Information processing device, server device, information processing system, and list image generation method

Abstract

An action information provision unit 122 provides a server device 5 with action information indicating operations performed by a user on a plurality of content items. An order acquisition unit 126 acquires the order of content items that are likely to be deleted by the user or the order of content items that are unlikely to be deleted by the user. A list image generation unit 128 generates a list image in which information items indicating two or more content items are arranged according to the acquired order of content items.

Description

Information processing device, server device, information processing system, and list image generating method

[0001] The present disclosure relates to a technique for displaying information indicating content, such as content names, side by side.

[0002] In recent years, the data volume of video games has increased as the quality of graphics and sound has improved, and the free space on storage devices has become tight. When a user purchases a video game from a content sales site and tries to play it, if there is not enough free space on the storage device, the video game cannot be installed.

[0003] In such cases, the user needs to delete (uninstall) installed content such as video games and applications to free up sufficient storage space on the recording device. However, if there is a large number of installed content items, it takes time for the user to select the content items to delete, and the user is unable to start the purchased video games promptly.

[0004] Therefore, an object of the present disclosure is to provide a technique for presenting a user with options for content to be deleted from a recording device.

[0005] An information processing device according to one aspect of the present disclosure includes a behavioral information providing unit that provides a server device with behavioral information indicating operations performed by a user on multiple pieces of content, an order acquisition unit that acquires from the server device an order of content that the user is likely to delete or an order of content that the user is unlikely to delete, and a list image generation unit that generates a list image that arranges information indicating two or more pieces of content according to the acquired order of the content.

[0006] A server device according to another aspect of the present disclosure includes a behavioral information acquisition unit that acquires behavioral information indicating operations performed by a user on multiple pieces of content from the user's information processing device, an order determination unit that determines, based on the behavioral information, an order of content that the user is likely to delete or an order of content that the user is unlikely to delete, and an order provision unit that provides the determined order of content to the user's information processing device.

[0007] An information processing system according to yet another aspect of the present disclosure includes a behavioral information acquisition unit that acquires behavioral information indicating operations performed by a user on multiple pieces of content, an order determination unit that determines, based on the behavioral information, an order of content that the user is likely to delete or an order of content that the user is unlikely to delete, and a list image generation unit that generates a list image that arranges information indicating two or more pieces of content according to the determined order of the content.

[0008] A list image generation method of yet another aspect of the present disclosure includes the steps of providing a server device with behavioral information indicating operations performed by a user on multiple pieces of content, obtaining from the server device an order of content that the user is likely to delete or an order of content that the user is unlikely to delete, and generating a list image in which information indicating two or more pieces of content is arranged according to the obtained order of the content.

[0009] Any combination of the above components, and conversion of the present disclosure into a method, device, system, recording medium, computer program, etc., are also valid aspects of the present disclosure.

[0010] 1 is a diagram illustrating a configuration of an information processing system according to an embodiment; 2 is a diagram illustrating a hardware configuration of an information processing device; 3 is a diagram illustrating an example of functional blocks of an information processing device; 4 is a diagram illustrating an example of a home screen; 5 is a diagram illustrating functional blocks of a server device; 6 is a diagram illustrating an example of a list screen; 7 is a diagram illustrating an example of a sorting criteria selection window; 8 is a diagram illustrating an example of a list screen; 9 is a diagram illustrating an example of a sorting criteria selection window; 10 is a diagram illustrating an example of a list screen;

[0011] FIG. 1 illustrates an information processing system 1 according to an embodiment of the present disclosure. The information processing system 1 includes an information processing device 10 operated by a user and a server device 5 managed by an operator of the information processing system 1. An access point (hereinafter referred to as "AP") 8 functions as both a wireless access point and a router, and the information processing device 10 connects to the AP 8 wirelessly or via a wired connection to communicate with the server device 5 on a network 3. While FIG. 1 illustrates one information processing device 10 operated by a single user, the information processing system 1 of the embodiment is premised on multiple information processing devices 10 operated by multiple users being connected to the server device 5 via the network 3. Note that one information processing device 10 may be shared by multiple users, allowing multiple users to operate one information processing device 10.

[0012] The information processing device 10 is connected wirelessly or wired to an input device 6 operated by a user, and the input device 6 outputs information operated by the user to the information processing device 10. When the information processing device 10 receives operation information from the input device 6, it reflects the information in the processing of system software and game software (game data) and outputs the processing results from the output device 4. In the information processing system 1, the information processing device 10 is a game device (game console) that executes a video game, and the input device 6 may be a device such as a game controller that supplies user operation information to the information processing device 10. Note that the input device 6 may also be an input interface such as a keyboard or a mouse. The information processing device 10 has a function for executing a video game, and may also have a function for executing applications other than video games, such as a video distribution app.

[0013] The auxiliary storage device 2 is a large-capacity storage device such as an HDD (hard disk drive) or an SSD (solid state drive), and may be a built-in storage device or an external storage device connected to the information processing device 10 via a USB (universal serial bus) or the like. There may be multiple auxiliary storage devices 2, and built-in storage devices and external storage devices may coexist.

[0014] A user records (installs) content such as video games and applications on the auxiliary storage device 2 and, as necessary, deletes (uninstalls) the content from the auxiliary storage device 2. For example, when it is necessary to increase the free space on the auxiliary storage device 2, the user selects one or more pieces of content to be deleted from the installed content and deletes the selected content from the auxiliary storage device 2.

[0015] The output device 4 may be a television having a display for outputting images and a speaker for outputting sounds. The output device 4 may be connected to the information processing device 10 by a wired cable or wirelessly. The camera 7, which is an imaging device, is provided near the output device 4 and captures images of the space around the output device 4. While FIG. 1 shows an example in which the camera 7 is attached to the top of the output device 4, it may be disposed on the side or bottom of the output device 4, and in either case, it is disposed in a position where it can capture an image of a user positioned in front of the output device 4. The camera 7 may be a stereo camera.

[0016] In the information processing system 1, the server device 5 provides a network service to multiple users. The server device 5 manages network accounts (user accounts) that identify each user, and each user signs in to the network service provided by the server device 5 using their network account. By signing in to the network service from their information processing device 10, users can register game save data and trophies, which are virtual rewards acquired during game play, on the server device 5. By registering the save data and trophies on the server device 5, it becomes possible to synchronize the save data and trophies even if the user uses an information processing device other than the information processing device 10.

[0017] The server device 5 of the embodiment acquires behavioral information indicating operations performed by multiple users on content from multiple information processing devices 10. The content may be various types such as a video game, an application, save data, or user-generated content (UGC) including screenshots and gameplay videos, but the following describes the "behavioral information" provided from the information processing device 10 to the server device 5 when the content is a video game (also simply referred to as a "game").

[0018] When a user installs a game, the information processing device 10 transmits installation information indicating that the game has been installed to the server device 5, along with information identifying the user's network account and the game (game ID). The installation information includes information indicating the date and time the game was installed. In the embodiment, "date and time" refers to the year, month, day, and time.

[0019] When a user launches an installed game, the information processing device 10 transmits to the server device 5 game launch information indicating that the game has been launched, along with the user's network account and game ID, and when the user ends the game, the information processing device 10 transmits to the server device 5 game end information indicating that the game has been ended, along with the user's network account and game ID. The game launch information includes information indicating the date and time the game was launched, and the game end information includes information indicating the date and time the game was ended. The game launch information may be transmitted to the server device 5 at the timing when the user launches the game, and the game end information may be transmitted to the server device 5 at the timing when the user ends the game.

[0020] When the user uninstalls the game, the information processing device 10 transmits uninstallation information indicating that the game has been uninstalled, together with the user's network account and game ID, to the server device 5. The uninstallation information includes information indicating the date and time when the game was uninstalled.

[0021] The server device 5 acquires installation information, game launch information, game termination information, and uninstallation information as behavioral information indicating operations performed by users on a game. When the server device 5 acquires behavioral information from multiple users, it records the behavioral information by linking it to the network account (user account) of each of the multiple users, and manages each user's behavioral history for the game for each game. If a game has not been uninstalled, the server device 5 does not acquire uninstallation information for that game, and therefore, naturally, the behavioral history for that game does not include uninstallation information.

[0022] The server device 5 learns behavioral information collected from multiple users and generates a machine learning model that extracts regularities (characteristics) of behavioral patterns in which multiple users have uninstalled games in the past. To this end, the server device 5 reads behavioral histories, including uninstallation information, from the behavioral information of the multiple users and uses the read behavioral histories as learning data to train the machine learning model. In this manner, the server device 5 of the embodiment may analyze user behavior from the time a user installs a game until the time the user uninstalls it, and extract regularities of behavioral patterns from the time the user uninstalls it. The trained machine learning model may be trained so that, when a user's behavioral history for a game that has not been uninstalled is input, it outputs a score value indicating the degree of likelihood (probability) that the user will uninstall the game.

[0023] 2 shows the hardware configuration of the information processing device 10. The information processing device 10 includes a main power button 20, a power ON LED 21, a standby LED 22, a system controller 24, a clock 26, a device controller 30, a media drive 32, a USB module 34, a flash memory 36, a wireless communication module 38, a wired communication module 40, a subsystem 50, and a main system 60.

[0024] The main system 60 includes a main CPU (Central Processing Unit), a memory and memory controller serving as a main storage device, a GPU (Graphics Processing Unit), etc. The GPU is primarily used for processing game programs. The main CPU has the function of starting up system software and executing content such as games and applications installed on the auxiliary storage device 2 in the environment provided by the system software. The subsystem 50 includes a sub-CPU, a memory and memory controller serving as a main storage device, etc., but does not include a GPU.

[0025] While the main CPU has the function of executing content installed in the auxiliary storage device 2, the sub-CPU does not have such a function. However, the sub-CPU does have the function of accessing the auxiliary storage device 2 and the function of sending and receiving data to and from the server device 5. The sub-CPU is configured with only these limited processing functions, and therefore can operate with less power consumption than the main CPU. These functions of the sub-CPU are executed when the main CPU is in standby mode and the information processing device 10 is operating in power saving mode.

[0026] The main power button 20 is an input unit that receives operational input from the user, is provided on the front of the housing of the information processing device 10, and is operated to turn on or off the power supply to the main system 60 of the information processing device 10. The power ON LED 21 lights up when the main power button 20 is turned on, and the standby LED 22 lights up when the main power button 20 is turned off. The system controller 24 detects that the main power button 20 has been pressed by the user.

[0027] The clock 26 is a real-time clock that generates current date and time information and supplies it to the system controller 24 , the subsystem 50 , and the main system 60 .

[0028] The device controller 30 is configured as an LSI (Large-Scale Integrated Circuit) that, like a southbridge, transfers information between devices. As shown in the figure, devices such as the system controller 24, media drive 32, USB module 34, flash memory 36, wireless communication module 38, wired communication module 40, subsystem 50, and main system 60 are connected to the device controller 30. The device controller 30 absorbs differences in the electrical characteristics and data transfer speeds of each device and controls the timing of data transfer.

[0029] The media drive 32 is a drive device that is driven by loading a ROM medium 44 on which software such as games and license information are recorded, and that reads programs, data, and the like from the ROM medium 44. The ROM medium 44 is a read-only recording medium such as an optical disk, a magneto-optical disk, or a Blu-ray disk.

[0030] The USB module 34 is a module that connects to external devices via a USB cable. The USB module 34 may also be connected to the auxiliary storage device 2 and the camera 7 via USB cables. The flash memory 36 is an auxiliary storage device that constitutes internal storage. The wireless communication module 38 wirelessly communicates with the input device 6 using a communication protocol such as Bluetooth (registered trademark) or IEEE802.11. The wired communication module 40 communicates with external devices via a wired connection and connects to the network 3 via the AP 8.

[0031] 3 shows an example of functional blocks of the information processing device 10. The information processing device 10 includes a processing unit 100, a communication unit 102, and a recording device 140. The processing unit 100 includes a reception unit 108, a recording control unit 110, an execution unit 116, an image / sound generation unit 118, a behavioral information acquisition unit 120, a behavioral information provision unit 122, a request unit 124, an order acquisition unit 126, and a list image generation unit 128. The recording control unit 110 includes an installation unit 112 and an uninstallation unit 114. The communication unit 102 has the functions of both the wireless communication module 38 and the wired communication module 40.

[0032] The recording device 140 records game data 150a-150m (hereinafter referred to as "game data 150" unless otherwise specified) for executing multiple games A-M, and behavior history data 152a-152z (hereinafter referred to as "behavior history data 152" unless otherwise specified) including behavioral information indicating operations performed by users for each of the multiple games A-Z. When a single user uses the information processing device 10, the behavior history data 152 includes behavioral information indicating operations performed by that single user. However, when the information processing device 10 is shared by multiple users and the multiple users play the same game, the behavior history data 152 may include behavioral information indicating operations performed by the multiple users. The game data 150 includes at least a game program, image data, and sound data, constituting game software. In addition to the game data 150, the recording device 140 may also record application data for executing applications other than games, although this is not shown here. The recording device 140 may be an auxiliary storage device 2.

[0033] In the embodiment, the behavior history data 152a-152m are data that record behavior information for currently installed games A-M, while the behavior history data 152n-152z are data that record behavior information for games N-Z that have already been uninstalled. Therefore, the behavior history data 152a-152m do not include uninstallation information, while the behavior history data 152n-152z include uninstallation information. Note that in the embodiment, the recording device 140 stores the behavior history data 152n-152z for uninstalled games, but the behavior history data may be deleted from the recording device 140 when the game data is uninstalled.

[0034] The functionality of the components in the information processing device 10 may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a CPU (a Central Processing Unit), conventional circuits, and / or combinations thereof, configured or programmed to perform the functions described herein. A processor is considered to be circuitry or processing circuitry including transistors and other circuits. A processor may also be a programmed processor that executes a program stored in a memory.

[0035] In this specification, a circuit, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed herein or any hardware known to be programmed to realize or perform the described functions.

[0036] If the hardware is a processor that is considered to be a type of circuitry, the circuitry, means, or unit may be a combination of hardware and software used to configure the hardware and / or processor.

[0037] <Game Installation> When a user purchases a game from a content sales site (not shown) connected to the network 3, the installation unit 112 downloads game data 150 from the content sales site via the communication unit 102 and installs the game data 150 in the recording device 140. When the game is installed, the behavioral information acquisition unit 120 acquires installation information including a game ID, information indicating that the game has been installed, and information indicating the date and time the game was installed. The installation information may be generated by system software. The recording control unit 110 links the installation information to the game ID and records it in the recording device 140 as behavioral history data 152. At this time, the behavioral information provision unit 122 provides the game ID, the user's network account, and the installation information to the server device 5 via the communication unit 102.

[0038] A user can play a game by installing game data 150 in the recording device 140. FIG. 4 shows an example of a home screen displayed on the output device 4. Icons of installed content are arranged horizontally on the home screen. When the user operates the horizontal key on the input device 6 while the home screen is displayed, the row of game icons is moved horizontally. When the user places the game icon 162 of game D in the selection area 160 and operates a predetermined execution button on the input device 6, the execution unit 116 starts and executes the game program of game title D.

[0039] <Game Launch> When the execution unit 116 launches the game of game title D, it outputs game launch information to the behavior information acquisition unit 120, the game launch information including the game ID of game title D, information indicating that the game has been launched, and information indicating the date and time the game was launched. The game launch information may be generated by system software or the game program. When the behavior information acquisition unit 120 acquires the game ID and game launch information, the recording control unit 110 links the game ID of game title D and records the game launch information as behavior history data 152d in the recording device 140. At this time, the behavior information provision unit 122 provides the game ID, the user's network account, and the game launch information to the server device 5 via the communication unit 102.

[0040] When the execution unit 116 starts the game, the game program performs calculations to move the game character in the virtual space based on the user's game operation information received by the reception unit 108. The image and sound generation unit 118 includes a GPU (Graphics Processing Unit) that performs rendering processing and the like, generates game images and game sounds, and outputs the game images and game sounds from the output device 4.

[0041] <Game End> When the execution unit 116 ends the game of game title D, it outputs game end information to the behavior information acquisition unit 120, including information indicating that the game has been ended and information indicating the date and time the game was ended, along with the game ID of game title D. The game end information may be generated by the system software or the game program. When the behavior information acquisition unit 120 acquires the game ID and the game end information, the recording control unit 110 links the game ID of game title D and records the game end information as behavior history data 152d in the recording device 140. At this time, the behavior information provision unit 122 provides the game ID, the user's network account, and the game end information to the server device 5 via the communication unit 102.

[0042] <Game Uninstallation> The user deletes installed game data 150 from the recording device 140 as needed. For example, the user may select and delete an unnecessary game from a list screen listing the titles of installed games. The uninstallation unit 114 uninstalls the game data 150 in accordance with the user's deletion instruction. When the game is uninstalled, the behavioral information acquisition unit 120 acquires uninstallation information including the game ID, information indicating that the game has been uninstalled, and information indicating the date and time the game was uninstalled. The uninstallation information may be generated by system software. The recording control unit 110 links the uninstallation information to the game ID and records it as behavioral history data 152 in the recording device 140. At this time, the behavioral information provision unit 122 provides the game ID, the user's network account, and the uninstallation information to the server device 5 via the communication unit 102.

[0043] In the information processing device 10, the behavior information providing unit 122 provides behavior information indicating operations performed by a user on multiple pieces of content to the server device 5. When transmitting the behavior information, the behavior information providing unit 122 may also transmit device identification information (device ID) that identifies the information processing device 10 together with the user's network account. The behavior information providing unit 122 may transmit the behavior information to the server device 5 in real time each time the behavior information is generated, or may transmit the behavior information periodically. For example, the behavior information providing unit 122 may transmit the accumulated behavior information (behavior information that has not yet been transmitted) when the request unit 124, which will be described later, transmits a sort request to the server device 5.

[0044] 5 shows functional blocks of the server device 5 according to the embodiment. The server device 5 includes a processing unit 200, a communication unit 202, and a recording device 230. The processing unit 200 includes a behavioral information acquisition unit 210, a request acquisition unit 212, an order determination unit 214, a learning unit 216, an order providing unit 218, and a recording control unit 220. The recording device 230 includes a behavioral information recording unit 232 that records behavioral information of multiple users, and a machine learning model 234 that has learned the behavioral information of the multiple users. The communication unit 202 of the server device 5 has a function of transmitting and receiving various information and / or data to and from the communication units 102 of multiple information processing devices 10 connected to the network 3.

[0045] The functionality of the components in the server device 5 may be implemented in circuitry or processing circuitry, including general-purpose processors, application-specific processors, integrated circuits, ASICs (Application Specific Integrated Circuits), a CPU (a Central Processing Unit), conventional circuits, and / or combinations thereof, configured or programmed to perform the functions described herein. A processor is considered to be a circuit or processing circuitry that includes transistors and other circuits. A processor may also be a programmed processor that executes a program stored in a memory.

[0046] In this specification, a circuit, unit, or means is hardware that is programmed to realize or performs the described functions, which may be any hardware disclosed herein or any hardware known to be programmed to realize or perform the described functions.

[0047] If the hardware is a processor that is considered to be a type of circuitry, the circuitry, means, or unit may be a combination of hardware and software used to configure the hardware and / or processor.

[0048] The behavioral information acquisition unit 210 acquires behavioral information indicating operations performed by multiple users on multiple pieces of content from multiple information processing devices 10, and the recording control unit 220 records the behavioral information in the behavioral information recording unit 232. The behavioral information acquisition unit 210 acquires the behavioral information along with a game ID and a user's network account, and the recording control unit 220 records the behavioral information for each game in the behavioral information recording unit 232, linking it to the user's network account. The recording control unit 220 may record the behavioral information in the behavioral information recording unit 232 as a behavior history, linking it to the game ID. By managing the behavioral information for each user, the server device 5 can recognize games that the user can play (installed games) and games that the user has uninstalled. Note that the recording control unit 220 may record the behavioral information for each game in the behavioral information recording unit 232, linking it to the device ID of the information processing device 10 in addition to the user's network account. Alternatively, the recording control unit 220 may record the behavioral information for each game in the behavioral information recording unit 232, linking it to the device ID of the information processing device 10.

[0049] In an embodiment, the learning unit 216 trains the machine learning model 234 using behavioral information of multiple users recorded in the behavioral information recording unit 232. The learning unit 216 may periodically train the machine learning model 234, and when a predetermined period (e.g., one week) has passed since the previous training, the learning unit 216 may train the machine learning model 234 using behavioral information newly collected during that period.

[0050] The machine learning model 234 may be trained to output a score value indicating the degree (probability) of the likelihood that a user will uninstall (delete) a game when behavioral information indicating operations performed by the user on a game is input. Therefore, behavioral information indicating operations performed on a game that has not yet been uninstalled, i.e., an installed game, is input to the trained machine learning model 234. The score value may be expressed on a scale of 100 points. In this case, the closer the score value is to 100 points, the more likely the user is to uninstall the game, and the closer the score value is to 0 points, the less likely the user is to uninstall the game. The score value may be set within a range of 0 to 1, as long as it can express the degree (high or low) of the likelihood of deletion.

[0051] The learning unit 216 reads out behavioral history including uninstallation information from the behavioral histories of multiple users recorded in the behavioral information recording unit 232, and uses the behavioral history from installation to uninstallation as learning data to train the machine learning model 234. In other words, the machine learning model 234 is trained using the behavioral information (installation information, game launch information, game termination information, uninstallation information) of the game that was finally uninstalled in the behavioral histories of multiple users with respect to games as learning data.

[0052] For example, the learning unit 216 may calculate the following time data from the installation information, game launch information, game end information, and uninstallation information, and use the calculated data as learning data for training the machine learning model 234. Installation time The installation time is calculated as the period from the installation date and time to the uninstallation date and time. Total play time The play time for one session is calculated as the time from the game launch date and time to the game end date and time, and the total play time is calculated by adding up the play time for the number of times the session is played. Non-play time until uninstallation The non-play time until uninstallation is calculated as the period from the last game end date and time to the uninstallation date and time.

[0053] These time data are thought to be factors that influence a user's decision to uninstall a game. For example, (total play time / installation time) constitutes information about a user's play frequency, and it is predicted that the lower the play frequency, the higher the likelihood that the game will be uninstalled, and vice versa. Furthermore, the non-play time before uninstallation indicates the period from the last time the user played the game to the uninstallation. Therefore, it is predicted that the longer the non-play time, the more likely the user is not to resume the game, and therefore the more likely the game will be uninstalled.

[0054] The learning unit 216 may use this time data as learning data to train the machine learning model 234, thereby effectively identifying regularities (characteristics) in the behavioral patterns of multiple users who have uninstalled games in the past. Note that this time data has been described to help understand the learning method used by the learning unit 216, and the learning unit 216 is not necessarily required to use this time data as learning data. The learning unit 216 trains the machine learning model 234 to output a score value indicating the degree (probability) of the possibility that a user will uninstall (delete) a game, based on the installation information, game launch information, game end information, and uninstallation information. The learning unit 216 may generate one machine learning model 234 using behavioral information for all games.

[0055] Furthermore, the learning unit 216 may use the data size of the uninstalled game as learning data to train the machine learning model 234. When the storage device 140 is short on free space, the data size of the game is a factor that influences whether the user decides to uninstall the game. For example, if there are two games that the user has not played for a while, the user tends to delete the game with the larger data size. By training the machine learning model 234 using the data size of the game, the learning unit 216 may effectively extract regularities (characteristics) of behavioral patterns in which multiple users have uninstalled games in the past.

[0056] Furthermore, the learning unit 216 may use the release dates of game patches and DLC (downloadable content) added after the game's launch as learning data to train the machine learning model 234. It is empirically known that even users who have not played for a while will return to the game when a major patch or DLC is implemented. Therefore, the learning unit 216 may effectively extract regularities (features) of behavioral patterns that led multiple users to uninstall the game in the past by training the machine learning model 234 using the implementation dates of patches and DLC.

[0057] Furthermore, the learning unit 216 may use the launch date of a new version of a game series as learning data to train the machine learning model 234. When a new version of a game is launched, it is expected that users will stop playing the old version of the game and switch to the latest version of the game. Therefore, the learning unit 216 may train the machine learning model 234 using the launch date of the new version of the game, thereby effectively extracting regularities (features) of behavioral patterns in which multiple users have uninstalled the game in the past.

[0058] The learning unit 216 may train the machine learning model 234 for each game genre. In this case, the same number of trained machine learning models 234 as the number of game genres are generated. The machine learning model 234 trained for each game genre receives behavioral information for games of the same game genre, and the machine learning model 234 outputs a score value indicating the degree of likelihood that games of that genre will be uninstalled by users. The learning unit 216 may train the machine learning model 234 for each game title. In this case, the same number of trained machine learning models 234 as the number of game titles are generated. The machine learning model 234 trained for each game title receives behavioral information for games of the same title, and the machine learning model 234 outputs a score value indicating the degree of likelihood that the game will be uninstalled by users. By training the machine learning model 234 according to genre and title in this way, it is possible to reflect genre- and title-specific characteristics in the learning results of the machine learning model 234.

[0059] Returning to FIG. 3 , the request unit 124 transmits a sort request to the server device 5, requesting that an order for displaying multiple pieces of content be provided. The sort request includes the user's network account. In consideration of the load on the server device 5, the request unit 124 can transmit a sort request up to a predetermined number of times within a predetermined period. For example, the request unit 124 can transmit a sort request up to once a day. Once the request unit 124 transmits a sort request, the request unit 124 may be managed so that it cannot transmit another sort request within 24 hours.

[0060] In the server device 5, the request acquisition unit 212 acquires a sort request and supplies it to the order determination unit 214. The order determination unit 214 acquires the sort request as a request to sort multiple pieces of content owned by a user in order of likelihood that the user will delete them. The order determination unit 214 reads out behavioral information recorded in the behavioral information recording unit 232, which is linked to the user's network account. For example, if game data for games A to M is installed in the user's recording device 140, the order determination unit 214 reads out behavioral information for games A to M linked to the user's network account from the behavioral information recording unit 232. In this case, the order determination unit 214 may simply read out behavioral information for games A to M that does not include uninstallation information from the behavioral information recording unit 232, among the user's behavioral information.

[0061] The order determination unit 214 inputs behavioral information for each game to the trained machine learning model 234 and obtains a score value output from the machine learning model 234. First, the order determination unit 214 inputs behavioral information for game A to the trained machine learning model 234 and obtains a score value for game A output from the machine learning model 234. As described above, the score value indicates the degree of likelihood that this user will delete game A. Next, the order determination unit 214 inputs behavioral information for game B to the trained machine learning model 234 and obtains a score value for game B output from the machine learning model 234. Thereafter, the order determination unit 214 similarly inputs behavioral information for games C to M to the machine learning model 234 and obtains the score values ​​for games C to M output from the machine learning model 234.

[0062] Below are examples of score values ​​output by the machine learning model 234 for games A to M. Game A 95 Game B 68 Game C 10 Game D 5 Game E 38 Game F 48 Game G 15 Game H 24 Game I 84 Game J 32 Game K 8 Game L 12 Game M 71 Here, a high score value means that the user is likely to uninstall the game, and a low score value means that the user is unlikely to uninstall the game. Therefore, the above example shows that the user is most likely to uninstall game A and least likely to uninstall game D.

[0063] The order determination unit 214 rearranges games A to M in order of likelihood that the user will delete them, as follows: Game A 95 Game I 84 Game M 71 Game B 68 Game F 48 Game E 38 Game J 32 Game H 24 Game G 15 Game L 12 Game C 10 Game K 8 Game D 5 The order determination unit 214 determines that the order of games that the user is likely to delete is game A, game I, game M, game B, game F, game E, game J, game H, game G, game L, game C, game K, and game D. The order providing unit 218 provides the order of games determined by the order determination unit 214 to the user's information processing device 10 via the communication unit 202.

[0064] In the information processing device 10, the order acquisition unit 126 acquires, from the server device 5, the order of games that are likely to be deleted by the user. The order acquisition unit 126 records the acquired order in the order recording unit 154. This game order acquisition process may be performed while the information processing device 10 is in a power saving mode. When performed in the power saving mode, the functions of the request unit 124 and the order acquisition unit 126 may be realized by a sub-CPU in the subsystem 50.

[0065] For example, a user can display a list screen of games installed on the recording device 140 on the output device 4, for the purpose of managing content recorded on the recording device 140. FIG. 6 shows an example of a list screen displaying information indicating two or more pieces of content. The list image generation unit 128 generates a list image displaying information indicating multiple installed games (e.g., game titles) from top to bottom according to predetermined sorting criteria, and displays the list image on the output device 4. In this example, the list image generation unit 128 generates a list image displaying game titles in descending order of most recent play date and time. The list image generation unit 128 references the action history data 152a-152m of games A-M to identify the last game completion date and time for each game, thereby sorting games A-M in descending order of most recent play date and time. When the user presses the sort button 260 on this list screen, options indicating possible sorting criteria for sorting are displayed.

[0066] The user can select the game they want to delete on the game list screen. For example, if the storage device 140 is running out of free space and a newly purchased game cannot be installed, the user may want to quickly delete the game from the game list screen to free up space for the purchased game. However, the list screen shown in Figure 6 does not allow users to easily select the game to delete, so it can take time to find the game to delete.

[0067] In the embodiment, the information processing device 10 obtains in advance from the server device 5 the order of contents that the user is likely to delete, and records the order in the order recording unit 154. Then, the user can change the sorting criteria to display on the output device 4 a list screen that makes it easy to select items to delete.

[0068] 7 shows an example of a sorting criteria selection window 262. When the user operates the input device 6 to press the sort button 260, the sorting criteria selection window 262 is displayed. A plurality of sorting criteria options are displayed in the sorting criteria selection window 262. In the example shown in FIG. 7, a selection frame 264 is placed in the display area for "play date and time (newest first)." The user operates the input device 6 to place the selection frame 264 in the display area for "play frequency (lowest first)."

[0069] 8 shows an example of the sort criteria selection window 262 in which the selection frame 264 has been moved. The selection frame 264 is located in the display area for "Play Frequency (Least to Most)." When the user presses the predetermined confirm button on the input device 6 in this state, the list image generation unit 128 generates a list image in which two or more game titles are arranged from top to bottom in the order recorded in the order recording unit 154, i.e., in the order of content that the user is most likely to delete, and displays the list image on the output device 4 as a list screen.

[0070] 9 shows an example of a list screen listing information indicating two or more pieces of content. The list image generation unit 128 generates a list image listing information indicating multiple installed games in order of likelihood of deletion by the user, and displays the list image on the output device 4. By looking at this list screen, the user can recognize games A and I, which are placed at the top of the list, as candidates for deletion. This allows the user to easily select games to uninstall, reducing the time required to select games to delete.

[0071] Note that the order acquisition unit 126 acquires the order of content that the user is likely to delete from the server device 5, for example, only once a day. Therefore, the user may play one of the games after the acquired order of content is recorded in the order recording unit 154. Therefore, the list image generation unit 128 may correct the order of content acquired by the order acquisition unit 126 based on the behavioral information recorded in the recording device 140, and generate a list image according to the corrected order of content. Specifically, the list image generation unit 128 may correct the order of the acquired content based on the behavioral information recorded in the recording device 140 after the order acquisition unit 126 acquires the order of the content. For example, if the user plays game I, the order acquisition unit 126 may modify the order of game I to lower the order, making it less likely that the user will select game I as a deletion candidate.

[0072] The present disclosure has been described above based on an embodiment. This embodiment is an example, and it will be understood by those skilled in the art that various modifications are possible in the combination of each component and each processing process, and that such modifications are also within the scope of the present disclosure. In the embodiment, the order determination unit 214 determines the order of content that the user is likely to delete, but it may also determine the order of content that the user is unlikely to delete. In this case, the order determination unit 214 may determine the order of content that the user is unlikely to delete by arranging games A to M in descending order of score value.

[0073] In the embodiment, it has been described that when behavioral information indicating operations performed by one user on one game is input to the machine learning model 234, the machine learning model 234 outputs a score value indicating the degree of possibility (probability) that the one user will uninstall (delete) the one game. In another example, the machine learning model 234 may be trained to output a score value indicating the degree of possibility (probability) that the one game will be uninstalled (deleted) on the information processing device 10 when behavioral information indicating operations performed by multiple users who share one information processing device 10 on one game is input. For example, in a case where one information processing device 10 is shared by a family consisting of multiple members, when behavioral information indicating operations performed on one game is input collectively for each family member to the machine learning model 234, the machine learning model 234 may output a score value indicating the degree of possibility that the game will be uninstalled on the information processing device 10. In this case, in the server device 5, the behavioral information recording unit 232 records behavioral information for each game, linking it to device identification information (device ID) that identifies the information processing device 10, and the learning unit 216 reads out behavioral history including uninstallation information from the behavioral history linked to multiple device IDs recorded in the behavioral information recording unit 232, and uses the behavioral history from installation to uninstallation as learning data to train the machine learning model 234.

[0074] In the embodiment, the learning unit 216 trains a machine learning model by using behavioral history for uninstalled content as learning data indicating a behavioral pattern of uninstallation. In a modified example, the learning unit 216 may use behavioral history for non-uninstalled content as learning data indicating a behavioral pattern of not uninstalling for machine learning. Even in this case, the learning unit 216 may learn behavioral information collected from multiple users and generate a machine learning model that extracts regularities in behavioral patterns of uninstallation.

[0075] In an embodiment, a machine learning model is trained to, when inputting behavioral information indicating an operation performed by a single user on a single piece of content, output a score value indicating the degree of likelihood that the single user will delete the single piece of content. In a variant example, a machine learning model may be trained to, when inputting behavioral information indicating an operation performed by a single user on multiple pieces of content, output a result in which the multiple pieces of content are sorted in order of likelihood that the user will uninstall them. Note that, when inputting behavioral information indicating an operation performed by a single user on multiple pieces of content, the machine learning model may be trained to, when inputting behavioral information indicating an operation performed by a single user on multiple pieces of content, output a result in which the multiple pieces of content are sorted in order of likelihood that the user will uninstall them.

[0076] In the embodiment, the server device 5 includes the learning unit 216 and trains the machine learning model 234. However, the information processing device 10 may also include a learning unit and train the machine learning model. Alternatively, the server device 5 may include a learning function, and the learning results may be provided to the information processing device 10, which may then configure a machine learning model based on the learning results and determine the order of the content. The function of determining the order of the content may be implemented by the server device 5 alone, or by the server device 5 and the information processing device 10 working together, or by the information processing device 10 alone. In the embodiment, it is assumed that the information processing system 1 includes multiple information processing devices 10 and the server device 5. However, if the information processing device 10 has some of the functions of the server device 5, the information processing system 1 may be implemented by the information processing device 10 alone. Furthermore, the functions of the information processing device 10 described in the embodiment may be implemented in a terminal device (information processing device) such as a smartphone or tablet.

[0077] The present disclosure may include the following aspects. [Item 1] An information processing device operated by a user, comprising: a circuit configured to: provide behavioral information indicating operations performed by the user on multiple pieces of content to a server device; obtain from the server device an order of content that the user is likely to delete or an order of content that the user is unlikely to delete; and generate a list image in which information indicating two or more pieces of content is arranged in accordance with the obtained order of content. [Item 2] The information processing device according to Item 1, wherein the circuit obtains the order of content from the server device up to a predetermined number of times within a predetermined period. [Item 3] The information processing device according to Item 1, further comprising: a recording device that records behavioral information indicating operations performed by the user on content, wherein the circuit corrects the order of the obtained pieces of content based on the behavioral information recorded in the recording device; and generates a list image in accordance with the corrected order of the content. [Item 4] The information processing device according to Item 3, wherein the circuit corrects the order of the obtained pieces of content based on the behavioral information recorded in the recording device after obtaining the order of the content. [Item 5] A server device comprising a circuit configured to: acquire, from a user's information processing device, behavioral information indicating operations performed by a user on multiple pieces of content; determine, based on the behavioral information, an order of content that the user is likely to delete or an order of content that the user is unlikely to delete; and provide the determined order of content to the user's information processing device. [Item 6] The server device according to Item 5, wherein, when behavioral information about one piece of content is input, the circuit determines the order of content using a machine learning model that has been trained to output a score value indicating the degree of likelihood that the user will delete the piece of content.[Item 7] The server device according to Item 6, wherein the circuit determines the order of the content based on the score values ​​output by the machine learning model for the multiple content items. [Item 8] An information processing system, comprising: a circuit configured to: acquire behavioral information indicating operations performed by a user on multiple content items; determine, based on the behavioral information, an order of content items that the user is likely to delete or an order of content items that the user is unlikely to delete; and generate a list image in which information indicating two or more content items is arranged in accordance with the determined order of content items. [Item 9] A method for generating a list image, comprising: providing a server device with behavioral information indicating operations performed by a user on multiple content items; acquiring from the server device the order of content items that the user is likely to delete or the order of content items that the user is unlikely to delete; and generating a list image in which information indicating two or more content items is arranged in accordance with the acquired order of content items. [Item 10] A recording medium having recorded thereon a program executed on a computer of an information processing device, the program causing the computer to realize the following functions: providing a server device with behavioral information indicating operations performed by a user on multiple pieces of content; obtaining from the server device an order of content that the user is likely to delete or an order of content that the user is unlikely to delete; and generating a list image in which information indicating two or more pieces of content is arranged according to the obtained order of the content.

[0078] The present disclosure can be used in the technical field of displaying information indicating content side by side.

[0079] 1...information processing system, 4...output device, 5...server device, 10...information processing device, 100...processing unit, 102...communication unit, 108...reception unit, 110...recording control unit, 112...installation unit, 114...uninstallation unit, 116...execution unit, 118...image and sound generation unit, 120...behavioral information acquisition unit, 122...behavioral information provision unit, 124...request unit, 126...order acquisition unit, 128...list image generation unit, 140...recording device, 154...order recording unit, 200...processing unit, 202...communication unit, 210...behavioral information acquisition unit, 212...request acquisition unit, 214...order determination unit, 216...learning unit, 218...order provision unit, 220...recording control unit, 230...recording device, 232...behavioral information recording unit, 234...machine learning model

Claims

1. An information processing device operated by a user, comprising: a behavioral information providing unit that provides a server device with behavioral information indicating operations performed by the user on multiple pieces of content; an order obtaining unit that obtains from the server device an order of content that the user is likely to delete or an order of content that the user is unlikely to delete; and a list image generating unit that generates a list image that arranges information indicating two or more pieces of content in accordance with the obtained order of the content.

2. The information processing device according to claim 1, wherein the order acquisition unit acquires the order of the content from the server device up to a predetermined number of times within a predetermined period.

3. An information processing device as described in claim 1, further comprising a recording device that records behavioral information indicating operations performed by a user on content, and wherein the list image generation unit corrects the order of the acquired content based on the behavioral information recorded in the recording device and generates a list image according to the corrected order of the content.

4. The information processing device according to claim 3, characterized in that the list image generation unit corrects the order of the acquired content based on the behavioral information recorded in the recording device after the order acquisition unit acquired the order of the content.

5. A server device comprising: a behavioral information acquisition unit that acquires behavioral information indicating operations performed by a user on multiple pieces of content from the user's information processing device; an order determination unit that determines, based on the behavioral information, an order of content that the user is likely to delete or an order of content that the user is unlikely to delete; and an order provision unit that provides the determined order of content to the user's information processing device.

6. The server device according to claim 5, characterized in that the order determination unit determines the order of content using a machine learning model that has been trained to output a score value indicating the degree of likelihood that a user will delete a piece of content when behavioral information about the piece of content is input.

7. The server device according to claim 6, characterized in that the order determination unit determines the order of the content based on the score values output by the machine learning model for the plurality of content items.

8. An information processing system comprising: a behavioral information acquisition unit that acquires behavioral information indicating operations performed by a user on multiple pieces of content; an order determination unit that determines, based on the behavioral information, an order of content that the user is likely to delete or an order of content that the user is unlikely to delete; and a list image generation unit that generates a list image in which information indicating two or more pieces of content is arranged in accordance with the determined order of the content.

9. A list image generation method comprising the steps of: providing a server device with behavioral information indicating operations performed by a user on multiple pieces of content; obtaining from the server device an order of content that the user is likely to delete or an order of content that the user is unlikely to delete; and generating a list image in which information indicating two or more pieces of content is arranged in accordance with the obtained order of the content.

10. A program for enabling a computer to perform the following functions: provide a server device with behavioral information indicating operations performed by a user on multiple pieces of content; obtain from the server device the order of content that the user is likely to delete or the order of content that the user is unlikely to delete; and generate a list image that lists information indicating two or more pieces of content according to the order of the obtained content.

Images