Server device, skill value derivation method, and grouping method

Abstract

In the present invention, a playing time acquisition unit 212 acquires the playing times of a plurality of users of an activity from event data indicating the start of said activity and event data indicating the end of said activity. A statistical processing unit 220 generates a distribution of playing times for each activity on the basis of a plurality of acquired playing times. A skill value derivation unit 214 derives the skill value of a user with regard to an activity on the basis of the playing time of the user for the activity and the distribution of the playing time for the activity. A classification unit 228 divides a plurality of activities into groups. The skill value derivation unit 214 derives a player skill value pertaining to the user's game play for each activity group on the basis of a plurality of skill values derived with regard to a plurality of activities.

Description

Technical Field

[0001] The present disclosure relates to a technique for processing event data related to game activities executed by a user.

Background Art

[0002] In recent years, cloud game services in which a player transmits operation information input by the player to a game server, and the game server generates game video and game sound according to the operation information and transmits them to the player's terminal device have become widespread. In cloud game services, since the game server manages all game play, it is possible to analyze the play status of activities executed by players and use it for improving game scenarios and the like.

[0003] On the other hand, current console games do not output information indicating the status of game play to the outside, so the server side cannot analyze the play status by the player. Therefore, even in console games, it is preferable to transmit various information related to game play to the server so that the server can analyze the play status of activities.

Summary of the Invention

Problems to be Solved by the Invention

[0004] By constructing an environment in which the server can collect the play status of activities by a plurality of players, the play time of activities by players can be statistically processed. Therefore, it is desired to construct a mechanism for providing useful information regarding play time to users who have not yet executed an activity.

[0005] An object of the present disclosure is to realize a mechanism for providing information regarding the play time of an activity.

Means for Solving the Problems

[0006] In order to solve the above problems, a server device according to an aspect of the present disclosure is a server device that derives a skill value related to a user's game play, and includes an event data recording unit that records event data related to activities executed by a plurality of users, and an event data recording unit that records event data related to activities executed by a plurality of users. A play time acquisition unit that acquires the play time of the activity of a plurality of users from the event data indicating the start of the activity and the event data indicating the end of the activity; and a statistical processing unit that generates a distribution of play times for each activity based on the acquired plurality of play times; and a skill value derivation unit that derives a skill value of a user for the activity based on the play time of the activity by the user and the distribution of the play time of the activity; and a classification unit that groups a plurality of activities. The skill value derivation unit derives a player skill value related to the user's game play for each activity group based on the plurality of skill values derived for the plurality of activities.

[0007] A server device according to another aspect of the present disclosure is a server device that groups a plurality of activities, and includes an event data recording unit that records event data related to activities executed by a plurality of users, and an event data recording unit that records event data indicating the start of an activity and event data indicating the end of an activity. A play time acquisition unit that acquires the play time of the activity of a plurality of users from the event data, a statistical processing unit that generates a distribution of play time for each activity based on the acquired plurality of play times, and a play time of the activity by the user and the play time of the activity. A skill value derivation unit that derives a skill value of the user for the activity based on the distribution, and a classification unit that groups a plurality of activities. The skill value derivation unit derives a player skill value related to the user's game play based on the plurality of skill values derived for the plurality of activities. The classification unit calculates the difference between the skill values of the plurality of users derived for each activity and the player skill value of the plurality of users, and groups the plurality of activities based on the plurality of differences calculated for each activity.

[0008] A server device according to another aspect of the present disclosure is a server device that provides information related to the expected play time to the user, and includes a classification unit that groups a plurality of activities, and a skill value derivation unit that derives a skill value of the user for the activity played by the user. The skill value derivation unit derives a player skill value related to the user's game play for each activity group based on the plurality of skill values derived for the plurality of activities. The server device further includes a class assignment unit that sets a class of the user for each activity group based on the player skill value derived for each activity group, and a notification unit that notifies the user of information related to the expected play time associated with the user's class.

[0009] Another skill value derivation method according to another aspect of the present disclosure is a method for deriving a skill value related to a user's game play, comprising: obtaining play times of the activity for a plurality of users from event data indicating the start of the activity and event data indicating the end of the activity; generating a distribution of play times for each activity based on the obtained plurality of play times; deriving a skill value of the user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity; grouping a plurality of activities; and deriving a player skill value related to the user's game play for each activity group based on the plurality of skill values derived for the plurality of activities.

[0010] Another grouping method according to another aspect of the present disclosure is a method for grouping a plurality of activities, comprising: obtaining play times of the activity for a plurality of users from event data indicating the start of the activity and event data indicating the end of the activity; generating a distribution of play times for each activity based on the obtained plurality of play times; deriving a skill value of the user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity; deriving a player skill value related to the user's game play based on the plurality of skill values derived for the plurality of activities; calculating a difference between the plurality of skill values of the users derived for each activity and the player skill value of the plurality of users; and grouping the plurality of activities based on the plurality of differences calculated for each activity.

[0011] Another aspect of the information providing method of the present disclosure is a method for providing information regarding the predicted play time to a user, including steps of grouping a plurality of activities, deriving a user's skill value for the activities played by the user, deriving a player skill value regarding the user's gameplay for each activity group based on the plurality of skill values derived for the plurality of activities, setting a class of the user for each activity group based on the player skill value derived for each activity group, and notifying the user of information regarding the predicted play time associated with the class of the user.

[0012] Note that any combination of the above components, and those obtained by converting the expression of the present disclosure among a method, an apparatus, a system, a recording medium, a computer program, etc., are also effective as aspects of the present disclosure.

Brief Description of the Drawings

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Embodiments for Carrying Out the Invention

[0014] The summary of the present disclosure will be described. In an embodiment, an information processing apparatus, which is a user terminal device, executes game software. When the game software starts an activity, it outputs event information including an activity identifier (activity ID) that identifies the activity and information indicating the start of the activity to the system software. An activity is a unit of game play and may be a stage, a quest, a mission, etc. provided to the user in the game progress. When the game software ends an activity, it outputs event information including the activity ID and information indicating the end of the activity to the system software. At the end of the activity, the game software may include the result (success or failure) of the activity in the event information. The system software generates event data by adding a game identifier (game ID) that identifies the game and time information (timestamp) to the event information output from the game software, and transmits it to the server device.

[0015] The server device collects event data transmitted from a plurality of information processing apparatuses operated by a plurality of players and analyzes the play trends regarding the activity. In particular, the server device in the embodiment statistically processes the play time of the activity by a plurality of players based on the event data transmitted from the plurality of information processing apparatuses. The server device has a function of notifying a user who has not yet played the activity of the expected play time based on the result of the statistical processing.

[0016] Figure 1 shows an information processing system 1 according to an embodiment of the present disclosure. The information processing system 1 of the embodiment is a play time analysis system that analyzes the play time of an activity, and includes an information processing device 10 operated by a user who is a player and a server device 5. An access point (hereinafter referred to as "AP") 8 has functions of a wireless access point and a router, and the information processing device 10 is connected to the AP 8 via wireless or wired, and is communicably connected to the server device 5 on the network 3. Although Figure 1 shows one user and one information processing device 10, it is assumed in the information processing system 1 that a plurality of information processing devices 10 operated by a plurality of users and the server device 5 are connected via the network 3.

[0017] The information processing device 10 is connected wirelessly or wired to an input device 6 operated by the user, and the input device 6 outputs the 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 it in the processing of system software or game software, and causes the output device 4 to output the processing result. In the information processing system 1, the information processing device 10 is a game device (game console) that executes a game, and the input device 6 may be a device that supplies the operation information of the user to the information processing device 10 such as a game controller. Note that the input device 6 may be an input interface such as a keyboard or a mouse.

[0018] The auxiliary storage device 2 is a large-capacity recording device such as an HDD (hard disk drive) or an SSD (solid state drive), and may be a built-in recording device, or may be an external recording device connected to the information processing device 10 by USB (Universal Serial Bus) or the like. The output device 4 may be a television having a display that outputs an image and a speaker that outputs sound. The output device 4 may be connected to the information processing device 10 by a wired cable or may be wirelessly connected.

[0019] The camera 7, which is an imaging device, is provided near the output device 4 and images the space around the output device 4. In FIG. 1, an example is shown in which the camera 7 is attached above the output device 4, but it may be arranged on the side or bottom of the output device 4. In any case, it is arranged at a position where it can image the user located in front of the output device 4. The camera 7 may be a stereo camera.

[0020] The server device 5 provides network services to the users of the information processing system 1. The server device 5 manages network accounts for identifying each user, and each user signs in to the network services provided by the server device 5 using the network account. By signing in to the network service from the information processing device 10, the user can register game save data and trophies, which are virtual rewards obtained during game play, with the server device 5. By registering save data and trophies with the server device 5, the user can synchronize the save data and trophies even when using an information processing device different from the information processing device 10.

[0021] The server device 5 of the embodiment collects event data from a plurality of information processing devices 10 operated by a plurality of players. The server device 5 statistically processes the play time of the activity from the collected event data and evaluates the play time corresponding to the player skill value representing the skill of game play for each activity. The server device 5 also derives the player skill value of the user from the collected event data. Based on the play time corresponding to the player skill value of the activity and the player skill value of the user, the server device 5 can notify the user who has not yet executed the activity of the play time corresponding to the player skill value of the user as the predicted play time. When notified of the predicted play time, the user can determine whether to play the activity based on their own situation (for example, having to go out in one hour, etc.).

[0022] Figure 2 shows the hardware configuration of the information processing apparatus 10. The information processing apparatus 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.

[0023] The main system 60 includes a main CPU (Central Processing Unit), a memory which is a main storage device and a memory controller, a GPU (Graphics Processing Unit), etc. The GPU is mainly used for the arithmetic processing of game programs. The main CPU has a function of starting the system software and executing a game program installed in the auxiliary storage device 2 in the environment provided by the system software. The subsystem 50 includes a sub CPU, a memory which is a main storage device and a memory controller, etc., and does not include a GPU.

[0024] While the main CPU has a function of executing a game program installed in the auxiliary storage device 2, the sub CPU does not have such a function. However, the sub CPU has a function of accessing the auxiliary storage device 2 and a function of transmitting and receiving data to and from the server device 5. The sub CPU is configured to have only such limited processing functions, and thus can operate with lower power consumption compared to the main CPU. These functions of the sub CPU are executed when the main CPU is in the standby state.

[0025] The main power button 20 is an input unit for receiving operation inputs from the user. It is provided on the front surface of the housing of the information processing apparatus 10 and is operated to turn on or off the power supply to the main system 60 of the information processing apparatus 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 the pressing of the main power button 20 by the user.

[0026] 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.

[0027] The device controller 30 is configured as an LSI (Large-Scale Integrated Circuit) that executes information transfer between devices, such as a south bridge. As shown in the figure, devices such as the system controller 24, the media drive 32, the USB module 34, the flash memory 36, the wireless communication module 38, the wired communication module 40, the subsystem 50, and the 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 the respective devices and controls the timing of data transfer.

[0028] The media drive 32 is a drive device that mounts and drives a ROM medium 44 recording application software such as games and license information, and reads programs, data, etc. 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.

[0029] The USB module 34 is a module that connects to an external device via a USB cable. The USB module 34 may be connected to the auxiliary storage device 2 and the camera 7 via a USB cable. The flash memory 36 is an auxiliary storage device that constitutes the internal storage. The wireless communication module 38 wirelessly communicates with the input device 6 using a communication protocol such as the Bluetooth (registered trademark) protocol or the IEEE802.11 protocol. The wired communication module 40 communicates with an external device and connects to the network 3 via the AP 8.

[0030] Figure 3 shows the functional blocks of the information processing apparatus 10. The information processing apparatus 10 includes a processing unit 100 and a communication unit 102. The processing unit 100 includes game software 110, an event information acquisition unit 12 , an event data transmission unit 122, a game image generation unit 130, a display processing unit 140, and an activity information acquisition unit 150.

[0031] The information processing apparatus 10 includes a computer, and by the computer executing a program, various functions shown in FIG. 3 are realized. The computer includes, as hardware, a memory for loading a program, one or more processors for executing the loaded program, an auxiliary storage device, and other LSIs. The processor is composed of a plurality of electronic circuits including a semiconductor integrated circuit and an LSI, and the plurality of electronic circuits may be mounted on one chip or may be mounted on a plurality of chips. It is understood by those skilled in the art that the functional blocks shown in FIG. 3 are realized by the cooperation of hardware and software, and thus these functional blocks can be realized in various forms by hardware only, software only, or a combination thereof.

[0032] The game software 110 includes at least a game program, image data, and sound data. The game program receives operation information of the input device 6 by the user and performs arithmetic processing to move game characters in the virtual space. The game image generation unit 130 includes a GPU (Graphics Processing Unit) that executes rendering processing and the like, and generates game image data. The display processing unit 140 outputs the generated game image from the output device 4. Note that the processing unit 100 includes a game sound generation unit that generates game sound data and a sound output unit that outputs game sound, but illustration thereof is omitted in FIG. 3.

[0033] When the game program starts an activity during the game progress, it outputs event information indicating the occurrence of the start event of the activity, and when the activity ends, it outputs event information indicating the occurrence of the end event of the activity. When the event information acquisition unit 120 acquires event information from the game software 110, it generates event data obtained by adding game ID and time information (timestamp) indicating the time when the event occurred to the event information, and provides it to the event data transmission unit 122. Note that the game program may output event information including a game ID and / or a timestamp to the event information acquisition unit 120. The event data transmission unit 122 transmits the generated event data to the server device 5 via the communication unit 102.

[0034] Game developers may incorporate various activities into the game. For example, when a battle activity with an enemy boss is incorporated into the game, the game program outputs, at the start of the battle, event information including an activity ID that identifies the battle activity and information indicating the start of the battle activity. When the player wins the battle against the enemy boss, the game program outputs event information including an activity ID that identifies the battle activity, information indicating the end of the battle activity, and information indicating the success of the activity.

[0035] The event data transmission unit 122 transmits event data regarding the activities executed by the player to the server device 5 via the communication unit 102. In the information processing system 1, the transmission process of the event data is performed by all the information processing devices 10 connected to the server device 5, and the server device 5 collects event data regarding various activities of various games from the plurality of information processing devices 10. When the event information acquisition unit 120 acquires event information from the game software 110, it preferably generates event data with a timestamp added thereto, and the event data transmission unit 122 transmits the event data to the server device 5.

[0036] FIG. 4 shows the functional blocks of the server device 5 of the embodiment. The server device 5 includes a processing unit 200, a communication unit 202, an event data recording unit 250, a play time recording unit 252, a play time distribution recording unit 254, a skill value recording unit 256, a representative value recording unit 258, an activity description recording unit 260, and an activity group recording unit 262. The processing unit 200 includes an event data acquisition unit 210, a play time acquisition unit 212, a skill value derivation unit 214, a statistical processing unit 220, and an information providing unit 240. The statistical processing unit 220 includes a distribution generation unit 222, a class distribution unit 224, a representative value determination unit 226, and a classification unit 228. The information providing unit 240 includes a class acquisition unit 242, a play time extraction unit 244, and a notification unit 246.

[0037] The server device 5 includes a computer, and by executing a program on the computer, various functions shown in FIG. 4 are realized. The computer includes, as hardware, a memory for loading a program, one or more processors for executing the loaded program, an auxiliary storage device, and other LSIs. The processor is composed of a plurality of electronic circuits including semiconductor integrated circuits and LSIs, and the plurality of electronic circuits may be mounted on one chip or may be mounted on a plurality of chips. The functional blocks shown in FIG. 4 are realized by the cooperation of hardware and software. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by only hardware, only software, or a combination thereof.

[0038] The event data acquisition unit 210 acquires event data regarding activities executed by a plurality of players from a plurality of information processing devices 10 and records the event data in the event data recording unit 250. The event data recording unit 250 records the event data for each game title in association with the player's network account. As described above, the event data at least includes a game ID, an activity ID, information indicating the start or end of an activity, and a timestamp. Hereinafter, the procedure for the server device 5 to analyze the collected event data will be described.

[0039] FIG. 5 shows the procedure for analyzing the collected event data. The analysis of the event data includes a play time calculation process (S10) by the play time acquisition unit 212, a skill value derivation process (S12) by the skill value derivation unit 214, and a representative value determination process (S14) by the representative value determination unit 226.

[0040] <S10: Play Time Calculation Process> The play time acquisition unit 212 acquires the play time of the activity. The play time acquisition unit 212 acquires the play time of the activity for a plurality of players from the event data (start event data) including the start event information of the activity recorded in the event data recording unit 250 and the event data (end event data) including the end event information of the activity. The play time of the activity is calculated based on the time stamp included in the start event data and the time stamp included in the end event data.

[0041] FIG. 6 is a diagram for explaining a method of calculating the play time. FIG. 6 shows the start event and end event of Activity A by one player. Specifically, "A start" indicates the start event of Activity A, and "A end" indicates the end event of Activity A. The start event data includes the start time t1 of Activity A, and the end event data includes the end time t2 of Activity A. The play time acquisition unit 212 calculates the play time of Activity A by the player as (t2 - t1). The play time acquisition unit 212 associates the calculated play time of the activity with the player's network account and records it in the play time recording unit 252 together with the game ID and the activity ID. The play time acquisition unit 212 analyzes the event data of a plurality of players, acquires the play time of each of the plurality of activities for each player, and records it in the play time recording unit 252.

[0042] <S12: Skill value derivation process> In the statistical processing unit 220, the distribution generation unit 222 generates a distribution of play times for each activity based on the plurality of play times acquired by the play time acquisition unit 212. The distribution generation unit 222 records the distribution of play times generated for each activity in the play time distribution recording unit 254.

[0043] FIG. 7(a) and FIG. 7(b) show distribution curves representing the relationship between the play time of one activity and the number of players who executed the activity. The horizontal axis represents the play time, and the vertical axis represents the number of players. FIG. 7(a) shows the relationship between the play time of Activity A and the number of players, and FIG. 7(b) shows the relationship between the play time of Activity B and the number of players.

[0044] Based on the finding that the play time of an activity correlates with the user's game skill (ability), the server device 5 realizes a technique of deriving a skill value that evaluates the user's skill and notifying the user of the play time corresponding to the derived skill value. In this finding, users with high game skills complete the activity in a relatively short play time, while users with low game skills complete the activity in a relatively long play time. Therefore, if the user's game skill is high, it is predicted that both Activity A and Activity B will be completed in a short play time, and if the user's game skill is low, it is predicted that both Activity A and Activity B will be completed in a long play time.

[0045] FIG. 8(a) and FIG. 8(b) show the distribution of play time respectively. The distribution generation unit 222 arranges all players in ascending order of play time for each activity and divides all players into a plurality of groups so that the number of players included in each group is equal. In the embodiment, the distribution generation unit 222 divides all players into 10 groups 1 to 10, and the number of players included in each group is the same. When the number of players who played Activity A is 1 million, each group includes 100,000 players, Group 1 is the group that gathers players with the shortest play time, and Group 10 is the group that gathers players with the longest play time. The distribution generation unit 222 records the distribution of play time generated for each activity in the play time distribution recording unit 254. For example, the play time distribution recording unit 254 may record the distribution of play time by associating the group numbers from 1 to 10 with the shortest play time and the longest play time included in each group.

[0046] The skill value derivation unit 214 derives the user's skill value for the activity based on the play time of the activity by the user and the distribution of the play time of the activity. The skill value derivation unit 214 refers to the play time distribution recorded in the play time distribution recording unit 254, and identifies the group number of the group in which the user's play time is included, thereby deriving the user's skill value for the activity. In an embodiment, the group number may directly represent the skill value. Therefore, if the play time of a user who executed activity A is included in the group with group number 3, the skill value derivation unit 214 derives the user's skill value for activity A as "3", and if the play time of a user who executed activity B is included in the group with group number 5, the skill value derivation unit 214 derives the user's skill value for activity B as "5". In an embodiment, skill value 1 indicates the highest skill level, and skill value 10 indicates the lowest skill level. The skill value derivation unit 214 associates the skill values derived for all the activities played by the user with the user's (player's) network account and records them in the skill value recording unit 256.

[0047] The skill value derivation unit 214 derives a skill value related to the user's game play based on the plurality of skill values derived for a plurality of activities. Specifically, the skill value derivation unit 214 derives the user's skill values for a plurality of activities that the user has recently played, and averages the derived plurality of skill values to derive a skill value related to the user's game play. In an embodiment, the skill value derivation unit 214 averages the ten skill values derived for the most recent ten activities to derive a skill value related to the user's game play. Hereinafter, the skill value related to the user's game play may also be referred to as the "player skill value".

[0048] Below, a specific example of deriving a player skill value of user X is shown. The numerical values shown in the specific example may include values that deviate from the above-mentioned findings, but please note that they are merely examples of numerical values used to explain the embodiment.

[0049] The group numbers of the groups containing the play times of the 10 most recent activities played by user X are as follows: Activity A Group No. 3 Activity B Group No. 5 Activity C Group No. 1 Activity D Group No. 2 Activity E Group No. 4 Activity F Group No. 7 Activity G Group No. 5 Activity H Group No. 5 Activity I Group No. 8 Activity J Group No. 10

[0050] Since the skill value is expressed by the group number, the skill value of the user for each activity is derived as follows: Activity A Skill Value 3 Activity B Skill value 5 Activity C Skill Value 1 Activity D Skill Value 2 Activity E Skill value 4 Activity F Skill value 7 Activity G Skill Value 5 Activity H Skill Value 5 Activity I Skill Score 8 Activity J Skill value 10

[0051] The skill value derivation unit 214 averages the skill values derived for the 10 activities to derive a player skill value for user X. (Player Skill Value) = (3 + 5 + 1 + 2 + 4 + 7 + 5 + 5 + 8 + 10) / 10 = 5 In this way, the player skill value of User X is derived as "5" by averaging the skill values derived for the most recent 10 activities.

[0052] The skill value derivation unit 214 derives the player skill values of all users and records them in the skill value recording unit 256. The player skill value takes a value within the range where the minimum value is 1 and the maximum value is 10. It can be said that the lower the player skill value of a user, the higher the game skill, and the higher the player skill value of a user, the lower the game skill.

[0053] As described above, the skill value derivation unit 214 derives the player skill value based on the skill values derived for a predetermined number of the most recent activities. The skill value derivation unit 214 may update the player skill value of the user every time the user executes an activity, or may update the player skill value periodically, for example, once a day.

[0054] <S14: Representative Value Determination Process> In the statistical processing unit 220, the class distribution unit 224 sets a plurality of classes and distributes the player to one of the plurality of classes according to the player's player skill value. In the embodiment, the class distribution unit 224 sets 9 classes and performs the distribution process of the user according to the following rules. PS represents the player skill value. 1 ≤ PS ≤ 2 Class 1 2 < PS ≤ 3 Class 2 3 < PS ≤ 4 Class 3 4 < PS ≤ 5 Class 4 5 < PS ≤ 6 Class 5 6 < PS ≤ 7 Class 6 7 < PS ≤ 8 Class 7 8 < PS ≤ 9 Class 8 9 < PS ≤ 10 Class 9

[0055] User X, whose player skill value described above is "5", is assigned to Class 4 by the class assignment unit 224. The class assignment unit 224 assigns all users participating in the information processing system 1 to any one of Classes 1 to 9 according to the player skill value, and records the class of each user in the skill value recording unit 256.

[0056] FIG. 9 shows a graph in which players are plotted for each class in a distribution curve showing the relationship between the play time and the number of players of Activity S. The graph of Class 1 shows the relationship between the play time and the number of players of a plurality of players belonging to Class 1, and the graph of Class 2 shows the relationship between the play time and the number of players of a plurality of players belonging to Class 2. That is, the graph of Class N (1 to 9) represents the relationship between the play time and the number of players of a plurality of players belonging to Class N. Therefore, when the graphs of all Classes 1 to 9 are added together, it becomes a distribution curve showing the relationship between the play time and the number of players of all users of Activity S. Note that the number of players belonging to each class may be different.

[0057] The representative value determination unit 226 determines a representative value of the play time in each class based on the play times of a plurality of players in each class for each activity. The representative value determination unit 226 may derive the median value of the plurality of play times in each class as the representative value of each class. The representative value determination unit 226 determines the representative value (medN) of the play time in each class as follows. Class 1 med1 Class 2 med2 Class 3 med3 Class 4 med4 Class 5 med5 Class 6 med6 Class 7 med7 Class 8 med8 Class 9 med9

[0058] The representative value of the play time determined for each class may be provided as the expected play time for users who have not yet executed the activity. When user X belonging to class 4 has not yet executed activity S, the server device 5 can notify user X that the expected play time when playing activity S is "med4".

[0059] The representative value determination unit 226 determines the representative value for each of classes 1 to 9 for all activities, associates the class with the representative value, and records it in the representative value recording unit 258. In the embodiment, the representative value is the median value, but it may also be the average value or the mode. The representative value determination unit 226 may perform the representative value determination process periodically, for example, once a day. The above is the description of the analysis process of the event data in the embodiment.

[0060] The information providing unit 240 notifies the user operating the information processing device 10 of the expected play time of the activity that the user has not yet executed. Specifically, the information providing unit 240 notifies the user of the representative value of the play time associated with the class of the user or the time based on the representative value as the expected play time. The information providing unit 240 may notify the expected play time at an arbitrary timing.

[0061] The class acquisition unit 242 acquires, from the skill value recording unit 256, the class to which the user operating the information processing apparatus 10 belongs. For example, when user X logs in to the information processing apparatus 10, the class acquisition unit 242 may acquire, from the skill value recording unit 256, the class to which the logged-in user X belongs. The play time extraction unit 244 extracts, from the representative value recording unit 258, the representative value of the play time associated with the class of user X with respect to the activities executable by user X. The notification unit 246 notifies the information processing apparatus 10 of user X of information regarding the predicted play time based on the extracted representative value of the play time. Note that the predicted play time based on the representative value of the play time may be the representative value of the play time itself, or may be a time obtained by slightly adjusting the representative value of the play time. For example, when the representative value of the play time is 4.9 minutes, the notification unit 246 may use a rounded time (e.g., 5 minutes) as the predicted play time. The notification unit 246 may notify the information processing apparatus 10 of user X of information regarding the predicted play times of a plurality of activities.

[0062] In the information processing apparatus 10, the activity information acquisition unit 150 acquires information regarding the predicted play time of an activity from the server apparatus 5. The display processing unit 140 displays the information acquired by the activity information acquisition unit 150.

[0063] FIG. 10 shows an example of a system screen displayed on the output device 4. The display processing unit 140 generates system images 180, 182, 184, 186 from the information acquired by the activity information acquisition unit 150 and displays them on the output device 4. The four system images 180, 182, 184, 186 display the predicted play times of activities of different game titles. Thereby, user X can determine which game to play from the predicted play times of the respective activities.

[0064] The system images 180, 182, 184, 186 displayed in card form may be a GUI (Graphical User Interface). For example, when user X selects any of the system images, the game corresponding to the system image may be automatically launched.

[0065] In the above embodiment, on the premise that a user with high game skills completes the activity in a relatively short play time, while a user with low game skills completes the activity in a relatively long play time, the skill value derivation unit 214 derives the player skill value by averaging the skill values derived for a predetermined number of the most recent activities. This player skill value represents the user's skill regarding the most recent game play.

[0066] However, various activities with different characteristics are prepared in the game. Even in one game title, there are various activities with different characteristics, such as defeating enemy characters, searching for items, reaching the goal without falling, solving puzzles, etc., and the skills required by each for the user are also different. Therefore, when user X, who is good at combat-related quests and poor at exploration-related quests, continuously plays 10 combat-related activities recently, the skill value derivation unit 214 derives a player skill value indicating high skill, and thus user X is assigned to a higher-level class.

[0067] When presenting the predicted play time of an activity that user X has not yet played, the play time extraction unit 244 extracts, from the representative value recording unit 258, the representative value of the play time associated with the class of user X for that activity. Since user X is assigned to a high-level class, the play time extraction unit 244 extracts the representative value of the play time for highly skilled players for that activity. At this time, there is no problem if the activity is a combat type, but if the activity is an exploration type, a representative value of the play time for highly skilled players will be extracted even though user X is not good at exploration quests. For this reason, an overly short predicted play time will be presented to user X, and user X will not be able to complete the exploration activity within the presented predicted play time.

[0068] Therefore, the statistical processing unit 220 of the embodiment has a function of grouping a plurality of activities according to the play tendencies of a plurality of players. The skill value derivation unit 214 derives a player skill value for each activity group, and the class distribution unit 224 sets the class of the user for each activity group based on the player skill value derived for each activity group. Therefore, when presenting the predicted play time of an activity that user X has not yet played, the play time extraction unit 244 can extract an appropriate representative value of the play time that matches the user's skill by identifying the activity group to which the activity belongs and identifying the class of the user set for the identified activity group.

[0069] In the statistical processing unit 220, the classification unit 228 has a function of grouping a plurality of activities. The classification unit 228 of the embodiment includes a function of clustering a plurality of activities using natural language and a function of clustering a plurality of activities using the skill value derived by the skill value derivation unit 214. Hereinafter, the two types of clustering functions will be described.

[0070] <Natural Language-based Clustering Function> The activity description recording unit 260 records the description text of the activities created by the game developer. The description text of the activity expresses the content of the activity in natural language and is usually prepared for the purpose of presenting it to the user. Therefore, the description text is created as a text message for the user to confirm the content of the activity, such as "Defeat Boss P", "Solve the puzzle and clear the stage", "Defeat 10 enemy characters", etc.

[0071] The classification unit 228 has a function of grouping the description texts prepared for each of the plurality of activities by natural language processing. For example, when the description text is in Japanese, the classification unit 228 may perform morphological analysis to split the description text into words, count the number of occurrences of the words, and generate a vector whose length is the number of vocabulary words. The classification unit 228 vectorizes all the description texts, evaluates the similarity between the sentence vectors, and performs grouping. The classification unit 228 may vectorize the activity description text using, for example, Sentence2vec. Also, various methods for evaluating the similarity of sentences have been proposed, and the classification unit 228 may use the K-means method as a clustering method.

[0072] Examples of the description texts of activities A to J are shown below. Activity A: "Defeat Boss A" Activity B: "Defeat Boss B" Activity C: "Defeat Boss C" Activity D: "Solve Puzzle D" Activity E: "Defeat Boss E" Activity F: "Defeat 10 characters of Character F" Activity G: "Defeat 5 characters of Character G" Activity H: "Solve Puzzle H" Activity I: "Defeat 15 characters of Character I" Activity J: "Solve Puzzle J"

[0073] Regarding activities A to J, the classification unit 228 may perform natural language processing to classify activities A to C, E to G, I and activities D, H, J into different groups. That is, the classification unit 228 may classify seven activities A to C, E to G, I into the first group and three activities D, H, J into the second group. The above grouping is an example, and the classification unit 228 may classify a plurality of activities into three or more groups by natural language processing.

[0074] When the game supports multiple languages, the classification unit 228 may group a plurality of activities using activity descriptions in multiple languages. When the game developer creates activity descriptions in two or more languages, the classification unit 228 may perform natural language processing on the activity descriptions in each language for grouping, and improve the accuracy of grouping a plurality of activities by statistically processing the grouping results derived from the activity descriptions in each language.

[0075] Note that the game developer may generate an activity ID in a natural language. For example, when generating the activity ID of activity A as "Defeat_A_boss". If there is an activity without a prepared description, the classification unit 228 may extract the natural language corresponding to the activity description from the activity ID and use it for clustering processing. The classification unit 228 records the result of grouping a plurality of activities in the activity group recording unit 262. In the activity group recording unit 262, the activity IDs of the activities belonging to each group may be recorded in association with information (group number) identifying each group.

[0076] <Skill value-based clustering function> In addition to the natural language-based clustering function, the classification unit 228 has a function of grouping a plurality of activities using the skill values of a plurality of users derived by the skill value derivation unit 214. In the following, as one of the typical functions of the grouping process, a process of finding an activity that is out of place from a plurality of activities constituting a group will be described.

[0077] FIG. 11 shows a flowchart of the skill value-based clustering process. The classification unit 228 acquires the skill values derived for a plurality of users for a plurality of activities belonging to one group from the skill value recording unit 256 (S10). The classification unit 228 may randomly select users with a predetermined number of samples (for example, 1000 people) and acquire the skill values derived for the selected predetermined number of users.

[0078] Note that in the initial state before the clustering process is performed, since all the activities included in the game are not grouped, they belong to one group. Therefore, when the classification unit 228 first performs the skill value-based clustering process, the skill values derived for a plurality of users may be acquired for all the activities.

[0079] Note that when the natural language-based clustering process has already been performed and a plurality of activities are grouped, the classification unit 228 acquires the skill values derived for a plurality of users for a plurality of activities belonging to one group that has already been classified. In this case, the classification unit 228 uses the natural language-based clustering function for the first-stage grouping, and uses the skill value-based clustering function for the second-stage grouping to improve the accuracy of each activity group classified in the first stage.

[0080] FIG. 12 shows an example of skill values derived for a plurality of activities belonging to activity group 1. In this example, activity group 1 is composed of ten activities K to T, and the activity group recording unit 262 records the activity IDs of activities K to T in association with activity group 1. The classification unit 228 refers to the activity group recording unit 262 to identify activities K to T that constitute activity group 1, and reads out the skill values derived for users A to D for activities K to T from the skill value recording unit 256. As described above, in the initial state where grouping is not performed, activity group 1 is composed of all the activities included in the game. In actual clustering processing, it is preferable for the classification unit 228 to perform skill value-based clustering processing using the skill values of hundreds or more users.

[0081] FIG. 13 shows an example of player skill values derived for a plurality of users. The classification unit 228 acquires the player skill values derived for users A to D from the skill value recording unit 256 (S12). The player skill value is a value obtained by averaging the skill values of a predetermined number of activities played most recently, and has the meaning as an index representing the skill of the most recent user. In the above description, the player skill value is derived as the average of the skill values of the ten activities played most recently. However, in the following description, regardless of the number ten, it is treated as the average of the skill values of a predetermined number of activities played most recently. The classification unit 228 calculates the difference (deviation) between the skill values of a plurality of users derived for each activity and the player skill values of the plurality of users, and derives the absolute value of the difference (S14).

[0082] FIG. 14 shows the absolute value of the difference between the skill value of each activity and the player skill value. The skill value of each activity represents the user's skill (ability) in that activity, and the player skill value represents the average skill of the user in a predetermined number of recent activities. Therefore, a large absolute value of the difference between the skill value of one activity and the player skill value means that the user has played that one activity either very well or very poorly compared to other activities in the group. This provides a basis for inferring that the nature or characteristics of that one activity are different from those of the remaining activities.

[0083] The classification unit 228 calculates the average of a plurality of absolute differences (hereinafter, also simply referred to as the "error average value") for each activity (S16). The error average value calculated for each activity is shown in the bottom row of the table shown in FIG. 14. The classification unit 228 performs a process of identifying the activity to be excluded from the group based on the error average value (S18). If there is no activity to be excluded within the group (N in S18), the classification unit 228 ends the clustering process for the group and performs the clustering process for another group.

[0084] In S18, the classification unit 228 identifies, as a candidate for exclusion from activity group 1, an activity among the plurality of activities belonging to activity group 1 that has a relatively large error average value. Specifically, the classification unit 228 identifies the activity with the largest error average value as the exclusion candidate. Referring to the bottom row of the table shown in FIG. 14, the activity with the largest error average value is activity N. The classification unit 228 compares the error average value of activity N with a predetermined threshold value, and if the error average value is less than or equal to the predetermined threshold value, determines that activity N belongs to group 1. The predetermined threshold value may be, for example, 2.0. Here, since the error average value of activity N is 3.15, the classification unit 228 determines that activity N is the activity to be excluded from this group 1 (Y in S18).

[0085] Subsequently, the classification unit 228 checks whether there is an activity to be excluded from this group in addition to activity N. For this check, the classification unit 228 calculates the centroid (average of skill values) of group 1 excluding activity N for each user.

[0086] FIG. 15 is a diagram for explaining a method of calculating the centroid of group 1 for user A. The classification unit 228 calculates the average of the skill values of the nine activities excluding activity N as the centroid of group 1. In this example, the centroid of group 1 excluding activity N is calculated to be 3.56. On the other hand, the centroid of activity N is the skill value itself, which is 1.

[0087] FIG. 16 shows the centroids of group 1 and the centroid of activity N calculated for a plurality of users. The classification unit 228 calculates the average of the skill values of the nine activities excluding activity N as the centroid of group 1 for each of the plurality of users.

[0088] Then, the classification unit 228 calculates the distance from the centroid of group 1 and the distance from the centroid of activity N for the skill value of each of the nine activities K to M, O to T (S20), and investigates which centroid the centroid of each activity is closer to (S22). Here, the fact that the centroid of the activity to be investigated is closer to the centroid of group 1 indicates that the activity has a property closer to group 1. On the other hand, the fact that the centroid of the activity to be investigated is closer to the centroid of the activity to be excluded (activity N) indicates that the activity to be investigated has a property closer to the group to which activity N belongs (at this point, only activity N belongs) than group 1.

[0089] The following shows the result of the investigation by the classification unit 228 on the centroid of activity P, which has the largest absolute difference among the nine activities. Figure 17 shows the result of comparing the distance from the centroid of Group 1 with the distance from the centroid of Activity N. The classification unit 228 calculates, for each user, the absolute value of the difference between (the centroid of Activity P) and (the centroid of Group 1 excluding Activity N), and the absolute value of the difference between (the centroid of Activity P) and (the centroid of Activity N), and calculates the average of each absolute difference. The bottom row of the table shown in Figure 17 shows the average of the distance between the centroid of Activity P and the centroid of Group 1, and the average of the distance between the centroid of Activity P and the centroid of Activity N.

[0090] From this investigation result, it can be seen that the centroid of Activity P is closer to the centroid of Group 1 than the centroid of Activity N. Therefore, the classification unit 228 determines that Activity P belongs to Group 1. The classification unit 228 conducts this investigation for all nine activities and determines whether there is an activity closer to Activity N than Group 1 (S22). If there is an activity closer to Activity N than Group 1 (Y in S22), the classification unit 228 decides to exclude the said activity from Group 1 and excludes it from Group 1 together with Activity N (S24). On the other hand, if there is no activity closer to Activity N than Group 1 (N in S22), the classification unit 228 decides that only Activity N is the target for exclusion and excludes Activity N from Group 1 (S24). As described above, the classification unit 228 can identify the outliers in Group 1 and make Group 1 into an aggregate of activities with similar play tendencies (play time) by individual users.

[0091] After deciding to exclude Activity N from Group 1, the classification unit 228 searches for a new group to which Activity N will belong. FIG. 18 shows an example of skill values derived for a plurality of activities belonging to activity group 2. In this example, activity group 2 is composed of four activities U to X, and the activity group recording unit 262 records the activity IDs of activities U to X in association with activity group 2. The classification unit 228 refers to the activity group recording unit 262 to identify activities U to X that make up activity group 2, and reads out the skill values derived for users A to D for activities U to X from the skill value recording unit 256.

[0092] FIG. 19 shows the centroids of group 1 and group 2 calculated for a plurality of users. The classification unit 228 calculates, for each of the plurality of users A to D, the average of the skill values of activity group 1 excluding activity N as the centroid of group 1. This centroid of group 1 is the same as that shown in FIG. 16. Further, the classification unit 228 calculates, for each of the plurality of users A to D, the average of the skill values of activity group 2 as the centroid of group 2.

[0093] FIG. 20 shows the centroid of activity N. The classification unit 228 investigates which of the centroids of group 1 and group 2 the centroid of activity N is closer to.

[0094] FIG. 21 shows the result of comparing the distance from the centroid of group 1 and the distance from the centroid of group 2. The classification unit 228 calculates, for each user, the absolute value of the difference between (the centroid of activity N) and (the centroid of group 1 excluding activity N), and the absolute value of the difference between (the centroid of activity N) and (the centroid of group 2), and calculates the average of the absolute values of the respective differences. The bottom row of the table shown in FIG. 21 shows the average of the distance between the centroid of activity N and the centroid of group 1, and the average of the distance between the centroid of activity N and the centroid of group 2.

[0095] From these survey results, it can be seen that the centroid of Activity N is closer to the centroid of Group 2 than to the centroid of Group 1. At this time, the classification unit 228 determines that Activity N belongs to Group 2 on the condition that the average of the absolute difference values (distances) between (the centroid of Activity N) and (the centroid of Group 2) is equal to or less than a predetermined threshold value (for example, 1.5). By this determination, Activity N is added to Group 2.

[0096] When the average of the absolute difference values is greater than the predetermined threshold value, the classification unit 228 also investigates other groups. As a result of investigating all groups, if there is no group in which the average of the absolute difference values is equal to or less than the predetermined threshold value, the classification unit 228 may create a new group including Activity N. The classification unit 228 records the result of the grouping in the activity group recording unit 262.

[0097] At the time when the classification unit 228 performs grouping, the distance between groups is separated by a predetermined value or more, but it is assumed that the distance between groups will vary as event data is accumulated. FIG. 22 shows an example of skill values derived for a plurality of activities belonging to Activity Group 3. In this example, Activity Group 3 is composed of five activities α to ε, and the activity group recording unit 262 records the activity IDs of activities α to ε in association with Activity Group 3. The classification unit 228 refers to the activity group recording unit 262 to identify activities α to ε that constitute Activity Group 3, and reads out the skill values derived for users A to D for activities α to ε from the skill value recording unit 256.

[0098] Figure 23 shows the centroids of Group 1 and Group 3 calculated for a plurality of users. The classification unit 228 calculates, for each of the plurality of users A to D, the average of the skill values of Activity Group 1 excluding Activity N as the centroid of Group 1. Also, the classification unit 228 calculates, for each of the plurality of users A to D, the average of the skill values of Activity Group 3 as the centroid of Group 3.

[0099] The classification unit 228 calculates, for each user, the absolute difference value (distance) between the centroids of Group 1 and Group 3, and obtains the average of the absolute difference values. This average of the absolute difference values represents the proximity of the two groups. The classification unit 228 may merge the two groups on the condition that the average of the absolute difference values is less than or equal to a predetermined threshold (for example, 0.5). For example, the classification unit 228 may cause Group 3 to disappear and make Activities α to ε belonging to Group 3 belong to Group 1. Note that the classification unit 228 may cause Group 1 to disappear and make Activities K to M, O to T belonging to Group 1 belong to Group 3. The classification unit 228 records the latest state of the grouping in the Activity Group Recording Unit 262.

[0100] By the classification unit 228 periodically performing skill value-based clustering processing, a plurality of activities can be grouped with high accuracy. By the classification unit 228 first performing grouping using a natural language-based clustering function and using the skill value-based clustering function to improve the accuracy of each activity group generated by the natural language-based clustering function, it becomes possible to generate a plurality of activity groups quickly and with high accuracy.

[0101] FIG. 24 is an image diagram of a group in which a plurality of activities are classified. In this example, the classification unit 228 classifies a plurality of activities into four groups. (Since Group 3 has been absorbed into Group 1, it has disappeared). The grouping process by the classification unit 228 reflects the play tendency (play time) of each user. For example, in Group 1, there are many combat-related activities, and in Group 2, there are many exploration-related activities.

[0102] In an embodiment, the skill value derivation unit 214 derives a player skill value for each activity group, and the class distribution unit 224 sets the user's class for each activity group based on the player skill value derived for each activity group. Therefore, when presenting the predicted play time of an activity that user X has not played yet, the play time extraction unit 244 identifies the activity group to which the activity belongs, and by identifying the user's class set for the identified activity group, an appropriate representative value of the play time that matches the user's skill can be extracted.

[0103] As described above, the present disclosure has been described based on embodiments. It is understood by those skilled in the art that these embodiments are examples, and various modifications are possible for each component and each combination of processing processes, and such modifications are also within the scope of the present disclosure. In the embodiment, an example of clustering a plurality of activities in one game title has been shown, but it is also possible to cluster a plurality of activities across a plurality of game titles.

[0104] The present disclosure may include the following aspects. [Item 1] A server device for deriving a skill value related to a user's game play, comprising one or more processors having hardware, The one or more processors group the plurality of activities, Obtain the play times of the activity for multiple users from the event data indicating the start of the activity and the event data indicating the end of the activity, Generate a distribution of play times for each activity based on the obtained multiple play times, Derive the skill value of the user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity, Derive a player skill value related to the user's game play for each activity group based on the multiple skill values derived for multiple activities, Server device. [Item 2] The one or more processors Derive a player skill value of the user for each activity group based on the multiple skill values derived for the multiple activities belonging to each activity group, The server device according to Item 1. [Item 3] The one or more processors Calculate the difference between the multiple skill values of the users derived for each activity and the player skill values of the multiple users, and group the multiple activities based on the multiple differences calculated for each activity, The server device according to Item 1. [Item 4] The one or more processors Group the multiple activities based on the absolute values of the multiple differences calculated for each activity, The server device according to Item 3. [Item 5] The one or more processors Exclude, from the group, the activity with a relatively large absolute value of the difference among the activities in one group, The server device according to Item 4. [Item 6] A server device for grouping a plurality of activities, comprising one or more processors having hardware, The one or more processors, obtain the play times of the activities of a plurality of users from event data indicating the start of an activity and event data indicating the end of the activity, generate a distribution of play times for each activity based on the obtained plurality of play times, derive a skill value of a user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity, derive a player skill value related to the game play of the user based on the plurality of skill values derived for the plurality of activities, calculate the difference between the skill values of the plurality of users derived for each activity and the player skill value of the plurality of users, group the plurality of activities based on the plurality of differences calculated for each activity, Server device. [Item 7] A server device for providing information related to an expected play time to a user, comprising one or more processors having hardware, The one or more processors, group a plurality of activities, derive a skill value of a user for the activity played by the user, derive a player skill value related to the game play of the user for each activity group based on the plurality of skill values derived for the plurality of activities, set a class of the user for each activity group based on the player skill value derived for each activity group, notify the user of information related to the expected play time associated with the class of the user, Server device to do. [Item 8] A method for deriving a skill value related to a user's game play, comprising: Obtaining the play times of a plurality of users for the activity from event data indicating the start of the activity and event data indicating the end of the activity; Generating a distribution of play times for each activity based on the obtained plurality of play times; Deriving a skill value of the user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity; Grouping a plurality of activities; Deriving a player skill value related to the user's game play for each activity group based on the plurality of skill values derived for the plurality of activities; Skill value derivation method. [Item 9] A method for grouping a plurality of activities, comprising: Obtaining the play times of a plurality of users for the activity from event data indicating the start of the activity and event data indicating the end of the activity; Generating a distribution of play times for each activity based on the obtained plurality of play times; Deriving a skill value of the user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity; Deriving a player skill value related to the user's game play based on the plurality of skill values derived for the plurality of activities; Calculating the difference between the plurality of skill values of the plurality of users derived for each activity and the player skill value of the plurality of users; Grouping the plurality of activities based on the plurality of differences calculated for each activity; Grouping method. [Item 10] A method for providing information on predicted play time to a user, comprising: Grouping a plurality of activities; Derive the user's skill value for the activity played by the user, Based on the multiple skill values derived for multiple activities, derive the player skill value regarding the user's game play for each activity group, Based on the player skill value derived for each activity group, set the user's class for each activity group, Notify the user of the information regarding the predicted play time associated with the user's class, Information providing method. [Item 11] On the computer, From the event data indicating the start of the activity and the event data indicating the end of the activity, obtain the play time of the activity for multiple users, and Based on the multiple obtained play times, generate the distribution of the play time for each activity, and Based on the play time of the activity by the user and the distribution of the play time of the activity, derive the skill value of the user for the activity, and Function to group multiple activities, and Based on the multiple skill values derived for multiple activities, derive the player skill value regarding the user's game play for each activity group, and A recording medium recording a program for realizing the above. [Item 12] On the computer, From the event data indicating the start of the activity and the event data indicating the end of the activity, obtain the play time of the activity for multiple users, and Based on the multiple obtained play times, generate the distribution of the play time for each activity, and Based on the play time of the activity by the user and the distribution of the play time of the activity, derive the skill value of the user for the activity, Based on a plurality of skill values derived for a plurality of activities, a function for deriving a player skill value related to the user's gameplay, and A function for calculating the difference between the plurality of user skill values derived for each activity and the player skill value of the plurality of users; A function for grouping a plurality of activities based on the plurality of differences calculated for each activity; A recording medium recording a program for realizing the above. [Item 13] On a computer, A function for grouping a plurality of activities; A function for deriving the user's skill value for the activity played by the user; Based on a plurality of skill values derived for a plurality of activities, a function for deriving a player skill value related to the user's gameplay for each activity group; A function for setting the user's class for each activity group based on the player skill value derived for each activity group; A function for notifying the user of information regarding the predicted play time associated with the user's class; A recording medium recording a program for realizing the above.

Industrial Applicability

[0105] The present disclosure can be used in the technical field of processing event data related to game activities executed by a user.

Explanation of Signs

[0106] 1... Information processing system, 5... Server device, 10... Information processing device, 100... Processing unit, 102... Communication unit, 110... Game software, 120... Event information acquisition unit, 122... Event data transmission unit, 130... Game image generation unit, 140... Display processing unit, 150... Activity information acquisition unit, 200... Processing unit, 202... Communication unit, 210... Event data acquisition unit, 212... Play time acquisition unit, 214... Skill value derivation unit, 220... Statistical processing unit, 222... Distribution generation unit, 224... Class distribution unit, 226... Representative value determination unit, 228... Classification unit, 240... Information providing unit, 242... Class acquisition unit, 244... Play time extraction unit, 246... Notification unit, 250... Event data recording unit, 252... Play time recording unit, 254... Play time distribution recording unit, 256... Skill value recording unit, 258... Representative value recording unit, 260... Activity description recording unit, 262... Activity group recording unit.

Claims

1. A server device for deriving a skill value related to a user's game play, comprising: an event data recording unit that records event data related to activities executed by a plurality of users; a play time acquisition unit that acquires the play times of the plurality of users for the activity from the event data indicating the start of the activity and the event data indicating the end of the activity recorded in the event data recording unit; a statistical processing unit that generates a distribution of play times for each activity based on the plurality of acquired play times; a skill value derivation unit that derives a skill value of the user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity; a classification unit that groups a plurality of activities; and the skill value derivation unit derives a player skill value related to the user's game play for each activity group based on the plurality of skill values derived for the plurality of activities. The server device is characterized by the above.

2. The skill value derivation unit derives a player skill value of the user for each activity group based on the plurality of skill values derived for the plurality of activities belonging to each activity group. The server device according to claim 1, characterized by the above.

3. The classification unit calculates the difference between the skill values of the plurality of users derived for each activity and the player skill values of the plurality of users, and groups the plurality of activities based on the plurality of calculated differences for each activity. The server device according to claim 1, characterized by the above.

4. The classification unit groups the plurality of activities based on the absolute values of the plurality of calculated differences for each activity. The server device according to claim 3, characterized by the above.

5. The classification unit excludes, from the group, an activity having a relatively large absolute value of the difference among the activities in one group. The server device according to claim 4, characterized by the above.

6. A server device for grouping a plurality of activities, comprising: an event data recording unit that records event data related to activities executed by a plurality of users; A play time acquisition unit that acquires the play times of the activity for a plurality of users from the event data indicating the start of the activity and the event data indicating the end of the activity, which are recorded in the event data recording unit, A statistical processing unit that generates a distribution of play times for each activity based on the plurality of acquired play times, A skill value derivation unit that derives a skill value of a user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity, A classification unit that groups a plurality of activities, The skill value derivation unit derives a player skill value related to the user's game play based on the plurality of skill values derived for the plurality of activities, The classification unit calculates the difference between the skill values of the plurality of users derived for each activity and the player skill value of the plurality of users, and groups the plurality of activities based on the plurality of calculated differences for each activity, A server device characterized by the above. **Claim 7**: A method for a server device to derive a skill value related to a user's game play, wherein the server device acquires the play times of the activity for a plurality of users from the event data indicating the start of the activity and the event data indicating the end of the activity; generates a distribution of play times for each activity based on the plurality of acquired play times; derives a skill value of a user for the activity based on the play time of the activity by the user and the distribution of the play times of the activity; groups a plurality of activities; derives a player skill value related to the user's game play for each activity group based on the plurality of skill values derived for the plurality of activities; A skill value derivation method for executing the above. **Claim 8**: A method for a server device to group a plurality of activities, wherein the server device acquires the play times of the activity for a plurality of users from the event data indicating the start of the activity and the event data indicating the end of the activity; generating a distribution of play times for each activity based on the obtained multiple play times; deriving a user's skill value for the activity based on the play time of the activity by the user and the distribution of the play time of the activity; deriving a player skill value related to the user's game play based on the multiple skill values derived for the multiple activities; calculating the difference between the multiple skill values of the users derived for each activity and the player skill value of the multiple users; grouping the multiple activities based on the multiple differences calculated for each activity; A grouping method for performing.

9. On a computer, a function of obtaining the play times of the activity for multiple users from event data indicating the start of the activity and event data indicating the end of the activity; a function of generating a distribution of play times for each activity based on the obtained multiple play times; a function of deriving a user's skill value for the activity based on the play time of the activity by the user and the distribution of the play time of the activity; a function of grouping the multiple activities; a function of deriving a player skill value related to the user's game play for each activity group based on the multiple skill values derived for the multiple activities; A program for realizing.

10. On a computer, a function of obtaining the play times of the activity for multiple users from event data indicating the start of the activity and event data indicating the end of the activity; a function of generating a distribution of play times for each activity based on the obtained multiple play times; a function of deriving a user's skill value for the activity based on the play time of the activity by the user and the distribution of the play time of the activity; a function of deriving a player skill value related to the user's game play based on the multiple skill values derived for the multiple activities; a function of calculating the difference between the multiple skill values of the users derived for each activity and the player skill value of the multiple users; A function that groups a plurality of activities based on the plurality of differences calculated for each activity, and A program for realizing the same.

Images