for each play, even for the same player, it is possible to enhance the replayability of the game.

[0130] For example, if a method using a format or the like is used to generate lines spoken by a character, the only change possible is to fit the name to the format, and since the basic story remains the same when the game is played repeatedly, User 4 ends up playing almost the same story and easily becomes bored with the game. In particular, when a format is used, only one story is prepared for each anticipated pattern, so User 4 often only gets to play a smaller number of stories compared to when the second event E2 is generated by AI.

[0131] On the other hand, if the second event E2 is an event that changes with each play, User 4 can play an event with different content each time. Therefore, 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, and may be, for example, a role-playing game (RPG), a sports game, an action game, a puzzle game, or a fighting game.

[0133] For example, in an RPG, important events are set as required events even when the game is played repeatedly. Therefore, if a first event E1 is set as a required event, the user 4 will always play the first event E1. On the other hand, as the game progresses, a second event E2 occurs at any timing. For example, the items that can be obtained in the second event E2, the characters that appear, the character's lines, or the enemies that can be fought will differ for each play. Therefore, it is possible to provide an RPG in which the types of events that occur will differ for each play.

[0134] For example, in a fighting game, the second event E2 may change the subsequent opponent or the character that will be the subsequent opponent (e.g., the character's appearance or parameters may differ) depending on the profile, score, etc. On the other hand, in the first event E1, the opponent is fixed and the player fights the same opponent even when playing repeatedly.

[0135] [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, etc. may be performed by an information system, etc., configured of multiple information processing devices.

[0136] The learning process may be additionally performed after the execution process or before the execution process.

[0137] 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.

[0138] Fig. 13 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. 13 differs in that an auxiliary device 60 is added. Note that the auxiliary device 60 may be a configuration that is used temporarily.

[0139] The auxiliary device 60 is an information processing device that is 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), 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 1: System A1: Learning model A2: Pre-trained model 1F11: First event data input means 1F12: Learning data input means 1F13: Learning tools 1F21: Unknown data input means 1F22: Means of execution 1F23:Method of play 4: User 5:Administrator 11: Server 20: User terminal 30: Choices 31: 1st sentence 32:Second sentence 33: 3rd sentence 60: Auxiliary equipment 61: Learning device 62: Execution device D1: Training data D11: Log data D111: Play Data D112: Image data D113: Profile Data D12: Correct data D2: Unknown data D21: Unknown log data D3: Output data D4: Big Data DE1: First event data DE2: Second event data E1: First event E11: 1st required event E12: Second required event E2: Second event

Claims

1. A computer including a learning device and an execution device, The learning device a first event data input means for inputting first event data indicating a first event to be generated in the game; The first event data, the log data, and the second event for the log data and causing the log data to function as a learning means for generating a trained model that learns the correlation between the log data and the second event using training data including correct answer data indicating the correlation between the log data and the second event; The execution device a first event data input means for inputting the first event data; an unknown data input means for inputting unknown data, which is unknown log data, to the trained model; an execution means for generating second event data indicating the second event based on the correlation and taking the unknown data into consideration when the unknown data is input and output data is output, and for generating the output data indicating the story of the game using the first event data and the second event data; causing a user terminal used by a user playing the game to function as a playing means for allowing the user to play the game based on the output data; The first event is a fixed event whose content is set by the first event data and which the user will definitely play, The second event is The content of the event is set based on the log data, and the event is variable and may vary depending on the user, and is generated differently for each play. The second event is an event that occurs between a plurality of the first events. program.

2. The log data is The game data includes play data indicating the content of the game, image data of the output screen of the game, or profile data indicating the profile of the character or user in the game. The program according to claim 1.

3. A system including a learning device and an execution device, The learning device a first event data input means for inputting first event data indicating a first event to be generated in the game; learning data input means for inputting learning data including the first event data, log data indicating play of the game, and correct answer data indicating a second event for the log data; a learning means for using the learning data to train a learning model that learns a correlation between the log data and the second event to generate a trained model; The execution device a first event data input means for inputting the first event data; an unknown data input means for inputting unknown data, which is unknown log data, to the trained model; an execution means for generating second event data indicating the second event based on the correlation and the unknown data when the unknown data is input and output data is output, and for generating the output data indicating the story of the game using the first event data and the second event data; a playing means for causing a user to play the game on a user terminal used by the user who plays the game, based on the output data; The first event is a fixed event whose content is set by the first event data and which the user will definitely play, The second event is The content of the event is set based on the log data, and the event is variable and may vary depending on the user, and is generated differently for each play; The second event is an event that occurs between a plurality of the first events. system.

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