Program and system

Abstract

To enhance the versatility of advice for a game.SOLUTION: A program causes a computer to function as: storage means capable of storing actions of a user-operable character in a virtual space as a play log; and generation means for generating output data indicating advice regarding a play content related to the play log when the play log is newly stored, on the basis of at least a learned model learned on the basis of learning data including the play log.SELECTED DRAWING: Figure 22

Description

[Technical field]

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

[0002] Conventionally, a technique is known for analyzing game data and providing advice to a user. Specifically, in a racing game, data on previous runs of a course is first downloaded. Then, based on the results of the data analysis, a technique is known for providing advice to a user on previous mistakes during or before a run in the game (for example, Patent Document 1, etc.).

[0003] In addition, in a racing game, when the same course is run, first, the current play is compared with the previous play to analyze the change from the previous play, and then, based on the analysis result, advice is given to the user by voice (for example, Patent Document 2, etc.). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2006-141665 A [Patent Document 2] JP 2002-028370 A Summary of the Invention [Problem to be solved by the invention]

[0005] Providing advice to the user as the game progresses is important in preventing the user from being unable to overcome difficult parts and becoming discouraged, and in allowing the user to continue enjoying the game.

[0006] However, the conventional technology can only provide advice for a limited number of game types, or for racing games where the same course is played repeatedly, and the situations in which advice can be given are limited. In addition, the types of advice are limited to measures to deal with mistakes, etc. In this way, the conventional technology lacks versatility in providing advice for games.

[0007] An object of the present invention is to increase the versatility of advice for games. [Means for solving the problem]

[0008] In order to solve the above problems, the present invention provides a program comprising: Computer, a storage means for storing actions of a user-controllable character in a virtual space as a play log; At least, the device functions as a generating means for generating output data indicating advice regarding the play content related to the play log when the play log is newly stored, based on a trained model trained on learning data including the play log. Effect of the Invention

[0009] According to the present invention, it is possible to increase the versatility of advice regarding games. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of a system configuration according to an embodiment of the present invention. [Diagram 2] FIG. 13 is a diagram illustrating an example of pre-processing. [Diagram 3] FIG. 11 illustrates an example of an execution process. [Figure 4] FIG. 1 is a diagram showing an example of the overall process of AI learning and execution. [Diagram 5] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Figure 6] FIG. 1 is a network diagram showing an example of an AI configuration. [Figure 7]FIG. 11 is a diagram showing an example of input in the first example. [Figure 8] FIG. 11 is a diagram showing an example of input of a moving image in the first example. [Figure 9] FIG. 11 is a diagram showing a first output example in RPG. [Figure 10] FIG. 13 is a diagram showing a second output example in RPG. [Figure 11] FIG. 11 is a diagram showing an example of input in the second example. [Figure 12] FIG. 13 is a diagram showing a first input example in the second example. [Figure 13] FIG. 13 is a diagram showing a second input example in the second example. [Figure 14] FIG. 13 is a diagram showing a third input example in the second example. [Figure 15] FIG. 13 is a diagram showing a fourth input example in the second example. [Figure 16] FIG. 11 is a diagram showing an example of output in the second example. [Figure 17] FIG. 13 is a diagram showing an example of input in the third example. [Figure 18] 13 is an example of input play data. [Figure 19] 13 is an input example of video data in the third example. [Figure 20] FIG. 13 is a diagram showing an example of output in the third example. [Figure 21] FIG. 13 is a diagram illustrating an example of the overall process. [Figure 22] FIG. 2 is a diagram illustrating an example of a functional configuration. [Diagram 23] FIG. 13 is a diagram showing a configuration example in which an auxiliary device is used. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment will be described with reference to the drawings.

[0012] [System configuration example] Fig. 1 is a diagram showing an example of a system configuration according to the present embodiment. For example, as shown in Fig. 1, a system 1 mainly includes user terminals 20A, 20B, and 20C (hereinafter, these may be collectively referred to as "user terminal 20") and a server 11.

[0013] Hereinafter, the person who manages the server 11 will be referred to as the "administrator 5." Also, the people who operate the user terminals 20A, 20B, and 20C will be referred to as the "user 4A," the "user 4B," and the "user 4C" (hereinafter, these may be collectively referred to as the "user 4").

[0014] The administrator 5 is a person who operates the information processing service provided by the system 1. On the other hand, the user 4 is a person who uses the information processing service provided by the system 1. The administrator 5 and the user 4 differ in which information processing device they operate: the server 11, which is an example of a management device, or the user terminal 20. Hereinafter, the user 4 will be a player of the game, and the administrator 5 will manage the game and the server 11.

[0015] 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.

[0016] The server 11 and the user terminal 20 are connected to be able to communicate with each other via a communication network 2. For example, the communication network 2 is the Internet, a mobile communication system (for example, a public line based on 4G (4th Generation, fourth generation mobile communication standard) or 5G (5th Generation, fifth generation mobile communication standard)), a wireless network such as Wi-Fi (registered trademark), or a combination of these.

[0017] 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.

[0018] [Examples of AI (Artificial Intelligence) learning and execution] Hereinafter, AI learns through "pre-processing." AI in the learning stage, i.e., "pre-processing," will be referred to as the "learning model A1." Then, once learning has progressed to a certain extent, the learning model A1 becomes the "trained model A2." Hereinafter, the execution stage in which output processing is executed using the trained model A2 will be referred to as the "execution process."

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

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

[0022] The learning data D1 includes known log data and is data associated with a "correct answer" for this log data. Specifically, the learning data D1 includes log data indicating the game play results and the play process, such as the eleventh data D11, the twelfth data D12, the thirteenth data D13, etc. (including a data group that combines a plurality of data such as the eleventh data D11, the twelfth data D12, and the thirteenth data D13. Hereinafter, simply referred to as "log data"), and the correct answer data D20.

[0023] The learning model A1 learns a correspondence relationship that outputs correct answer data D20 for input of log data based on input of learning data D1.

[0024] The log data will be described in detail later.

[0025] Furthermore, it is preferable that the learning model A1 learns using big data D4. For example, the big data D4 is data on the Internet. However, the big data D4 may be data input by an administrator 5 or the like.

[0026] [Example of execution process] 3 is a diagram showing an example of the execution process. For example, the execution process is performed by the user terminal 20, or by the user terminal 20 and the server 11 working together.

[0027] 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 FIG. 2 is executed, the trained model A2 is generated.

[0028] When unknown data D2 is input, the trained model A2 generates output data D3 for the unknown data D2.

[0029] The unknown data D2 is unknown log data, that is, data in which the "correct answer" to the log data is unknown at the time of input. Specifically, the unknown data D2 includes unknown log data indicating the game play results and the play process, such as the twenty-first data D21, the twenty-second data D22, the twenty-third data D23, ... (including the case of a data group that combines multiple data such as the twenty-first data D21, the twenty-second data D22, and the twenty-third data D23). For example, the unknown data D2 is composed of the same type of data as the learning data D1 and the log data.

[0030] 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.

[0031] The output data D3 and the output based on the output data D3 will be described in detail later.

[0032] 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.

[0033] Note that the pre-processing and the execution processing do not have to be executed in the consecutive order as illustrated in the figure. Therefore, it is not essential that the period in which preparation is performed by the pre-processing and the period in which the execution processing is performed thereafter are consecutive. Therefore, the execution processing may be executed after a time has elapsed from the pre-processing, once the trained model A2 has been created. Also, once the trained model A2 has been generated, the execution processing may be executed by repurposing the trained model A2.

[0034] The learning data D1 and unknown data D2 are different between the learning process and the execution process. Also, the AI ​​is a learning model A1 in the learning stage, but when learning progresses to a certain extent, it becomes a trained model A2. In this way, the trained model A2 trained using big data D4 as training data is a so-called "generative AI."

[0035] The eleventh data D11 included in the learning data D1 and the twenty-first data D21 included in the unknown data D2 (hereinafter, the twelfth data D12 and the twenty-second data D22, etc. have the same relationship) are the same data type. That is, the eleventh data D11 and the twenty-first data D21 are both log data.

[0036] In the learning data D1, the "correct answer" is known, whereas in the unknown data D2, the "correct answer" is unknown. Specifically, the learning data D1 includes the correct answer data D20, whereas the unknown data D2 does not include the correct answer data D20. Therefore, in the learning data D1, the relationship between the log data and the correct answer data D20 is known.

[0037] On the other hand, the unknown data D2 does not include the correct answer data D20, 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 trained in pre-processing and the correct answer data D20.

[0038] A part of the execution process may be replaced by a process using a table, etc. In this way, the pre-processing in the process using a table, etc. (so-called rule-based processing) is a process of preparing for inputting a table (also called a look-up table (LUT) or a mathematical expression, etc.).

[0039] [Example of hardware configuration of information processing device] 5 is a hardware configuration diagram of an information processing device. The information processing device is a server 11, a user terminal 20, etc. Hereinafter, the information processing device is assumed to have the same hardware configuration as the server 11. For example, the information processing device is a workstation or a general-purpose computer such as a personal computer. However, each information processing device may have a different hardware configuration.

[0040] The server 11 mainly includes a processor 111, a memory 112, a storage 113, an input / output interface 114, and a communication interface 115. In addition, each component of the server 11 is connected to a communication bus .

[0041] The processor 111 executes a series of instructions contained in a server program 11P stored in the memory 112 or the storage 113, thereby achieving processing and control.

[0042] 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.

[0043] The memory 112 is a main storage device (which is volatile) that stores the server program 11P and data, etc. For example, the server program 11P is loaded from the storage 113. The data includes data input to the server 11 and data generated by the processor 111. For example, the memory 112 is a RAM (Random Access Memory) or other volatile memory.

[0044] The storage 113 is an auxiliary storage device (non-volatile) that stores the server program 11P and data, etc. The storage 113 is, for example, a ROM (Read-Only Memory), a hard disk drive, a flash memory, or other non-volatile storage device. The storage 113 may also be a removable storage device such as a memory card. As another example, the storage 113 may be an external storage device. With this configuration, for example, in a scene where multiple user terminals 20 are used, such as an amusement facility, it becomes possible to collectively update the server program 11P or data.

[0045] 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.

[0046] The processor 111 also communicates with external devices via an input / output interface 114. The input / output interface 114 is, for example, a Universal Serial Bus (USB), a Digital Visual Interface (DVI), a High-Definition Multimedia Interface (HDMI (registered trademark)), wireless, or other terminal.

[0047] The communication interface 115 communicates with other devices (e.g., the user terminal 20, etc.) connected to the communication network 2. For example, the communication interface 115 is a wired communication interface such as a Local Area Network (LAN), or a wireless communication interface such as Wireless Fidelity (Wi-Fi), Bluetooth (registered trademark), or Near Field Communication (NFC).

[0048] 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.

[0049] [Examples of learning model and trained model configuration] 6 is a network diagram showing an example of the configuration of an AI. The learning model A1 and the trained model A2 are, for example, AIs having a configuration shown in the following network.

[0050] Hereinafter, the learning model A1 and the trained model A2 will be described as being implemented on the server 11, i.e., on the cloud. However, a part or all of the learning model A1 and the trained model A2 may be implemented on the user terminal 20, etc.

[0051] The network 300 has, for example, an input layer L1, an intermediate layer L2 (also called a "hidden layer"), and an output layer L3.

[0052] The input layer L1 is a layer that inputs data.

[0053] The intermediate layer L2 converts the data input in the input layer L1 based on weights (e.g., coefficients used for multiplication) and biases (e.g., adding a constant), etc. The results of processing in this way in the intermediate layer L2 are transmitted to the output layer L3.

[0054] The output layer L3 is a layer that outputs output contents, etc.

[0055] Then, through learning, weight coefficients (for example, coefficients for input characters or images are changed based on learning) and parameters changed through learning are optimized. Note that the network 300 is not limited to the network structure shown in the figure. In other words, the AI ​​may be realized by other machine learning.

[0056] For example, the AI ​​may be configured to perform preprocessing such as dimensionality reduction (for example, a process that converts a relationship of three or more dimensions into a relationship that can be obtained by simple calculations of three dimensions or less) using "unsupervised" machine learning. It is desirable to process the relationship between input and output using simple calculations such as linear expressions. This type of calculation reduces calculation costs and makes it possible to accurately determine the degree of deterioration.

[0057] In addition, the AI ​​may undergo processing to reduce overfitting, such as dropout. In addition, preprocessing such as dimensionality reduction and normalization may be performed.

[0058] 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.

[0059] The AI ​​may also have hyperparameters. In other words, the AI ​​may be configured such that some of the settings are performed by a user, etc. Furthermore, the AI ​​may specify features to be learned, or the user may set some or all of the features to be learned.

[0060] Furthermore, the learning model A1 and the trained model A2 may use other machine learning. For example, the learning model A1 and the trained model A2 may be preprocessed by normalization using an unsupervised model. Furthermore, the learning may be reinforcement learning (a learning method in which an AI is made to make a choice and an evaluation (reward) is given for the choice, resulting in a larger evaluation), etc.

[0061] In the learning, data expansion or the like may be performed. That is, in order to increase the amount of learning data used in the learning of the learning model A1, a single piece of experimental data or the like may be expanded and pre-processed to generate multiple pieces of learning data. In this way, if the amount of learning data can be increased, the learning of the learning model A1 can be further advanced.

[0062] Furthermore, the learning model A1 and the trained model A2 may be configured to perform transfer learning, fine tuning, or the like. That is, since the user terminal 20 often has a different execution environment for each device, the settings may be different for each device according to the execution environment. For example, the basic configuration of the AI ​​is trained on another information processing device. After that, each information processing device may be further trained or configured for further optimization to each execution environment.

[0063] [Example of log data] When a game is played on the user terminal 20, the log data is generated in accordance with the progress of the game. Hereinafter, it is assumed that the play log is stored in the log data. The play log is a record indicating what actions a character controllable by the user 4 performed in the virtual space. The unknown data D2 may be generated based on an operation by the user 4 (e.g., an operation to save the game, etc.), or may be automatically generated by the user terminal 20 in the background in accordance with the progress of the game.

[0064] The log data is data that indicates the progress of playing a game.

[0065] In the following, an example will be described in which the eleventh data D11 in the pre-processing and the twenty-first data D21 in the execution processing are the first data.

[0066] The first data is data indicating a profile of a character appearing in the game. Or, the first data is data indicating a profile of a user 4 who performs operations on the game. For example, the first data is input by the user 4 when the game starts. However, the first data may be acquired from the user terminal 20 or another database, or a part or all of the profile may be determined randomly or by presetting in the game. Hereinafter, the profile is said to be fixed once it is determined, but the profile may change during the game.

[0067] Specifically, the first data includes setting items such as name, background, occupation, and friendships.

[0068] The name is the real name, abbreviation, pseudonym, etc. of the user 4. The name is also identification information such as a nickname set for the character.

[0069] The background is the user's 4 history or the in-game history set for the character.

[0070] The occupation is the occupation or work history of the user 4. The occupation is an in-game occupation set for the character.

[0071] A friendship is a relationship with another person who is or has been involved with the user 4 or character.

[0072] The second data is event information that has occurred up until the time when advice is given in the game (hereinafter referred to as the "advice time"), or data that indicates the process up until the advice time. For example, the second data is generated in the background while the user 4 is playing the game.

[0073] Specifically, the second data is image data (the image data is a still image or a video) that is recorded from a screen (the screen may be from the viewpoint of the user 4 or another viewpoint) showing the play by the user 4. In addition, the second data is process information such as the content of the event played by the user 4, the play results, the score, the time taken, or other participants.

[0074] For example, when an event or a "battle" occurs during game play, the second data is data showing the results of the event or the "battle," the operation content, the enemy character encountered, and various images. Therefore, the second data is in the form of image data, text data, or other recorded data.

[0075] An event has a specific goal, conditions for participation, or a period during which it can be participated in the game, and ends when the specific goal is achieved, the specific conditions are met, or a specific period of time has passed. Specifically, when an event occurs, an event-specific item may be acquired, an event-specific character may appear, or an event-specific benefit may be obtained, depending on the period during which the event occurs or the actions taken within the event. Events may also include "battles" and the like. Furthermore, when an event occurs, various events occur, and there are many scenes that are worth seeing.

[0076] The third data is data showing an output screen output up until the time of the advice. For example, the third data is image data or the like that is a recording of an output screen output when the user 4 plays. Note that the image data is not limited to being recorded.

[0077] The third data is image data showing the play process that has been output in the game up until the advice point.

[0078] [Example of execution process in RPG] In the following, the type of game is assumed to be an RPG (role-playing game). Also, when the game progresses and the player saves, the game will start from the point where the player saved the game.

[0079] In the following example, the output is made from the time when the game was last saved and resumed to the time when advice was given, but the time when the output corresponds to the time may be set by the user 4 or may be the start of the game, etc.

[0080] Fig. 7 is a diagram showing an example of input in Example 1. For example, when a game is played on the user terminal 20 with respect to the server 11 on which the trained model A2 generated by the pre-processing shown in Fig. 2 is implemented, unknown data D2 is transmitted to the server 11.

[0081] In addition, through pre-processing, i.e., learning using big data D4, the trained model A2 has learned game strategies and the like based on information on the Internet. Therefore, the trained model A2 is in a state where it can give advice on how to progress through the game.

[0082] For example, on the Internet, message boards or websites for the target game may contain walkthrough information. In addition, on video sites, etc., there may be videos showing good plays, such as game play videos. For example, the trained model A2 learns using the big data D4 as training data, and therefore understands how to play the game well.

[0083] The unknown data D2 is transmitted to the server 11, for example, when a so-called save point is reached during game play. However, the unknown data D2 does not have to be transmitted all at once, and the constituent data may be divided and transmitted to the server 11 while the game is in progress.

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

[0085] The process data D201 is data that records events, results of various actions, character movement, growth, parameter changes, etc. The process data D201 may be input by the user 4 (including a case where the user 4 inputs only a part of the data), or may be generated by an AI based on the content of the gameplay. The process data D201 may also be data in a format that the user 4 cannot read.

[0086] Additionally, it is desirable to receive an advice request command D5 from the user 4. For example, assume that a button for the user 4 to request advice (hereinafter referred to as an "advice button", although other types of GUIs may be used) is placed on the screen.

[0087] In the following, the advice time will be the time when the advice button is pressed. However, the advice time and the time when the advice is output may be different. Specifically, depending on the type of game, the advice time, i.e., the time when advice becomes available, may differ from the situation in which the advice is desired to be output. In other words, if advice is output immediately at the advice time, the output of the advice (for example, a GUI such as a text box) may be displayed on the screen and become a hindrance in the progress of the game, or user 4 may be too busy operating the game to have time to see the advice.

[0088] In this way, there are cases where user 4 wants to see the advice later, but wants to set the time point at which the advice is requested. In such a case, when the advice button is pressed, it is desirable that the advice be provided mainly around the time point at which the advice button was pressed on a dedicated screen for providing advice later. In this way, the advice time point may be in a format that user 4 can specify. And, if user 4 can specify the advice time point, user 4 can view the advice at an appropriate timing.

[0089] Also, it is preferable that the user 4 can set whether or not the advice is displayed. Depending on the content of the advice, information showing future game content may be displayed, which may be a so-called "spoiler." In other words, for the user 4 who is looking forward to the next development, it may be preferable that advice including future content is not displayed. Therefore, if the advice display can be switched, "spoilers" can be prevented, and the user can enjoy the game more.

[0090] FIG. 8 is a diagram showing an example of input of a video in the first example. For example, the first video data D2021 is data recorded mainly on movement scenes in a RPG, as shown in FIG. In this way, the log data may include data showing a play in a format other than text. In this case, the video data D202 is subjected to image analysis to extract keywords, parameters, etc.

[0091] The log data may include data that is not directly visible to the user 4. For example, data used in the background, such as play time or a character's movement range, may be included as a profile. Other data, such as the character's name, status, parameters, equipment, and participants, may be included in the log data.

[0092] 9 is a diagram showing a first output example in the RPG. For example, when log data such as the first video data D2021 is input, the trained model A2 generates the first output data D31 by execution processing. Then, based on the first output data D31, the summary text D311, the digest image D312, and the advice D313 are output. In this way, the output may be performed in various formats such as text, images, audio, and graphs.

[0093] FIG. 9 shows an example in which an output based on the first output data D31 is performed on a so-called "save screen", that is, on a part of an operation screen for saving a game.

[0094] The summary text D311 is text summarizing in the form of a "diary" the gameplay from the time the game was last saved to the time the game is next saved (i.e., the time the screen shown in FIG. 9 is opened). Specifically, the summary text D311 is generated based on the big data D4 in the same manner as the text structure of an actual diary or the like. Meanwhile, proper nouns and the like that appear in the text included in the summary text D311 are the names of characters that appear in the gameplay. When such summary text D311 is output together with advice D313, the user 4 can grasp the game progress and the like.

[0095] The summary text D311 is text that explains events ("△△" is the name of the event) that occurred during game play. Items that the summary text D311 explains are summaries of events that were participated in, opponents played against ("◆◆◆◆" is the name of a representative opponent), and occurrences that occurred during play ("□□□" is the name of a character that actually participated in the game).

[0096] The digest image D312 is an image showing a highlight scene in the play indicated by the summary text D311. That is, the digest image D312 shows the contents of the summary text D311 in the form of an image. Such an image allows the user 4 to more easily grasp the progress of the game, etc. The digest image D312 may be a still image or a video. Moreover, the digest image D312 is preferably a few still images or a video of about several seconds.

[0097] For example, the digest image D312 is generated by extracting a portion from the video data D202. However, the digest image D312 may be generated using an image other than the video data D202. For example, the digest image D312 may be generated by extracting or processing an image from the big data D4 or the like.

[0098] The advice D313 analyzes the current state of play based on the log data, and indicates information that is useful for the subsequent progress of the game. For example, the advice D313 indicates effective items and ways to deal with character techniques, etc. Also, the advice D313 may be based on an analysis of the trends of other players (data on other players is acquired and analyzed by the server 11), such as "There are many players heading north from here."

[0099] 10 is a diagram showing a second output example in an RPG. For example, the output of advice D313 does not have to be on the same screen as the screen for saving, etc. In other words, there may be a dedicated screen for outputting advice D313, rather than being part of the GUI used for other functions such as saving.

[0100] Fig. 10 is an example of a dedicated screen for viewing a summary and advice. For example, when the user 4 performs an operation to open the dedicated screen, the screen shown in Fig. 10 is displayed. The time when such a dedicated screen is opened becomes the advice time. However, the advice time may be set separately, and this dedicated screen may output advice D313 corresponding to the advice time.

[0101] [Example of execution process in sports game] A second example of playing a golf game will be described below, however, the sports game may be a sport other than golf, such as soccer, baseball, tennis, basketball, motor sports, winter sports, table games, fishing, or sumo.

[0102] Fig. 11 is a diagram showing an example of input in Example 2. For example, when a game is played on a user terminal 20 with respect to a server 11 on which a trained model A2 generated by the pre-processing shown in Fig. 2 is implemented, unknown data D2 is transmitted to the server 11.

[0103] Furthermore, through pre-processing, i.e., through learning, the trained model A2 has learned game strategies and the like based on information on the Internet through the big data D4. The learning may utilize the results of image analysis of videos. Therefore, the trained model A2 is in a state where it knows what content should be written in the advice D313 in order to progress through the game.

[0104] 12 is a diagram showing a first input example in the second example. For example, event information is input as log data. The event information is, for example, an event name D2011 or an event difficulty level D2012.

[0105] The event name D2011 is information for identifying an event described in a video or the like. Note that the event name is not limited to the event name D2011, and other identification information may be used at the time of the event, as long as the event can be identified. If such event identification information is available, it is possible to collect information mainly about similar events from the big data D4.

[0106] The event difficulty D2012 is information indicating the difficulty of conquering an event. In other words, the event difficulty D2012 makes it possible to collect information about events of similar difficulty. Specifically, the difficulty level is set in advance to be divided into four stages, such as "DIAMOND", "PLATINUM", "GOLD", and "SILVER". In this example, the highest level of difficulty is "DIAMOND". The difficulty levels are set in the order of "DIAMOND", "PLATINUM", "GOLD", and "SILVER", becoming easier in that order.

[0107] The difficulty levels are not limited to the above-mentioned division into stages, and may be divided into less than four stages or five stages or more. The names of the difficulty levels may be other than those mentioned above.

[0108] For example, the difficulty level is set based on experience points, score (which may be the most recent score or average, etc.), level, or user 4 settings, and the character belongs to one of the stages based on predetermined criteria.

[0109] The event difficulty level D2012 may be estimated from the characteristics of the course, the participants, the character levels, and the like.

[0110] 13 is a diagram showing a second input example in Example 2. For example, the character operated by user 4 (hereinafter, information on the character operated by user 4 is referred to as "character information D2013") and information on settings related to the character (hereinafter, referred to as "setting information D2014") are input as log data.

[0111] The character information D2013 includes, for example, the character's name, status, level, parameters, and other settings.

[0112] The setting information D2014 is, for example, items that a character can use. Specifically, in a golf game, it is possible to set the type of golf club that a character can use. The setting information D2014 is the selected type of golf club, etc. In addition, if there is external environment information or settings that affect the parameters of the character, etc., these may be input as log data.

[0113] 14 is a diagram showing a third input example in Example 2. For example, the result of an operation by the user 4 is converted into video data D202 and input as log data.

[0114] In this example, the video data D202 is data that records an output screen in which the club hits the ball and the ball flies, such as a first timing D2015, a second timing D2016, etc., before the club is swung. Furthermore, information such as the flying distance may be further input as an individual result of this play. Other individual results of a play may be, for example, in a game in which good timing in touching the screen causes the ball to fly as intended, whether or not the timing of touching (including tapping) the screen was good (if "GREAT!" is displayed as in the figure, the evaluation result is that the timing was good), etc.

[0115] 15 is a diagram showing a fourth input example in Example 2. For example, the course score D2017 for each course when the course is cleared is input.

[0116] The course results D2017 include the score D2018 for each course. In addition, when the same course is played multiple times, such as a "best score," a statistical value may be generated by statistical processing including the results of the previous play.

[0117] The course results D2017 are not limited to the score D2018, but may be other numerical values, cleared time, ranking, or the like.

[0118] When information indicating results is input, such as course results D2017, it can be determined whether the player played well, with a high score or a good ranking.

[0119] 16 is a diagram showing an example of output in Example 2. For example, the advice is output in a text format. For example, the advice includes a cause explanation D321, differences D322, and points to note D323.

[0120] The cause explanation D321 is text that indicates the cause of poor performance. For example, the cause explanation D321 is the result of an analysis such as a comparison with the play of other players. In particular, it is desirable to compare with players who are good at playing. For example, a comparison with other players in the top 10% of performance may reveal areas for improvement.

[0121] It is desirable that the cause explanation D321 be outputted only for those things that improve the results. For example, it is desirable that the cause explanation D321 be outputted only for those things that have a strong tendency to improve the results when advice is given based on learning. There are often multiple elements that can be compared with the play of other players. Therefore, if all the comparison results are listed in the cause explanation D321, the amount of description is likely to become enormous. Also, there are cases where things that have little to do with improving the results are included in the cause explanation D321. Therefore, it is desirable that the cause explanation D321 be generated only for those things that are likely to improve the results by learning the results of past advice, etc.

[0122] The differences D322 are texts indicating differences in settings with other players, etc. In other words, if there is a difference, such as when the user 4 has different settings from other players when playing the same course, the differences D322 explain how the settings differ from the other players.

[0123] As with the cause explanation D321, it is desirable to limit the difference points D322 to those that improve grades and output them. That is, in many cases, multiple types of difference points D322 can be output. Therefore, it is desirable to generate the difference points D322 by learning the results of past advice, etc., and limiting them to those that are likely to improve grades.

[0124] The attention points D323 indicate in text specific measures for the user 4 to improve his / her play. For example, the attention points D323 specifically explain measures such as referring to information such as distance and wind direction to select a good club. It is also desirable to give advice on other useful items or operation methods.

[0125] Also, the attention points D323 show a countermeasure for improving the cause explanation D321 and the difference D322. Specifically, as shown in Fig. 16, the cause explanation D321 deals with the cause of "swinging the club one beat slow," whereas the attention points D323 clearly show a specific value of how much faster the swing should be, and an operation method, etc.

[0126] The advice is not limited to text, and may be accompanied by a video or the like.

[0127] [Example of execution process in fighting games or competitive games] The following description will be given taking as an example a fighting action game in which characters mainly fight one-on-one.

[0128] Fig. 17 is a diagram showing an example of input in Example 3. For example, when a game is played on the user terminal 20 with respect to the server 11 on which the trained model A2 generated by the pre-processing shown in Fig. 2 is implemented, unknown data D2 is transmitted to the server 11.

[0129] In this example, the input data includes the settings that user 4 makes for the character before the match, and the results of operations performed during the match (including both operations by user 4 and operations by the opponent), which are input into the trained model A2 as unknown data D2.

[0130] Specifically, the unknown data D2 includes, for example, gameplay data D211 and video data D202.

[0131] The play data D211 is, for example, an operation history of the user 4 operating various buttons or an operation stick of a controller (so-called "command operation", such as a case where a certain command operation is performed to perform a so-called "special move"), the hit rate of each attack, and the like. In the case of a game operated on a smartphone, the play data D211 may be, for example, an operation history of taps, flicks, and the like. Therefore, the play data D211 differs depending on the type of device on which the game is played, the type of game, the interface, settings, and the like.

[0132] In addition, the play data D211 may include parameters (which may include parameters that the user 4 cannot directly see or operate), settings (including settings related to the stage if they are affected by the stage on which the battle takes place), etc.

[0133] Furthermore, the play data D211 includes opponent information (for example, the identification information, settings, and parameters of the battle opponent. If there is compatibility with the opponent, detailed information such as compatibility is included), and results, etc.

[0134] 18 shows an example of input of play data. For example, when a pre-match setting screen as shown in the figure is input, character data D2111, setting data D2112, etc. are input.

[0135] The character data D2111 includes the name, parameters, status, etc. of the character to be used. The character data D2111 may be in the form of an image (which may be analyzed from an image) or in another parameter format.

[0136] The setting data D2112 is, for example, settings for weapons equipped by a character, etc. The setting data D2112 indicates changes in the parameters of the character due to the set weapon, etc. Details of the setting data D2112 may be obtained from a database, etc.

[0137] 19 is an example of input of video data in Example 3. For example, an output screen during a match as shown in the figure is recorded and input as video data D202.

[0138] Specifically, as a result of image analysis of the video data D202 or by collating it with the command operation history, opponent actions D2113, player actions D2114, and the like are acquired.

[0139] The opponent action D2113 and the player action D2114 may also indicate play results, such as receiving damage as a result of an operation or avoiding an attack.

[0140] 20 is a diagram showing an example of output in the third example. For example, on a screen after a match has ended, an output including advice is given in the following format. However, advice may be given at other times or on other screens.

[0141] For example, a reference image D331 and an image operation GUID 332 are displayed on the output screen. The reference image D331 is an image recorded during a match. When the image operation GUID 332 is operated, the time at which the reference image D331 is output changes.

[0142] For example, important points in giving advice, such as "first advice point D333" and "second advice point D334", are displayed on the time bar in the image operation GUID 332. Furthermore, when the first advice point D333 or the second advice point D334 is clicked, the reference image D331 outputs a scene that is important in giving advice. In this way, when points that can serve as advice are extracted, the user 4 can easily review his or her own operations in order to improve his or her game skills.

[0143] Additionally, the advice display D335 is output as an image, text, etc. The advice display D335 indicates, for example, the result of an analysis of the cause of defeat and a countermeasure, etc. Specifically, the advice display D335 indicates the point in time when the most damage was received, or what countermeasures can be taken to reduce the damage, etc.

[0144] The advice display D335 and the reference image D331, etc. may be linked. Therefore, when the reference image D331 is switched by operating the image operation GUID332, the advice display D335 corresponding to the reference image D331 may be switched. In this way, when countermeasures, etc. are output in conjunction with the user's play results, the user 4 can easily understand which points need to be improved.

[0145] [Overall processing example] 21 is a diagram showing an example of the overall process. In the following example, the overall process is a sequence of a pre-process and an execution process. Specifically, the pre-process is step S01. The execution process is steps S02 to S04. However, the overall process may include other processes.

[0146] In step S01, the server 11 inputs the learning data D1 including the log data and the correct answer data to train the learning model A1. Then, when step S01 is performed, a trained model A2 is generated. In this way, when the learning model A1 is trained to become the trained model A2, the execution process is performed using the trained model A2.

[0147] In step S02, the server 11 inputs unknown data D2. For example, step S03 is executed in the background in parallel with the progress of the game, that is, transmitted from the user terminal 20 to the server 11 without the user 4 being aware of it. The unknown data D2 may include data generated in the server 11.

[0148] In step S03, the server 11 judges whether it is time for advice. Next, if it is time for advice (YES in step S03), the server 11 proceeds to step S04. On the other hand, if it is not time for advice (NO in step S03), the server 11 proceeds to step S02. That is, step S04 and subsequent steps are executed in response to an operation such as requesting advice.

[0149] In step S04, the server 11 generates the output data D3. For example, the timing of generating the output data D3, that is, the timing of executing step S04, is set in advance.

[0150] In step S05, the server 11 performs output based on the output data D3. For example, the timing of output or the screen to be output, that is, the timing and target of executing step S05, are set in advance.

[0151] [Example of functional configuration] 22 is a diagram illustrating an example of a functional configuration of the system 1. For example, the system 1 is an advice output system including a learning device 31 and an execution device 32.

[0152] The learning device 31 includes a learning data input means 1F1 and a learning means 1F2.

[0153] The learning data input means 1F1 performs a learning data input procedure for inputting the learning data D1 including the log data and the correct answer data D20. For example, the learning data input means 1F1 is realized by the communication interface 115 or the like.

[0154] The learning means 1F2 performs a learning procedure in which the learning model A1 is trained using the training data D1 to generate a trained model A2. For example, the learning means 1F2 is realized by the processor 111 or the like.

[0155] The execution device 32 includes a storage means 1F10 and a generation means 1F4. It is preferable that the execution device 32 includes an unknown data input means 1F3 and an output means 1F5. The storage means 1F10 may be included in the learning device 31. Alternatively, the storage means 1F10 may be shared by both the learning device 31 and the execution device 32.

[0156] The unknown data input means 1F3 performs an unknown data input procedure for inputting unknown data D2 including unknown log data to the trained model A2 generated by the training means 1F2. For example, the unknown data input means 1F3 is realized by the communication interface 115 or the like.

[0157] The generating means 1F4 performs a generating procedure for generating output data D3 indicating advice on the play content related to the play log when the play log is newly stored based on the trained model A2. For example, the generating means 1F4 is realized by the processor 111 or the like.

[0158] Based on the output data D3, the output means 1F5 performs an output procedure to output to the user terminal 20. For example, the output means 1F5 is realized by the communication interface 115 or the like.

[0159] The storage means 1F10 performs a storage procedure for storing, as a play log, the actions of the user-operable character in the virtual space, etc. For example, the storage means 1F10 is realized by the storage 113 or the like.

[0160] The learning device 31 and the execution device 32 are, for example, the server 11. However, the learning device 31 and the execution device 32 may be different information processing devices.

[0161] With the above configuration, advice is output to the user 4 as the game progresses. For example, the advice is presented by analyzing and collecting information on how to deal with the problem or the reasons for the problem. However, the user 4 may be able to set advice ON / OFF.

[0162] When such advice is output, the user 4 can enjoy the game because the advice is based on the individual and specific play results and situation of the user 4. In other words, the advice is not a set phrase, but is given flexibly to suit the user 4.

[0163] Also, advice can be output for different types of games, such as RPG. In this way, versatile advice can be output. It is preferable that the advice is different each time it is output. In other words, multiple pieces of advice can be generated and output randomly or in order each time a request is made.

[0164] The type of game is not limited to the above types. As long as advice is output during the progress of the game, the type of game is not limited. For example, the type of game may include sports, action, puzzle, fighting, etc.

[0165] [Variations] The unknown data D2 may be pre-processed. The pre-processing may include a process of limiting the unknown data D2. For example, the pre-processing is preferably such that the video data D202, which is the third data input as the unknown data D2, is limited to data at important points in time. In particular, the data volume of the video data D202 tends to be large if it is long. Therefore, the data volume can be reduced by limiting the video data D202.

[0166] For example, the unknown data D2 includes all output screens from the start point to the advice point. Therefore, the pre-processing is a process of excluding data from a predetermined time before the advice point in order to narrow the unknown data D2 down to the most recent data. In this way, when the pre-processing is performed to narrow the unknown data D2 down to the most recent data, it is possible to output data that is mainly new information.

[0167] In addition, when extracting highlights, the preprocessing may be a process of narrowing down the unknown data D2 to a point where the profile changed suddenly or a point where the character was in trouble (for example, 10 minutes before and after the point to be identified). When preprocessing is performed in this way, the amount of data to be processed is reduced, and the execution process can be sped up.

[0168] For example, a point in time when so-called "hit points (HP)" suddenly decrease is often a time when the character is in trouble. In addition, when a so-called "battle" is long, it is often a time when the character is in trouble. If such a point in time can be searched for using log data, etc., it is possible to identify the point in time when the character is in trouble.

[0169] The point in time when the profile changes suddenly is often the point in time when the user 4 makes an effort to temporarily strengthen the parameters of the character. After such a point in time, it is often the highlight when the strengthened character plays an active role. If such a point in time can be searched for in the log data, etc., the point in time when the character plays an active role can be identified.

[0170] In this way, it is desirable to perform preprocessing in which not all of the unknown data D2 is input to the trained model A2, but rather carefully selected data is input to the trained model A2.

[0171] [Other embodiments] The virtual space is not limited to a game. For example, the virtual space may be a metaverse. The metaverse is a combination of the words "meta" (transcendence) and "universe." The metaverse refers to a three-dimensional virtual space on a computer network in which multiple people can participate and in which participants can act freely. Therefore, the character that the user 4 can operate may be an avatar in the metaverse space.

[0172] 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. In addition, the pre-processing and execution processing do not have to be consistently performed by one information processing device. In other words, each process and data storage, etc. may be performed by an information system, etc., composed of multiple information processing devices.

[0173] [About the "Diary"] The "journal" may have any name, type of medium, format, or information structure as long as it describes the game play. In other words, within the game, the "journal" may be operated or output using various GUIs, etc.

[0174] On the other hand, when generating a "journal" in text format, journals, diaries, blogs, reports, comments, or articles that describe matters other than the game may be used as reference for learning. In other words, the learning data may include data other than what is called a "journal."

[0175] The learning process may be additionally performed after the execution process or before the execution process.

[0176] 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.

[0177] Fig. 23 is a diagram showing a configuration example using an auxiliary device. Compared to the example shown in Fig. 1, the configuration shown in Fig. 23 differs in that an auxiliary device 60 is added. Note that the auxiliary device 60 may be a configuration that is used temporarily.

[0178] The auxiliary device 60 is an information processing device installed near the user terminal 20 (in this example, it is installed near the user terminal 20A, but it may be installed near other devices), etc. The auxiliary device 60 executes a part or all of a specific process on behalf of the user terminal 20 or the server 11.

[0179] For example, the auxiliary device 60 is provided with a device specialized for graphic processing and performs graphic processing at high speed. In this way, so-called edge computing or the like may be performed by installing the auxiliary device 60 or the like. In this way, the above-mentioned processing may be executed by utilizing the hardware resources of various information processing devices. Therefore, the above-mentioned processing may be executed by an information processing device different from the above-mentioned one.

[0180] The above-described processes and data used in the processes executed in this embodiment may be executed and stored by an information processing system. For example, the information processing system may execute or store the data in a plurality of information processing devices in order to realize redundant, distributed, parallel, or a combination of these processes or storage. Therefore, the present invention may be realized in devices other than the hardware configurations shown above and in systems other than the devices shown above.

[0181] In addition, the program according to the present invention is not limited to a single program, but may be a collection of multiple programs. In addition, the program according to the present invention is not limited to being executed by a single device, but may be executed by multiple information processing devices. Furthermore, the role allocation of each information processing device is not limited to the above example. In other words, a part or all of the above-mentioned processing may be executed by an information processing device different from the above-mentioned information processing device.

[0182] Furthermore, a part or all of the means realized by the program can be realized by hardware such as an integrated circuit. Furthermore, the program may be provided by being recorded on a non-transitory recording medium readable by a computer. The recording medium refers to, for example, a hard disk, an SD card (registered trademark), an optical disk such as a DVD, or a server on the Internet. Therefore, the program may be distributed via a telecommunication line such as the Internet.

[0183] Furthermore, the information processing devices that make up the information processing system may be located overseas.

[0184] The present invention is not limited to the above-mentioned embodiments. Therefore, the present invention can be modified or modified without departing from the technical gist of the invention. Therefore, all technical matters included in the technical ideas described in the claims are the subject of the present invention. The above-mentioned embodiments are preferred examples. Those skilled in the art can realize various modifications from the disclosed contents, and such modifications are included in the technical scope described in the claims. [Explanation of symbols]

[0185] 1: System 1F1: Learning data input means 1F2: Learning methods 1F3: Means for inputting unknown data 1F4: Generation means 1F5: Output method 1F10: Storage means 3:Administrator 4: User 5:Administrator 11: Server 20: User terminal 31: Learning device 32: Execution device 60: Auxiliary equipment D1: Training data D2: Unknown data D20: Correct data D4: Big Data A1: Learning model A2: Trained model

Claims

1. Computer, a storage means for storing actions of a user-controllable character in a virtual space as a play log; A program that functions as a generation means for generating output data indicating advice regarding the play content related to the play log when the play log is newly stored, based on at least a trained model trained based on learning data including the play log.

2. The log data indicating the play log includes: First data indicating a parameter in a game or an operation of a user on the game; and second data showing an output screen that has been output up until the advice request time. The program according to claim 1.

3. The output data is In a game, a method of dealing with a problem at the time when the advice is requested is displayed in text or an image. The program according to claim 1.

4. a storage means for storing actions of a user-controllable character in a virtual space as a play log; a generation means for generating output data indicating advice on a play content related to the play log when the play log is newly stored, based on a trained model trained on training data including at least the play log; A system comprising:

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