An information processing device (10) includes a learning unit (16b) that learns a situation at the time of operation by a user on a game application (corresponding to an example of an “application”) by machine learning, and a gameplay assistance unit (16c) (corresponding to an example of an “assistance unit”) that executes, across one or more of the game applications, operation assistance processing of performing auto-play (corresponding to an example of “automatic operation”) of the game application by using a learning result of the learning unit (16b).

FIELD

BACKGROUND

In recent years, the spread of portable information processing devices such as smartphones and tablet terminals is remarkable. In addition, in these information processing devices, performance of image processing and communication processing is also rapidly improved, and a user can easily enjoy various kinds of game application software (hereinafter, referred to as “game application”) such as, for example, a third-person shooter (TPS) and a massively multiplayer online role-playing game (MMORPG) by using these information processing devices.

Furthermore, in such a situation, in order to further improve convenience of the user, some game applications have, for example, an auto-play function of automatically executing predetermined game contents set in advance. Low-skill users, busy users, and the like can be assisted in their own gameplay by using such an auto-play function. Similarly, a technology of assisting team organization and strategy in a team environment such as the MMORPG has also been proposed (see, for example, Patent Literature 1).

CITATION LIST

Patent Literature

SUMMARY

Technical Problem

However, the above-described conventional technology has room for further improvement in improving convenience in gameplay of the user regardless of the game application.

For example, in a case where the above-described conventional technology is used, only a user who uses a specific game application having an auto-play function can be assisted in gameplay.

Note that such a problem is also a common problem in a case where a user operates an application that is other than a game application and that has or does not have an automatic operation function corresponding to the auto-play function.

Thus, the present disclosure proposes an information processing device, an information processing method, and a program capable of improving convenience in application operation by the user regardless of the application.

Solution to Problem

In order to solve the above problems, one aspect of an information processing device according to the present disclosure includes: a learning unit that learns a situation at a time of operation by a user on an application by machine learning; and an assistance unit that executes, across one or more of the applications, operation assistance processing of automatically operating the application by using a learning result of the learning unit.

DESCRIPTION OF EMBODIMENTS

In the following, embodiments of the present disclosure will be described in detail on the basis of the drawings. Note that in each of the following embodiments, overlapped description is omitted by assignment of the same reference sign to the same parts.

Furthermore, in the following, a case where an application operated by a user is a game application will be described as a main example.

Furthermore, the present disclosure will be described in the following order of items.1. Outline2. Configuration of an information processing device3. Setting example of auto-play3-1. Auto-play3-2. Semi-auto-play4. Example of notification to another information processing device5. Processing sequence of learning algorithm6. Modification example7. Hardware configuration8. Conclusion

FIG.1is a schematic explanatory diagram (part1) of an information processing method according to an existing technology.FIG.2is a schematic explanatory diagram (part2) of the information processing method according to the existing technology.FIG.3is a schematic explanatory diagram (part1) of an information processing method according to an embodiment of the present disclosure.FIG.4is a schematic explanatory diagram (part2) of the information processing method according to the embodiment of the present disclosure.

In recent years, a user can easily enjoy various game applications such as a TPS and an MMORPG by using a portable information processing device such as a smartphone or a tablet terminal, and some game applications have an auto-play function as a part of the function.

Low-skill users, busy users, and the like can be assisted in their own gameplay by using such an auto-play function. On the other hand, as a matter of course, a user who uses a game application not having the auto-play function cannot be assisted in the gameplay by the auto-play function.

Specifically, as illustrated inFIG.1, the existing technology is based on a relationship in which the user directly plays one game application on a one-to-one basis. Furthermore, at this time, even when the game application has the auto-play function, the user can only use the auto-play as a part of the function of the game application. Contents of the auto-play usually varies depending on game applications.

Thus, as illustrated inFIG.2, in a case where the user uses a plurality of game applications by using one information processing device, the user needs to perform different operations for game sessions of the game applications respectively with respect to the use of the auto-play in the existing technology.

That is, in the existing technology, obviously, the user cannot use the auto-play for the game application that does not have the auto-play function. Thus, in the example ofFIG.2, the user cannot use the auto-play for a game application #2, and for example, a monotonous level grinding work that is so-called “leveling” also needs to be repeated steadily by his/her own.

On the other hand, although the user can use the auto-play for the game applications having the auto-play function, that is, game applications #1 and #3 in the example ofFIG.2, contents of the auto-play that can be set vary depending on the game applications. For example, there are some game applications in which the above-described level grinding can be performed and those in which the level grinding cannot be performed.

Thus, in the existing technology, even in a case where the user can use the auto-play, there are many cases where it is inconvenient that the user needs to perform different kinds of setting of the auto-play respectively for game sessions or the contents of the auto-play is limited.

Thus, in the information processing method according to the embodiment of the present disclosure, a situation at the time of play by the user on the game application is learned by machine learning, and operation assistance processing of performing the auto-play of the game application by using a learning result of the machine learning is executed across one or more of the game applications.

Specifically, the information processing device according to the embodiment of the present disclosure has a gameplay assistance function. As illustrated inFIG.3, a “gameplay assistance application” that executes such a function is interposed between the game application and the user, and provides an auto-play function of automatically playing a game on behalf of the user.

Furthermore, as illustrated inFIG.4, the gameplay assistance application provides the auto-play function in a form of integrating a plurality of game applications, in other words, across a plurality of game applications. Thus, the gameplay assistance application can also provide the auto-play function for the game application #2 that does not originally have the auto-play function. In addition, the gameplay assistance application can integrally perform setting of the auto-play of the game applications #1 and #3 originally having individual auto-play functions in addition to the game application #2.

For example, as illustrated inFIG.4, the gameplay assistance application sets the auto-play on the basis of an artificial intelligence (AI) model15athat learns a “situation at the time of play” including “mental and physical state” of the user based on vital data, a “surrounding situation” such as a global positioning system (GPS) position and temperature, and the like as needed. The AI model15ais, for example, a deep neural network (DNN) or the like.

That is, in the information processing method according to the embodiment of the present disclosure, information that is related to the situation at the time of play by the user and that is indicated by not only the game application but also what is other than the game application is acquired, and contents of the auto-play is set according to a feature at the time of play of the user which feature is learned by utilization of the information.

For example, the gameplay assistance application replays a video of a play scene played by the user in the past, and proposes contents of the auto-play according to each scene and corresponding to the feature of the user at the time of play to the user. Then, contents of the auto-play are set in a form of being interactive with the user for the proposal. Then, the gameplay assistance application executes the auto-play according to contents of the set auto-play.

A specific example related to setting and execution of the auto-play in the information processing method according to the embodiment of the present disclosure will be described later in description with reference toFIG.6toFIG.10.

Furthermore, in the information processing method according to the embodiment of the present disclosure, as illustrated inFIG.3, it is possible to provide a semi-auto-play function that allows interrupt intervention only when the user intervenes in the gameplay assistance application that plays on behalf of the user. A specific example of the semi-auto-play function will be described later in description with reference toFIG.11.

Furthermore, as illustrated inFIG.4, in the information processing method according to the embodiment of the present disclosure, the gameplay assistance application can notify the other information processing device10that the auto-play function is used with respect to a game application that exchanges information with the other information processing device10via a network N, such as the game application #3. Such a specific example will be described later in description with reference toFIG.12.

In such a manner, in the information processing method according to the embodiment of the present disclosure, the situation at the time of play by the user on the game application is learned by machine learning, and the operation assistance processing of performing the auto-play of the game application by using the learning result of the machine learning is executed across one or more of the game applications.

Thus, according to the information processing method according to the embodiment of the present disclosure, it is possible to improve convenience in gameplay by the user regardless of the game application. Specifically, according to the information processing method according to the embodiment of the present disclosure, regardless of the game application, monotonous work such as level grinding can be automatically performed on behalf of the user, and a burden on the user can be reduced.

Note that as a supplement for the “level grinding”, the monotonous level grinding work that needs to be performed steadily by the user in the existing technology includes two meanings. The first is work of repeatedly laying down a monster or the like and accumulating an experience point in an RPG-type game. In a case of a sport-related game, the work is to repeatedly perform practice and a match and accumulate an experience point.

The second is work of improving an attribute of a character by using the accumulated experience point. In the second case, in a case of the RPG-type game, when the experience points necessary to be a next level are accumulated, “raising the level” automatically occurs due to processing on a game side, and there are many cases where it is not necessary for the user himself/herself to perform the operation for raising the level. On the other hand, although the auto-play function is included in the sport-related game, the experience points are accumulated but are not consumed in many cases while the practice or the match is repeatedly performed in the auto-play function.

Thus, in this case, the user needs to stop the auto-play once and perform operation to improve ability of the character by manual operation. That is, in this case, the user needs to manually perform operation of selecting a specific attribute to be improved from among attributes such as offensive ability, defensive ability, running ability, and dribbling of the character by using the accumulated experience points.

The information processing method according to an embodiment of the present disclosure realizes automatic execution of the monotonous level grinding work on behalf of the user. Hereinafter, a configuration example of the information processing device10to which the information processing method according to the embodiment of the present disclosure is applied will be described more specifically.

2. Configuration of an Information Processing Device

FIG.5is a block diagram illustrating the configuration example of the information processing device10according to the embodiment of the present disclosure. Note that only components necessary for describing features of the embodiment of the present disclosure are illustrated inFIG.5, and description of general components is omitted.

In other words, each of the components illustrated inFIG.5is functionally conceptual, and is not necessarily configured physically in an illustrated manner. For example, a specific form of distribution/integration of each block is not limited to what is illustrated in the drawings, and a whole or part thereof can be functionally or physically distributed/integrated in an arbitrary unit according to various loads and usage conditions.

Furthermore, in the description with reference toFIG.5, description of the already-described components may be simplified or omitted.

The information processing device10is a computer used by the user to use a game application and various other applications, and is, for example, a smartphone or a tablet terminal. Note that the information processing device10may be a personal computer (PC), a wearable device, or a game dedicated machine or the like as when being limited to a game application.

As illustrated inFIG.5, the information processing device10includes a sensor unit11, an input unit12, an output unit13, a communication unit14, a storage unit15, and a control unit16.

The sensor unit11is a group of various sensors, and includes, for example, a camera11a, a vital sensor11b, a GPS sensor11c, and a microphone11d.

The camera11ais, for example, a front camera of a smartphone or the like, and is provided in such a manner as to be able to capture image data from which an expression, a line of sight, a pupillary reaction, and the like of the user playing a game can be detected. The vital sensor11bis a sensor that detects a mental and physical state of the user, is worn by the user, and measures vital data indicating the mental and physical state of the user playing the game, such as a heartbeat, brain waves, a blood oxygen level, and perspiration of the user.

The GPS sensor11cmeasures a GPS position of the user playing the game. The microphone11dcollects utterance of the user playing the game. Note that, needless to say, the sensor unit11may appropriately include various sensors other than those described above, such as an inertial sensor.

The input unit12is an input component to which the user inputs various kinds of operation. Note that the input unit12may be integrated with the output unit13(described later) by a touch panel or the like. Thus, the input unit12may be a software component, and may be a graphical user interface (GUI) for operating a game application, for example.

The output unit13is, for example, a display device that displays visual information, and displays visual information such as a moving image and text related to an entire system or the game application under the control of the control unit16. Examples of the above display device include a liquid crystal display (LCD), an organic light emitting diode (OLED), and the like.

Furthermore, the output unit13is, for example, a sounding device that emits voice information, and emits voice information such as a voice related to the entire system or the game application under the control of the control unit16. Examples of the sounding device include a speaker and the like.

The communication unit14is realized, for example, by a network interface card (NIC) or the like. The communication unit14is connected in a wireless or wired manner to the network N such as the Internet or a mobile phone network, and transmits and receives information to and from the other information processing device10or a game server (not illustrated) via the network N.

The storage unit15is realized by, for example, a semiconductor memory element such as a random access memory (RAM), a read only memory (ROM), or a flash memory. In the example illustrated inFIG.5, the storage unit15stores the AI model15aand application information15b.

The AI model15aalready illustrated inFIG.4is a learning model learned by a learning unit16b(described later). As described above, the AI model15ais a DNN or the like, and learns correlation of elements in a multidimensional feature amount space having elements indicating the contents of the game and the situation at the time of play as dimensions as needed.

Examples of the elements include a game title, an input by the user with respect to the contents of the game being played and an output corresponding thereto, a mental and physical state of the user with respect to the input or the output, a surrounding situation such as a GPS position and a temperature during the play, change operation at the time of erroneous input, and the like.

The application information15bis information including various applications executed by the information processing device10, such as programs of the game applications, various parameters used during execution of the game applications, and the like.

The control unit16is a controller, and is realized, for example, when various programs stored in the storage unit15are executed by a central processing unit (CPU), a micro processing unit (MPU), or the like with a RAM as a work area. Also, the control unit16can be realized by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

The control unit16includes an acquisition unit16a, the learning unit16b, a gameplay assistance unit16c, an application execution unit16d, and a transmission/reception unit16e, and realizes or executes a function and an action of information processing described below.

The acquisition unit16aacquires the above-described situation at the time of the play, which situation includes the mental and physical state of the user and the surrounding situation, via the sensor unit11. Furthermore, the acquisition unit16aoutputs the acquired situation at the time of the play to the learning unit16bas needed. Note that the acquisition unit16acan acquire the situation (such as a temperature, weather, traffic condition, and the like) at the time of the play via the network N not only from the sensor unit11but also from, for example, the transmission/reception unit16e(described later).

The learning unit16blearns the AI model15aon the basis of the situation at the time of the play, which situation is acquired by the acquisition unit16a, and contents of a game which contents are input from the gameplay assistance unit16c(described later). An example of learning algorithm executed by the learning unit16bwill be described later with reference toFIG.13andFIG.14. Note that in a case where the AI model15ais the DNN, the learning unit16blearns the AI model15aby using deep learning.

The gameplay assistance unit16cexecutes gameplay assistance processing for realizing the function of the gameplay assistance application described with reference toFIG.3andFIG.4. That is, the gameplay assistance unit16cprovides the auto-play function in a form in which a plurality of game applications is integrated.

Specifically, the gameplay assistance unit16cproposes contents of the auto-play corresponding to the feature at the time of the play by the user, which contents are output from the AI model15a, to the user with respect to the input situation at the time of the play and contents of the game. Then, the gameplay assistance unit16csets the contents of the auto-play in a form of being interactive with the user who uses the input unit12and the output unit13in response to the proposal. Then, with respect to the application execution unit16d(described later) that executes the game application, the gameplay assistance unit16cexecutes the auto-play in place of the user according to the set contents of the auto-play.

Furthermore, the gameplay assistance unit16cprovides the semi-auto-play function of reflecting the operation of the user only when the user intervenes via the input unit12interruptively with respect to the auto-play that substitutes for the user.

Furthermore, the gameplay assistance unit16cnotifies the other information processing device10, with which the application execution unit16dexchanges information related to the currently-executed game application via the network N, that the auto-play function is used.

Note that the gameplay assistance unit16cexecutes the gameplay assistance processing while residing in the control unit16as, for example, middleware.

On the basis of the application information15b, the application execution unit16dexecutes the game application started by the user. In addition, from the gameplay assistance unit16c, the application execution unit16dreceives an input by the user via the input unit12or an input by the auto-play in place of the user. In addition, the application execution unit16dcauses the output unit13to output the contents of the game, which progresses according to the received input, as an output result via the gameplay assistance unit16c.

In a case where it is necessary to exchange the information related to the game application, which is currently executed by the application execution unit16d, with the other information processing device10, the transmission/reception unit16etransmits and receives the information to and from the other information processing device10via the communication unit14. Furthermore, the transmission/reception unit16ereceives the situation at the time of the play which situation can be acquired via the network N, and causes the acquisition unit16ato perform the acquisition.

3. Setting Example of Auto-Play

Next, a setting example of the auto-play in the information processing according to the embodiment of the present disclosure will be described with reference toFIG.6toFIG.10. First,FIG.6is a view illustrating an example of an on/off setting screen of the auto-play.

In a case where the user sets on/off of the auto-play, the information processing device10displays the on/off setting screen of the auto-play in a manner illustrated inFIG.6, for example. Such a setting screen is displayed by the gameplay assistance unit16c, for example, in a case where the user selects an item corresponding to the auto-play from a system setting screen or the like of the information processing device10.

Then, when the user sets the auto-play to be turned ON on the setting screen, an input to items below “auto-play setting” illustrated inFIG.6is enabled.

Then, the user can select “all game sessions” as an “auto-play target range” among the enabled items. In a case where the “all game sessions” are selected, it becomes possible to “perform AI learning of all gameplay and propose the auto-play in all play scenes” as illustrated inFIG.6.

Note that although not illustrated, any game session may be selected instead of the “all game sessions”.

Furthermore, as illustrated inFIG.6, the user can arbitrarily select each item indicating contents of each auto-play, such as “level grinding in a field” or “searching for an item in a field” for the “auto-play contents”. The gameplay assistance unit16ccan present the items as options on the basis of each of determined scenes as a result of the past AI learning.

Note that an example of the “auto-play contents” that can be set for the MMORPG and the like is illustrated inFIG.6. For example, in a case where the user uses another game application such as a shooting game, a sport game, or the like by using the information processing device10, in other words, in a case where such another game application is installed, an item corresponding to the other game application is displayed together in the “auto-play contents”.

Incidentally, as the situation at the time of the play of the game application, the point of acquiring the vital data of the user, the surrounding situation including the GPS position, the temperature, and the like has already been described. In the information processing according to the embodiment of the present disclosure, it is possible to learn the correlation between the game application and the situation at the time of the play by AI learning using these pieces of data, calculate a matching rate of the auto-play, and set the auto-play based on the matching rate, for example.

An example of such a case will be described with reference toFIG.7andFIG.8.FIG.7is an explanatory diagram (part1) of a setting example of the auto-play corresponding to the situation at the time of the play. Furthermore,FIG.8is an explanatory diagram (part2) of the setting example of the auto-play corresponding to the situation at the time of the play.

As illustrated inFIG.7, the vital data of the user, a GPS position, an ambient temperature, a time period, an environmental sound, and the like are acquired as the situation at the time of the play. There is a case where each of these pieces of data indicates, for example, a situation in which it is difficult for the user to concentrate on play in playing of the game application by himself/herself, such as during commuting, going to school, working, or taking a class, or a situation that is originally not suitable for playing.

Thus, as illustrated inFIG.7, the AI model15amay be learned by utilization of these pieces of data, and in a case where, for example, a real-time situation at the time of the play is input, the AI model15amay output an auto-play matching rate indicating a degree whether the situation is suitable for the auto-play.

Then, as illustrated inFIG.8, in a case where the auto-play matching rate is high, that is, in a situation in which it is difficult for the user to concentrate on the play, in a case of the sport game, for example, a dialogue such as “Do you want to skip the match?” may be displayed, and auto-play setting corresponding to a response to the dialogue may be enabled. As a result, it becomes possible to dynamically set the auto-play according to the real-time situation in which the user is in.

Furthermore, as described above, in the gameplay assistance processing, it is possible to replay a video of a play scene played by the user in the past, and propose contents of the auto-play according to each scene and corresponding to the feature of the user at the time of play to the user. Then, it is possible to interactively set the contents of the auto-play by a form of being interactive with the user for the proposal.

An example of such a case will be described with reference toFIG.9andFIG.10.FIG.9is an explanatory diagram (part1) of an example of interactively setting the auto-play by using a replay of a scene.FIG.10is an explanatory diagram (part2) of the example of interactively setting the auto-play by using the replay of a scene.

As illustrated inFIG.9, in a case where the game application is the TPS, it is assumed that there is a series of flows of scenes of “breaking a wall”, “finding an opponent”, “aiming at the opponent”, and “attacking the opponent” in the play scenes played by the user in the past.

Then, while replaying a video of the series of play scenes, the gameplay assistance unit16cdisplays a dialog asking the user “Do you approve this operation?” about the operation from “aiming at the opponent” to “attacking the opponent” illustrated at a time point T2to a time point T3with a scene of “finding an opponent” at a time point T1as a trigger, for example.

Here, when the user selects “Yes”, the operation from the time point T2to the time point T3is set as the auto-play contents in the series of scenes triggered by the time point T1, and the auto-play of reproducing the play scene played by the user in the past is executed as illustrated inFIG.10.

On the other hand, as illustrated inFIG.9, when the user selects “No” in the dialog asking “Do you approve this operation?”, the gameplay assistance unit16cproposes other operation. In the example ofFIG.9, the other operation is, for example, operation of attacking the opponent with another weapon or operation of not attacking the opponent.

Furthermore, in a case of another trigger other than “finding an opponent”, the gameplay assistance unit16creplays a scene corresponding to the other trigger, presents operation corresponding to the scene, and interactively sets contents of the auto-play.

By repeating such setting and confirmation, for example, in a case of “finding an opponent”, it is possible to set the auto-play contents with high reproducibility corresponding to a feature at the time of the play by the user, such as immediately attacking in a case of a weak opponent, attacking with a different weapon in a case of a strong opponent, escaping in a case of a further strong opponent, and not attacking in a case where the opponent is an ally or the own level is MAX.

Next, a setting example of the above-described semi-auto-play function will be described with reference toFIG.11.FIG.11is an explanatory diagram of the setting example of the semi-auto-play.

The gameplay assistance unit16ccan set the semi-auto-play triggered by, for example, utterance of a predetermined wake word by the user. As illustrated inFIG.11, such a wake word evokes temporarily stopping the auto-play that substitutes for the user, such as “Wait” or “Oh”.

Conversely, as illustrated inFIG.11, the wake word that restarts the auto-play evokes the restart of the auto-play, such as “Good” or “Return”.

For example, when a setting item of the semi-auto-play is provided on the setting screen of the auto-play illustrated inFIG.6and the wake word for starting/canceling the semi-auto-play is registered in the setting item of the semi-auto-play through the microphone11d, the gameplay assistance unit16ccan perform setting of the semi-auto-play.

When setting of the semi-auto-play is performed in such a manner, as illustrated inFIG.11, for example, utterance of the user which utterance indicates a start of the semi-auto-play at the time point T1enables the user to perform intervening operation such as “not aiming at the opponent” or “not attacking the opponent” on the contents of the auto-play illustrated inFIG.10. In addition, it becomes possible to resume the auto-play by utterance by the user which utterance indicates cancellation of the semi-auto-play at the time point T3.

Note that although an example in which the utterance of the predetermined wake word by the user is used as a trigger for starting/canceling the semi-auto-play has been described in the description with reference toFIG.10andFIG.11, this is not a limitation. For example, the trigger of the start/cancellation of the semi-auto-play may be based on vital data of the user. Examples the above include a change in a line of sight, a gesture, a change in brain waves, and the like.

4. Example of Notification to Another Information Processing Device

Next, an example of notification to another information processing device10will be described with reference toFIG.12.FIG.12is an explanatory diagram of the example of the notification to the other information processing device10. Note thatFIG.12is a view illustrating a game screen displayed on the other information processing device10.

As illustrated inFIG.12, in a case where another user confirms the user, who is in the auto-play, from a third party viewpoint by using the other information processing device10via the network N, the gameplay assistance unit16cnotifies the other information processing device10that the user is in the auto-play.

As illustrated inFIG.12, the other information processing device10that receives the notification displays a symbol (“A” indicating “Auto Play” in the example ofFIG.12), an icon, or the like indicating that the auto-play is being performed together with a user name of the user. As a result, in an online game such as the TPS or the MMORPG, it becomes possible for another participant to easily distinguish whether the user is in the auto-play, or to prevent unnecessary talking in chat or the like.

5. Processing Sequence of Learning Algorithm

Next, a processing sequence of the learning algorithm in the information processing device10will be described with reference toFIG.13andFIG.14.FIG.13is a processing sequence (part1) of the learning algorithm in the information processing device10.FIG.14is a processing sequence (part2) of the learning algorithm in the information processing device10.

Note thatFIG.13is a view illustrating a processing sequence of a case where the game application is a sport game, andFIG.14is a view illustrating a processing sequence of a case where the game application is an RPG game.

As illustrated inFIG.13, first, the acquisition unit16aacquires the situation at the time of the play, which situation includes the mental and physical state of the user, as needed (Step S101). Then, by the learning unit16b, the AI model15alearns the correlation of the situation at the time of the play, the contents of the game, the input, and the output as needed (Step S102).

On the other hand, the user checks whether there is an action point while playing the sport game (Step S103). In a case where there is the action point (Step S103, Yes), a match in the game is performed (Step S104).

Then, by the learning unit16b, the AI model15alearns an input start action in Step S104and a difference in a state before and after the start of the input (Step S105). Here, the difference in the state before and after the start of the input indicates a difference in the action point before the match and at the start of the match.

Furthermore, by the learning unit16b, the AI model15alearns an action of stopping the match at the end of Step S104and the difference in the state before and after the stop (Step S106). The difference in the state before and after the stop indicates a difference in a level of a player in the game before and after the match.

Note that in a case where there is no action point (Step S103, No), Step S103is repeated until the action points are accumulated.

Then, after playing the match, the user checks whether there is a player whose level is MAX (Step S107). In a case where there is a player whose level is MAX (Step S107, Yes), the target player is awakened (Step S108).

Furthermore, the user checks whether there is an input mistake with respect to the awakening of the player (Step S109). In a case where there is the input mistake (Step S109, Yes), the input contents are corrected (Step S110).

Then, by the learning unit16b, the AI model15alearns the correction contents in Step S110(Step S111). Note that the learning unit16bcan learn the AI model15arelated to the input mistake by using, for example, the utterance of “Oh” by the user, a change in brain waves, or the like as a trigger.

Then, the user checks whether there is no action point (Step S112). In a case where there is the action point (Step S112, No), the user repeats the action from Step S104.

In a case where there is no action point (Step S112, Yes), the user performs an action point recovery measure (Step S113). The action point recovery measure is, for example, paying money, waiting for time recovery, or the like. Then, the user repeats the action from Step S103.

Furthermore, also in a case where the game application is the RPG game, as illustrated inFIG.14, the acquisition unit16aacquires the situation at the time of the play, which situation includes the mental and physical state of the user, as needed (Step S201). Then, by the learning unit16b, the AI model15alearns the correlation of the situation at the time of the play, the contents of the game, the input, and the output as needed (Step S202).

On the other hand, while playing the RPG game, the user checks whether there are a health point (HP)/magic point (MP)/recovery item (Step S203). In a case where there are these points and items (Step S203, Yes), movement to a place where an enemy that can be hunted at the current level is present and an important item in the current experience point or the current state is present is performed, and the enemy is hunted (Step S204).

Then, by the learning unit16b, the AI model15alearns an input start action in Step S204and a difference in a state before and after the start of the input (Step S205). Here, the state before and after the start of the input indicates the HP/MP/recovery item, level, enemy in the hunting place, item, and the like.

Furthermore, by the learning unit16b, the AI model15alearns the action of stopping the hunting at the end of Step S204and the difference in the state before and after the stop (Step S206). Here, the state before and after the stop indicates, for example, the remaining number of recovery items.

Note that in a case where there is no HP/MP/recovery item (Step S203, No), Step S203is repeated until these are accumulated by time recovery or the like.

Then, after hunting the enemy, the user checks whether there is no recovery item (Step S207). In a case where there is no recovery item (Step S207, Yes), the item is acquired (Step S208). In a case where there is the recovery item (Step S207, No), transition to Step S209is performed.

Furthermore, the user checks whether there is an input error with respect to hunting of the enemy (Step S209). In a case where there is the input mistake (Step S209, Yes), the input contents is corrected (Step S210).

Then, by the learning unit16b, the AI model15alearns the correction contents in Step S210(Step S211). Note that the learning unit16bcan perform learning of the AI model15arelated to the input mistake by using, for example, the utterance of “Oh” by the user, the change in brain waves, or the like as the trigger as described above.

Then, the user checks whether a stage has been cleared (Step S212). The stage indicates, for example, a current hunting place. In a case where the stage is not cleared (Step S212, No), the user repeats the action from Step S204.

In a case where the stage is cleared (Step S212, Yes), the user moves to a next stage (Step S213), and repeats the action from Step S203.

6. Modification Example

Incidentally, there are some modification examples for the above-described embodiment of the present disclosure.

For example, although it is assumed that the AI model15ais the DNN in the embodiment of the present disclosure, the configuration of the AI model15alearned by machine learning is not limited. For example, the AI model15amay be a variational auto encoder (VAE), a generative adversarial network (GAN), or the like. In addition, algorithm other than deep learning may be used as algorithm of machine learning. For example, machine learning may be executed by a regression analysis method such as support vector regression using a pattern identifier such as a support vector machine (SVM) and the AI model15amay be learned. Furthermore, here, the pattern identifier is not limited to the SVM, and may be, for example, AdaBoost. In addition, random forest, deep forest, or the like may be used.

Furthermore, among the pieces of processing described in the above-described embodiment of the present disclosure, a whole or part of the processing described to be automatically performed can be manually performed, or a whole or part of the processing described to be manually performed can be automatically performed by a known method. In addition, the processing procedures, specific names, and information including various kinds of data or parameters in the above document or in the drawings can be arbitrarily changed unless otherwise specified. For example, various kinds of information illustrated in each of the drawings are not limited to the illustrated information.

In addition, each component of each of the illustrated devices is a functional concept, and does not need to be physically configured in the illustrated manner. That is, a specific form of distribution/integration of each device is not limited to what is illustrated in the drawings, and a whole or part thereof can be functionally or physically distributed/integrated in an arbitrary unit according to various loads and usage conditions.

In addition, the above-described embodiments of the present disclosure can be arbitrarily combined in a region in which the processing contents do not contradict each other. Furthermore, the order of steps illustrated in the sequence diagram or the flowchart of the present embodiment can be changed as appropriate.

7. Hardware Configuration

Furthermore, the information processing device10according to the above-described embodiment of the present disclosure is realized by, for example, a computer1000having a configuration in a manner illustrated inFIG.15.FIG.15is a hardware configuration diagram illustrating an example of the computer1000that realizes functions of the information processing device10. The computer1000includes a CPU1100, a RAM1200, a ROM1300, a hard disk drive (HDD)1400, a communication interface1500, and an input/output interface1600. Each unit of the computer1000is connected by a bus1050.

The CPU1100operates on the basis of programs stored in the ROM1300or the HDD1400, and controls each unit. For example, the CPU1100expands the programs, which are stored in the ROM1300or the HDD1400, in the RAM1200and executes processing corresponding to the various programs.

The ROM1300stores a boot program such as a basic input output system (BIOS) executed by the CPU1100during activation of the computer1000, a program that depends on hardware of the computer1000, and the like.

The HDD1400is a computer-readable recording medium that non-temporarily records the programs executed by the CPU1100, data used by the programs, and the like. Specifically, the HDD1400is a recording medium that records a program according to the embodiment of the present disclosure which program is an example of program data1450.

The communication interface1500is an interface with which the computer1000is connected to an external network1550(such as the Internet). For example, the CPU1100receives data from another equipment or transmits data generated by the CPU1100to another equipment via the communication interface1500.

The input/output interface1600is an interface to connect an input/output device1650and the computer1000. For example, the CPU1100receives data from an input device such as a keyboard or mouse via the input/output interface1600. Furthermore, the CPU1100transmits data to an output device such as a display, speaker, or printer via the input/output interface1600. Also, the input/output interface1600may function as a medium interface that reads a program or the like recorded on a predetermined recording medium (medium). The medium is, for example, an optical recording medium such as a digital versatile disc (DVD) or phase change rewritable disk (PD), a magneto-optical recording medium such as a magneto-optical disk (MO), a tape medium, a magnetic recording medium, a semiconductor memory, or the like.

For example, in a case where the computer1000functions as the information processing device10according to the embodiment of the present disclosure, the CPU1100of the computer1000realizes a function of the control unit16by executing the program loaded on the RAM1200. Also, the HDD1400stores a program according to the present disclosure, and data in the storage unit15. Note that the CPU1100reads the program data1450from the HDD1400and performs execution thereof. However, these programs may be acquired from another device via the external network1550in another example.

As described above, according to an embodiment of the present disclosure, the information processing device10includes the learning unit16bthat learns a situation at the time of operation by the user on a game application (corresponding to an example of an “application”) by machine learning, and the gameplay assistance unit16c(corresponding to an example of an “assistance unit”) that executes, across one or more of the game applications, operation assistance processing of performing auto-play (corresponding to an example of “automatic operation”) of the game application by using a learning result of the learning unit16b. As a result, it is possible to improve convenience in the gameplay (corresponding to an example of “application operation”) by the user regardless of the game application.

Although embodiments of the present disclosure have been described above, a technical scope of the present disclosure is not limited to the above-described embodiments as they are, and various modifications can be made within the spirit and scope of the present disclosure. In addition, components of different embodiments and modification examples may be arbitrarily combined.

Also, an effect in each of the embodiments described in the present description is merely an example and is not a limitation, and there may be a different effect.

An information processing device comprising:a learning unit that learns a situation at a time of operation by a user on an application by machine learning; andan assistance unit that executes, across one or more of the applications, operation assistance processing of automatically operating the application by using a learning result of the learning unit.
(2)

The information processing device according to (1), whereinthe application is a game application, andthe situation at the time of operation includes information related to the situation at the time of the play by the user which situation is indicated by what is other than the game application,the learning unitlearns correlation of each of elements of the information related to the situation at the time of play and information related to the game application, andthe assistance unitperforms the automatic operation on behalf of the user by using a learning result of the correlation.
(3)

The information processing device according to (2), whereinthe elements includea game title of the game application, an input by the user during the play, an output in response to the input, a mental and physical state of the user with respect to the input or the output, position information of the user, a temperature around the user, and change operation at a time of an erroneous input.
(4)

The information processing device according to (2) or (3), whereinthe assistance unitsets contents of the automatic operation according to a feature of the user at the time of the play which feature is indicated by a learning result of the correlation.
(5)

The information processing device according to (4), whereinthe assistance unitreplays a play scene of the user, proposes contents of the automatic operation to the user according to the replayed scene and according to a feature at the time of the play, and sets the contents of the automatic operation in a form of being interactive with the user.
(6)

The information processing device according to (4) or (5), whereinthe assistance unitis provided in such a manner that the operation of the user can intervene interruptively during the execution of the automatic operation corresponding to the set contents.
(7)

The information processing device according to (6), whereinthe assistance unitis provided in such a manner that intervention by or cancellation of the operation of the user can be performed with utterance of a predetermined wake word by the user as a trigger.
(8)

The information processing device according to any one of (2) to (7), whereinthe assistance unitnotifies another user other than the user, via a network, that the user is assisted by the operation assistance processing.
(9)

An information processing method comprising:learning a situation at a time of operation by a user on an application by machine learning; andexecuting, across one or more of the applications, operation assistance processing of automatically operating the application by using a learning result of the learning.
(10)

A program causing a computer to realizelearning a situation at a time of operation by a user on an application by machine learning, andexecuting, across one or more of the applications, operation assistance processing of automatically operating the application by using a learning result of the learning.

REFERENCE SIGNS LIST