HK1252809B - Server and non-transitory computer readable medium - Google Patents

Description

Server and non-transitory computer-readable medium

Technical Field

The present invention relates to a server and a program.

Background Art

As games that can be executed on terminals such as smartphones, there are conventional games such as multiplayer battle games in which a plurality of players can participate (for example, refer to Patent Document 1).

Most of these games have a ranking function. In other words, ranking is a basic and necessary function for these games.

Reference List

Patent Literature

Patent Document 1: Japanese Patent Application Laid-Open No. 2006-192142

Summary of the Invention

Problems to be solved by the invention

However, in existing games, there are only rankings using a single ranking indicator such as daily ranking or comprehensive ranking, etc. It is no exaggeration to say that most of the players with higher rankings in the rankings of existing games are so-called important players.

Therefore, in existing games, the ranking function is not very familiar to most ordinary gamers.

Therefore, in order to link the ranking function with the fun of the game for a wide range of players, it is necessary to establish a technology that encourages each player to participate in the ranking.

The present invention has been made in view of the above circumstances, and an object of the present invention is to establish a technology for motivating each player to participate in ranking.

Solutions for solving problems

To achieve the above-mentioned object, a server according to an aspect of the present invention is a server configured to communicate with each of a plurality of terminals capable of executing a game by accepting operations performed by respective players, manage a plurality of types of ranking indicators in which at least one player among the plurality of players participates as a participant, and calculate a ranking of the participant for each of the plurality of types of ranking indicators, the server comprising:

a prediction unit for setting a player to be processed among the plurality of players as a player of interest, and calculating, for at least some of the plurality of types of ranking indices, a ranking of the player of interest when participating in each ranking indicator as a predicted ranking;

a determining component for determining a ranking index to be recommended to the interested player as a recommended ranking index based on the predicted ranking; and

The presenting component is used to present the recommended ranking indicator to the terminal of the player paying attention to the game.

Effects of the Invention

The present invention enables the establishment of a technique for motivating each player to participate in ranking.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing the structure of an information processing system according to an embodiment of the present invention.

FIG. 2 is a block diagram showing a hardware structure of a server as an embodiment of a terminal according to the present invention in the information processing system of FIG. 1 .

FIG. 3 is a functional block diagram illustrating an example of a functional structure of the server of FIG. 2 .

FIG. 4 is a diagram showing an example of a user interface related to a player's participation in a ranking.

FIG. 5 is a diagram showing an example of the structure of a participation ranking index DB in the server of FIG. 3 .

FIG. 6 is a diagram showing a structure of a general log database, that is, an example of a log structure.

FIG. 7 is a diagram showing a specific example of one entry of the log having the log structure of FIG. 6 .

FIG. 8 is a diagram showing a specific example of an entry of the log having the log structure of FIG. 6 , wherein the entry is generated when the player recovers the physical strength of his character by using recovery magic or a recovery item.

FIG. 9 is a diagram showing a specific example of implementation of ranking indicators, that is, an example of a ranking structure.

FIG. 10 is a diagram showing a specific example of one entry of the ranking index having the ranking structure in FIG. 9 .

FIG. 11 is a diagram for explaining an example of a method for calculating a predicted ranking of a player of interest when the ranking index of interest is an index of a ranking in which the player of interest has not yet participated.

FIG. 12 is a diagram showing an example of time-series information of past score logs related to the ranking index of interest.

FIG. 13 is a diagram showing an example of a template for automatically generating a ranking index related to damage in a game.

FIG. 14 is a diagram showing an application example different from the example in FIG. 13 , that is, an example of a type of template in which a player who has achieved control over a specific character has a higher ranking.

FIG. 15 is a diagram illustrating an application example different from the examples of FIGS. 13 and 14 , that is, an example of a template for automatically generating a ranking index using a score not related to injury.

FIG. 16 is a flowchart for explaining an example of the flow of a ranking recommendation process in the processing of the server having the functional configuration in FIG. 3 .

FIG. 17 is a flowchart for explaining an example of details of the predicted order calculation process in step S1 of the ranking recommendation process in FIG. 16 .

DETAILED DESCRIPTION

Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

It should be understood that what is hereinafter simply referred to as an "image" should be interpreted as including both a "moving image" and a "still image."

Furthermore, the term "moving image" should be interpreted as including images displayed through the following first to third processes, respectively. The first process refers to a process for displaying a series of multiple still images while continuously switching between them over time, in response to each action of an object (e.g., a game character) in a two-dimensional image (2D image). Specifically, two-dimensional animation, i.e., a process similar to so-called flipping animation, is an example of the first process.

The second process is to pre-set motions corresponding to the actions of an object (e.g., a game character) in a stereoscopic image (an image based on a 3D model) and display the object while changing the motions over time. Specifically, three-dimensional animation is an example of the second process.

The third process refers to a process for preparing a video (ie, a moving image) corresponding to each action of an object (eg, a game character) and replaying the video over time.

FIG1 shows the structure of an information processing system according to an embodiment of the present invention.

The information processing system shown in FIG1 is a system including player terminals 1-1 to 1-n used by n players (n is an arbitrary integer greater than or equal to 1), and a server 2. Each of the player terminals 1-1 to 1-n and the server 2 is connected to each other via a predetermined network N such as the Internet.

The server 2 provides a game execution environment to each player terminal 1-1 to 1-n, and provides various services related to the games executed at each player terminal 1-1 to 1-n. As one of these services, in this embodiment, a new ranking function service using various ranking indicators is adopted.

Hereinafter, when there is no need to distinguish between the player terminals 1-1 to 1-n, these player terminals will be collectively referred to as "player terminal 1".

FIG. 2 is a block diagram showing a hardware configuration of server 2 as an embodiment of the present invention in the information processing system of FIG. 1 .

2 includes a CPU (Central Processing Unit) 51 , a ROM (Read Only Memory) 52 , a RAM (Random Access Memory) 53 , a bus 54 , an input/output interface 55 , an output unit 56 , an input unit 57 , a storage unit 58 , a communication unit 59 , and a drive 60 .

The CPU 51 executes various processes according to a program recorded in the ROM 52 or a program loaded from the storage unit 58 into the RAM 53 .

The RAM 53 also appropriately stores data and the like necessary for the CPU 51 to execute various processes.

The CPU 51, ROM 52, and RAM 53 are connected to each other via a bus 54. An input/output interface 55 is also connected to the bus 54. An output unit 56, an input unit 57, a storage unit 58, a communication unit 59, and a drive 60 are connected to the input/output interface 55.

The input unit 56 is composed of a display, a speaker, and the like, and outputs various information in the form of images or audio.

The input unit 57 is composed of a keyboard, a mouse, and the like, and enables input of various information.

The storage unit 58 is composed of a hard disk, a DRAM (Dynamic Random Access Memory), and the like, and stores various data.

The communication unit 59 controls communication performed with other devices (the player terminal 1 in the example of FIG. 1 ) via the network N including the Internet.

The drive 60 is provided as needed. A removable medium 71 composed of a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory is appropriately placed in the drive 60. A program read from the removable medium 71 by the drive 60 is installed in the storage unit 58 as needed. Similar to the storage unit 58, the removable medium 71 can also store various data stored in the storage unit 58.

Through the cooperation between various hardware and various software on the server 2 side of FIG. 2 , the execution of the game on the player terminal 1 can be managed or supported.

In this embodiment, a game in which a plurality of players participate, such as a multiplayer battle game, is considered.

In the game under consideration, a ranking function using various ranking indicators is provided.

Thus, among various ranking indices, the server 2 determines a ranking indices that are suitable for the game style of the player as recommended objects, sorts the recommended objects in descending order of suitability, and presents the recommended objects to the player terminal 1 of the player. This makes it possible to encourage many ordinary players to participate in the ranking.

Here, the premise regarding the relationship between the players and the ranking index in the game of this embodiment will be described.

The first premise is that the selective addition method is adopted in this embodiment.

That is, conventionally, an opt-in method has been employed. In other words, conventionally, the number of ranking index types has been one or relatively few. Therefore, the ranking indexes that a player participates in are automatically determined, the rankings by ranking index are automatically totaled, and it is possible to control whether or not to display data related to the player based on these rankings.

On the other hand, in the game of this embodiment, the number m of ranking index types is as many as hundreds to thousands. Since it is unrealistic for the player to participate in all m types of ranking indexes, the player narrows down the ranking indexes to participate in a few ranking indexes.

In this case, in this embodiment, as a method for determining the ranking index for the player to participate, a method in which the player decides which ranking index he or she wishes to participate, i.e., a selective participation method, is adopted, as opposed to a method of automatically determining the ranking index to be participated in.

The second premise is that in this embodiment, the maximum number of ranking indicators that a player can participate in simultaneously is predefined.

This makes it possible to prevent the number of participants from being biased toward only a specific ranking index. In addition, the number of participants in the ranking index can be easily estimated.

The third premise is that, in this embodiment, in addition to using existing types of ranking indicators, new types of ranking indicators can also be automatically generated.

That is, as described above, in this embodiment, the number m of ranking index types becomes large, that is, from hundreds to thousands. However, it is not realistic to manually set all m types of ranking indexes.

Therefore, the server 2 in this embodiment has a function for automatically generating a ranking index based on existing game data. The method for automatically generating a ranking index will be described later.

Under these conditions, server 2 calculates the relevance of n×m combinations of n (e.g., n = 5 million or more) players and m (e.g., m = hundreds to thousands) types of ranking indicators. Server 2 then sorts the ranking indicators for each of the n players in descending order of relevance and presents the sorted results to each of the player terminals 1-1 to 1-n. Furthermore, server 2 presents the top few ranking indicators with the highest relevance as recommendations to each of the player terminals 1-1 to 1-n.

This allows each of the n players to easily determine a ranking index that suits his or her play style as a ranking index to participate in without having to confirm all of the ranking index groups.

In short, the ranking function in conventional social games, etc. does not have an automatic recommendation function. Therefore, ranking (such as daily ranking and comprehensive ranking, etc.) using only a small number of ranking indicators, that is, one or a few ranking indicators, is provided.

In contrast, in this embodiment, the ranking is improved using m types of various ranking indicators.

Therefore, in this embodiment, the predicted ranking when a player who is not currently participating in the ranking indicator participates in the ranking indicator is used as a level indicating how well a specific ranking indicator suits the player's gaming style.

That is, the server 2 calculates the predicted ranking when a player who is not currently participating in the ranking index participates in the ranking index.

Furthermore, the server 2 sorts the various ranking indices in descending order of predicted ranking (from higher to lower), in other words, in descending order of the likelihood that the player will be ranked higher if the player participates in the ranking indices. Based on this sorting result, the server 2 automatically recommends, for example, the top k ranking indices (an arbitrary integer value such as k=5) to the player as the ranking indices selected, for example, by "auto-selection." That is, the ranking indices selected by "auto-selection" are presented to the player's player terminal 1.

Therefore, for a player, it is easy to participate in a ranking index that is competitive (i.e., expected to be ranked high) without having to confirm all m types of ranking indexes. This can encourage many ordinary players to participate in ranking.

The series of processing steps in the server 2 as described above (i.e., processing steps for calculating the suitability between the ranking indicators and the players (the predicted rank for each ranking indicator), sorting the ranking indicators based on the calculation results, and presenting the predetermined type of ranking indicators as recommended objects to the players) will be referred to as "recommended ranking indicator presentation processing."

FIG. 3 is a functional block diagram showing a functional configuration of the server 2 when executing the recommendation ranking index presentation process.

3 , the CPU 51 of the server 2 has the functions of a ranking participation management unit 101 , a time management unit 102 , a ranking order designation unit 103 , a ranking index recommendation unit 104 , a recommendation result presentation unit 105 , and a ranking index generation unit 106 .

In the area of the storage unit 58 of the server 2, a participation ranking index DB 121, an all-player log DB 122, a ranking result DB 123, a score DB 124, and a ranking index DB 125 are provided.

The ranking participation management unit 101 manages the participation status of the player of the player terminal 1 with respect to the ranking indicator.

For example, the ranking participation management unit 101 performs control to display a user interface as shown in FIG. 4 , ie, a user interface related to the player's participation in the ranking index, on the player terminal 1 of the player.

FIG4(A) shows a user interface 201 for selecting a ranking index for a player to participate in from ranking indexes sorted according to a specific viewpoint.

The user interface 201 includes a selection operation unit 211 as a means for selecting a "viewpoint" for sorting ranking indicators. In the example of FIG4(A), since "in descending order of likelihood" is selected, the plurality of ranking indicators are sorted in descending order of likelihood (with the player being ranked higher).

Here, the process for sorting the likelihood (for the player to be queued higher) in descending order is performed by the ranking index recommendation unit 104 (Figure 3) described later. That is, although it will be described in detail later, in short, the ranking index recommendation unit 104 calculates the predicted rank for the case where the player (the player of interest to be described later) participates in the ranking index (the ranking index of interest to be described later). Here, a higher predicted rank (a smaller value) means a higher likelihood (for the player to be queued higher). Thus, the ranking index recommendation unit 104 sorts the ranking index in descending order of the predicted rank (from low value to high value), in other words, in descending order of the likelihood (for the player to be queued higher).

The ranking index display area 212 displays the ranking result performed by the ranking index recommendation unit 104 as described above.

However, it is difficult to display m types of various ranking indicators on a single screen. Therefore, in the ranking indicator display area 212, a predetermined number of types (20 types in the example of FIG4(A) ) of ranking indicators are displayed in a list according to the sorting order of the viewpoint selected from the selection operation unit 211. That is, in the example of FIG4(A) , of the m types of ranking indicators sorted in descending order of likelihood (for players with a higher likelihood of being ranked), the ranking indicators (icons) of the top 20 types are displayed in sorted order.

More specifically, as will be described later, it is not particularly necessary to sort all m types of ranking indices. However, for the sake of convenience, it is assumed here that all m types of ranking indices are sorted.

As described above, among the m types of ranking indicators, only a limited number of types of ranking indicators (such as approximately 20 types of ranking indicators) can be displayed simultaneously in the ranking indicator display area 212. Therefore, an operation unit 213 for changing the items to be displayed in the ranking indicator display area 212 is also provided.

In addition, an operation unit 214 for executing a keyword search is provided so that the player can search for a desired ranking index by inputting a specific keyword.

FIG4(B) shows a user interface 202 for confirming the ranking index in which the player participates and displaying an explanation of the ranking index.

As described above, in this embodiment, a selective participation method is adopted in which the player himself determines the ranking index to participate in, and the maximum number of ranking indexes to which the player can participate is predefined.

Then, the player can determine, for example, the three types of ranking indices that he or she wishes to participate in by using the user interface 202 in (B) of Figure 4. Specifically, the player presses the ranking participation registration button 221. This confirms the player's participation in the ranking indices.

In this case, explanatory images 222 , 223 , and 224 of ranking indicators for the player to participate in are displayed on the user interface 202 .

Since each ranking indicator has its own aggregate duration, a preferred implementation is such that once a player has enrolled in a particular ranking indicator, the player cannot cancel their registration for the ranking indicator during the aggregate duration of the ranking indicator (e.g., 24 hours or a month).

Here, the three types of ranking indicators represented by the explanatory images 222, 223 and 224, respectively, can be ranking indicators selected by the player from the ranking indicator display area 212 in the user interface 201 shown in (A) of Figure 4, or can be ranking indicators automatically selected by the server 2 as a result of pressing the automatic selection button 226 shown in (B) of Figure 4.

When the latter automatic selection button 226 is pressed, the above-mentioned recommended ranking indicator presentation processing is performed, and among the ranking indicators suitable for the player, for example, the top three types of ranking indicators, that is, the three types of ranking indicators in which the player has a higher possibility of being ranked higher, are recommended, and explanatory images 222, 223 and 224 showing explanations of each ranking indicator are automatically displayed in the user interface 201.

This allows the player to easily find their competitive ranking index without having to confirm all m types of ranking indexes.

The explanatory images 222 , 223 , and 224 are images each including a duration, a sorting condition, and a current ranking for each ranking index.

For example, explanatory image 222 is an image showing an explanation of the daily ranking, and includes the explanatory content "Daily, (Ranked based on the cumulative damage points inflicted by using water-attribute weapons/abilities) Water-attribute weapon user, 27th place." That is, the ranking index illustrated in explanatory image 222 uses the cumulative damage inflicted by the player using water-attribute weapons and abilities over the past 24 hours as a score to calculate the ranking.

Here, the reward explanation image 225 is an image for explaining the content of the reward that can be obtained when the player participates in the ranking index.

That is, by using the user interface of Figure 4, the player can register the ranking index of the player with a higher possibility of being ranked higher as the ranking index to be participated in, and obtain rewards when playing the game. Giving rewards according to the order of ranking as described above can enhance the motivation of the player.

The above is summarized as follows. The server 2 in this embodiment can provide each player with m types of ranking indicators, which are much more than before, and strongly encourages players to participate in the ranking indicators among these ranking indicators that are suitable for the game style of each player.

That is, by providing each player with m types of ranking indicators, which are far more than before, even ordinary players can have the opportunity to be ranked higher according to their gaming style, that is, ranking indicators that suit the gaming style of each player.

However, it is difficult for each player to blindly find a ranking index that suits his or her game style from among the m types of ranking indexes, which are much larger than before.

Thus, the server 2 in this embodiment sorts the m types of ranking indicators, for example, in order of suitability for the player's game style (in other words, in order of how high the player is expected to be ranked (get a higher ranking) if a player with such a game style participates in the ranking indicator). Note that, as will be described later, the number of types to be sorted does not necessarily have to be m.

The ranking result is presented to the player via the user interface 201 in (A) of Figure 4. Thus, the player can easily and immediately find the ranking index that is expected to be ranked higher according to his or her game style from among the m types of various ranking indexes.

In addition, when the player simply presses the automatic selection button 226 in (B) of Figure 4, several ranking indicators that suit the player's style are automatically recommended from m types of various ranking indicators. In other words, when a player with such a gaming style participates in the ranking indicator, several ranking indicators are expected to rank higher (obtain a higher ranking) for the player.

In this way, each player can easily and immediately select a ranking index that is expected to be ranked higher according to his or her game style from among m types of various ranking indexes. This makes it possible to encourage many ordinary players to participate in ranking.

Referring back to FIG. 3 , the participation ranking index DB 121 stores the respective players of the player terminals 1 - 1 to 1 - n and the ranking indexes in which the players participated in association with each other.

FIG. 5 shows an example of the structure of the participation ranking index DB 121 .

In the ranking index participation database 121 shown in FIG5 , a user ID uniquely identifies each player. A ranking index ID uniquely identifies a ranking index. By associating the user ID and ranking index ID in the same row, it is possible to manage the compatibility between a player and the ranking index in which the player participates.

The "Begin" field in a particular row indicates the start time for collecting scores for the player in that row's ranking for that row's ranking indicator. The "End" field in that row indicates the end time for this collection. The player in that row's ranking indicator score is calculated based on the game logs recorded during the time period specified by the "Begin" and "End" values. Although details will be explained later, a player's score for a particular ranking indicator is used to determine the player's ranking within that ranking indicator.

Referring back to FIG. 3 , the time management unit 102 controls the frequency of totaling the points required to determine the ranking for each ranking indicator.

For example, in order to notify players of intermediate ranking results, it is preferable to update the ranking several times a day, even in the case of daily ranking. Therefore, the time management unit 102 activates the ranking order specifying unit 103, described later, at specific time intervals (specifically, for example, every four hours for daily ranking). That is, the ranking order specifying unit 103 is activated at specific time intervals based on the management of the time management unit 102, and performs processing such as point aggregation (the details of this processing will be described later).

Although not shown in FIG. 3 , the player may be notified of the total frequency managed by the time management unit 102 by displaying, for example, “remaining time to next update: 2 hours” on the player terminal 1 via the ranking participation management unit 101 .

Here, the time management unit 102 is not an essential component, and the ranking order specifying unit 103 can perform ranking aggregation in real time. However, as in this embodiment, by providing the time management unit 102 and updating the ranking at specific time intervals without performing ranking aggregation in real time, the load imposed on the system of the server 2 due to ranking aggregation can be reduced.

The ranking rank specifying unit 103 refers to the participation ranking index DB 121, calculates the score of each ranking index in which a player to be the target of processing (hereinafter referred to as "focused player") participates, and specifies a rank based on the score.

Specifically, for example, the ranking order specifying unit 103 extracts the game log of the player of interest from the all-player log DB 122 .

The ranking order specifying unit 103 sequentially sets each ranking index among the ranking indexes in which the player of interest participates as a ranking index to be processed (hereinafter referred to as "ranking index of interest"), and repeats the following series of processing steps for each ranking index.

The ranking order specifying unit 103 obtains a counting method for focusing on the ranking index from the ranking index DB 125 .

The ranking order specifying unit 103 calculates the score of the player of interest for the ranking indicator of interest based on the game log of the player of interest according to the obtained score aggregation method, and stores the calculated score in the score DB 124 .

The ranking order specifying unit 103 specifies the ranking of the focused player with respect to the focused ranking index based on the score.

That is, by sequentially setting each player of the player terminals 1-1 to 1-n as a focused player and sequentially setting each ranking indicator in one or more ranking indicators in which the focused player participates as a focused ranking indicator, the score and ranking of each ranking indicator in one or more ranking indicators in which each player participates are calculated for each player.

The all-player log DB 122 stores game logs recorded during the game for each player of the player terminals 1-1 to 1-n, for example, logs of all activities related to ranking (such as item usage history and battle history in the game). A specific example of the structure of the all-player log DB 122 will be described later with reference to Figures 6 to 8.

The ranking index DB 125 stores, for each of the m types of ranking indexes, various information such as a point totaling method, etc. A specific example of the structure of the ranking index DB 125 will be described later with reference to FIG9 and FIG10 .

The ranking result DB 123 is a buffer database for storing intermediate calculation results of the ranking totals performed by the ranking order specifying unit 103 .

That is, in this embodiment, since the entire player log DB 122 becomes a large database containing many game logs, it is not feasible to perform ranking aggregation on demand. Therefore, the intermediate ranking aggregation results at specific time intervals are stored in the ranking result DB 123. When performing score aggregation, the ranking order specifying unit 103 calculates the current score aggregation result by extracting the previous aggregation result from the ranking result DB 123 and adding the difference to the previous score aggregation result.

This makes it possible to significantly reduce the load caused by score aggregation.

Specifically, for example, assuming that 100,000 game logs need to be aggregated per minute, no ranking result DB 123 is provided, and no intermediate results are cached, in this case, since the aggregation cost increases in proportion to time, 8.64 billion game logs need to be aggregated simultaneously within 24 hours.

On the other hand, in the present embodiment providing the ranking result DB 123, for example, when intermediate calculation results obtained every hour are cached and reused, aggregation of 360 million game logs only needs to be performed 24 times in 24 hours.

The ranking index recommendation unit 104 includes a predicted ranking calculation unit 111 and a recommended ranking index determination unit 112 .

For the player of interest, the predicted ranking calculation unit 111 calculates, for at least some of the m types of ranking indices, the ranking of the player of interest when participating in each ranking index as the predicted ranking.

The recommended ranking index determination unit 112 sorts the ranking indices based on the predicted order for each ranking index, and determines a ranking index to be recommended to the interested gamer as a recommended ranking index.

The recommendation result presentation unit 105 presents the ranking index sorting result and the recommended ranking index to the player terminal 1 of the player of interest.

It should be understood that there are no particular limitations on the timing and method of presenting the ranking results and recommending the ranking indicators.

In this embodiment, for example, in the user interface 201 of FIG. 4(A) , the ranking index display area 212 displays the ranking index sorting result when “in descending order of likelihood” is selected in the selection operation unit 211 .

In addition, for example, the timing at which the explanation contents for the recommended ranking indices are displayed as the explanation images 222 , 223 , and 224 is when the automatic selection button 226 in FIG. 4 is pressed.

The ranking order specifying unit 103 , the ranking index recommending unit 104 , and the recommendation result presenting unit 105 will be described in further detail below.

As described above, the ranking order designation unit 103 repeatedly performs the process of calculating the scores of one or more participants participating in the specific ranking indicator according to the score calculation method (score aggregation method) predefined for the specific ranking indicator for each of the m types of ranking indicators, and designates the respective ranks of the one or more participants based on these scores.

The score calculated for each participant is stored in the score DB 124 .

That is, the score DB 124 stores the score history of past participants for each of the m types of ranking indicators.

Then, the predicted ranking calculation unit 111 sequentially sets at least some of the m types of ranking indices as the ranking index of interest, and repeats the subsequent series of processing steps.

That is, the predicted ranking calculation unit 111 sets the assumed scores of the 1st to nth places assumed as the indices of the predicted ranking based on a plurality of past scores in the focus ranking index.

The predicted ranking calculation unit 111 calculates the current score of the player of interest according to a score calculation method (score aggregation method) predefined for the ranking index of interest.

The predicted rank calculation unit 111 calculates the predicted rank of the attention player based on the assumed scores of the 1st to nth places and the current score of the attention player.

The calculation method of the predicted order of the player of interest described in this paragraph will be described in more detail below with reference to FIGS. 6 to 12 .

The ranking index recommendation unit 104 and recommendation result presentation unit 105 are characterized by automatically calculating the compatibility between the player's gaming style and each ranking index based on the player's predicted ranking. Specifically, ranking indexes with higher predicted rankings are more compatible with the player's gaming style.

Using this feature as a premise, the relationship between the game log used as basic data for ranking and the ranking index will be explained.

FIG6 shows an example of the structure of a general log database (hereinafter referred to as “log structure”).

The log structure shown in FIG. 6 is a data structure corresponding to one entry of the entire player log DB 122 ( FIG. 3 ).

That is, the entire player log DB 122 may be defined as a collection of logs having the log structure of FIG. 6 .

The log structure shown in Figure 6 consists of three elements: event_type, timestamp, and contents. Event_type is a string indicating the type of event recorded in the log. Timestamp is a timestamp indicating the time when the event corresponding to the log occurred. Contents is a substructure that defines the content of the log information. The content of the log information is defined as log information consisting of an arrangement of key and value groups.

FIG. 7 shows a specific example of an entry of a log having the log structure of FIG. 6 .

In the example of FIG7 , event_type is battleAttack, and Timestamp (the time of the event corresponding to the log that occurred) is defined as 2015/02/02 21:36:59:23.

Furthermore, the content of the event is defined as an attribute of the content substructure as follows: the character is player character 1, the characterAttribute is water attribute, the targetEnemy is enemy monster 2, and the given damage is 2100.

Attributes included in the content substructure change according to the attribute value of event_type of the log structure.

Fig. 8 shows a specific example of an entry in the log having the log structure of Fig. 6. This entry is generated when a player recovers the physical strength of his character by using recovery magic or recovery items.

In the example of FIG8 , event_type (event type) is battleHeal (battle recovery) and timestamp (the time of the event corresponding to the log occurrence) is defined as 2015/02/02 21:37:11:43.

Furthermore, the content of the event is defined as an attribute of the content substructure as follows: the character is player character 2, characterAttribute is the light attribute, the target is player character 4, the method is the recovery magic, and the recovery amount is 1000.

In the invention proposal of the above-mentioned log structure shown in FIG. 6 to FIG. 8 , an example is shown in which log information is defined as a flexible structure whose structure can be identified by using a character string of event_type.

As an implementation different from this example, an implementation method may be used in which all attributes are listed in advance and associated with table attributes in a relational database.

By adopting such a log structure, sorting and summarizing can be realized as an operation for extracting entries to be summarized from the logs accumulated in the database (all player log DB 122 in FIG. 3 ) on the server 2 side providing the game apparatus.

As a specific example of this implementation, as described below, the ranking index can be defined as a data structure including three pieces of information.

Ranking index = {total duration, target score, reward information}

The total duration is data indicating duration such as daily, monthly, and total (accumulation of all durations).

The target score is data indicating a score to be totaled (for example, "damage points given by using water-attribute weapons/abilities").

Reward information is a reward given to a player when the player ranks higher (e.g., ranks among the top three players). For example, "obtaining a recovery item A at the start of a battle" may be considered as a reward.

That is, the score calculation method (score aggregation method) predefined for the above ranking index is a calculation method using these three pieces of information.

FIG. 9 shows an example of implementation of the ranking index, that is, an example of a structure for defining the ranking index (hereinafter referred to as a “ranking structure”).

That is, the ranking index can be defined as a ranking structure as shown in FIG9 .

In the example of Figure 9 , among the three pieces of information that make up the ranking indicator, the total duration is represented by duration_begin and duration_end. duration_begin is a 64-bit integer indicating the start time of the object's total duration. duration_end is a 64-bit integer indicating the end time of the object's total duration.

In addition, among the three pieces of information that make up the ranking index, the target score is defined by targets and queries. That is, the attributes to be aggregated are defined as the arrangement of the attribute names of the log structure described as examples with reference to Figures 6 to 8. Here, the notable feature of the ranking structure in Figure 9 is that the conditions for the logs to be aggregated are specified by using pairs consisting of attribute names (keys) and values (values), which are generally known as key-value pairs. The three substructures used to define these key-value pairs are query structures.

Specifically, for example, when "damage points given by using water attribute weapons/abilities" are to be totaled, the condition is "water attribute weapons/abilities", and thus a ranking index having a ranking structure defined as a query structure as shown in FIG10 is used.

FIG. 10 shows a specific example of one entry for the ranking index having the ranking structure in FIG. 9 .

In the example of Figure 10, in the query structure, event_type = battleAttack specifies that logs recorded during battles should be aggregated. Furthermore, characterAttribute = water attribute specifies that actions performed using water-attribute weapons or abilities should be aggregated. Furthermore, targets: {damage} specifies that the damage dealt to enemies under the above conditions should be aggregated.

By using the ranking index having the ranking structure of Figure 10, the ranking order designation unit 103 and the predicted order calculation unit 111 are able to total only the "damage points given by using water attribute weapons/abilities" based on the game logs accumulated in all player log DB 122 (Figure 3) when calculating the score of the ranking index.

The predicted ranking calculation unit 111 of the ranking index recommendation unit 104 calculates the ranking, that is, the predicted ranking that the player of interest with the game log having the above-mentioned log structure shown in Figures 6 to 8 expects to obtain when the player participates in the ranking index of interest with the ranking structure shown in Figures 9 and 10.

The predicted ranking calculation unit 111 sequentially sets each ranking indicator of at least some of the m types of ranking indicators as the focus ranking indicator, and repeats the above processing steps, thereby calculating the predicted ranking of at least one ranking indicator of the m types of ranking indicators for the focus player.

Then, the recommended ranking index determination unit 112 sorts the ranking indices in descending order of predicted order, ie, in descending order of possibility of being ranked higher, for example, and determines a ranking index to be recommended to the interested player based on the sorting result.

FIG. 11 is a diagram for explaining an example of a calculation method of the predicted ranking of the player of interest (player A) in a case where a ranking index in which the player of interest has not yet participated is set as the ranking index of interest.

FIG11(A) shows the temporal transition of the scores of the ranking participants when the predicted ranking for the ranking index of interest is calculated (hereinafter referred to as “current”).

As shown in FIG11(A), the score required to obtain a specific ranking (e.g., 1st) in the focus ranking index varies depending on the timing of the aggregation. Therefore, in order to estimate the ranking that a specific player can obtain in the focus ranking index, it is necessary to estimate the score required for each ranking from 1st to nth.

Although there is no particular limitation on the method of calculating the estimate as long as the method uses a time series of past score logs, in this embodiment, a method of calculating the median value of score logs corresponding to respective ranks within a predetermined time range in the past is employed.

Score logs for respective ranks (players who acquired these ranks) within a predetermined time range in the past are stored in the score DB 124 ( FIG. 3 ) having a structure as shown in, for example, FIG. 12 .

FIG. 12 shows an example of time series information of past score logs related to the ranking index of interest.

As shown in FIG. 11(B) , the predicted ranking calculation unit 111 calculates the median value for each of the first to nth rankings based on the score history of past participants who focus on the ranking index.

Specifically, for example, the predicted ranking calculation unit 111 performs a process for converting the time series information of the score logs of each ranking as shown in FIG12 from the score DB 124 into an arrangement of the median values of each ranking as shown in FIG11(B). Hereinafter, the arrangement of these median values will be referred to as "hypothetical score p".

Next, the predicted rank calculation unit 111 extracts the game log of the focused player (player A in the example of FIG. 11 ) from the all-player log DB 122 , and calculates a score corresponding to the focused ranking index based on the game log.

The calculation method of the score is the same as the calculation method in the ranking order specifying unit 103. As described above, this embodiment adopts a method for calculating the score corresponding to the ranking index of interest having the ranking structure shown in Figures 9 and 10 by using the game log having the log structure shown in Figures 6 to 8.

It should be understood that the score of the player of interest corresponding to the ranking index of interest calculated as described above is referred to as a "current score c".

In addition, the predicted order calculation unit 111 compares the magnitude relationship between the 1st to nth assumed scores p (see FIG. 11(B) ) and the current score of the player of interest (the score α of player A in the example of FIG. 11 ).

The predicted rank calculation unit 111 calculates a rank that is greater than the assumed score p and minimizes the difference between the assumed score p and the current score c (c>p∧MIN(c-p)) as the predicted rank of the player of interest.

A series of processing steps of the predicted ranking calculation unit 111 as described above are performed every time each ranking index of at least one of the m types of ranking indexes is sequentially set as a ranking index of interest.

As a result, the predicted ranking of the player of interest is calculated for each ranking indicator of at least one of the m types of ranking indicators.

Here, "each ranking indicator among at least one ranking indicator of m types of ranking indicators" means, for example, excluding the ranking indicators in which the player being followed has been ranked first from the candidates for ranking indicators recommended to the player being followed, thereby allowing new ranking indicators to always be presented to the player being followed as recommended ranking indicators.

That is, it is not particularly necessary to calculate the predicted ranking for all m types of ranking indices. For example, the ranking indices for which the player of interest is already ranked first may be excluded from the ranking indices for which the predicted ranking is to be calculated.

The recommended ranking index determination unit 112 sorts the ranking indices in descending order of prediction order (from high order to low order), and determines a recommended ranking index based on the sorting result.

As described above, the server 2 in the present embodiment executes processing for realizing the ranking function using m types of various ranking indices related to processing for determining the recommendation ranking index.

Therefore, it is necessary to design the system in consideration of the load during operation of the server 2 .

In this regard, the server 2 in this embodiment has the following characteristics: the calculation cost for each ranking indicator is much lower than, for example, the calculation cost of a conventional server provided with a conventional ranking function.

This feature is achieved because the number of ranking index types m is huge, and different ranking indicators need to be presented to each player as recommended ranking indicators, whereby the number of participants in the ranking index becomes smaller than the number of participants in the conventional comprehensive ranking.

With this feature, when performing calculations related to ranking indices, the number of databases to be referenced becomes smaller, thereby making it possible to significantly reduce the load due to log aggregation in a distributed database environment.

A specific example of log total cost is given below.

That is, for example, the cost when the daily ranking is updated every hour in a case where the number of active players is 1 million and each player participates in 5 ranking indices will be examined.

Assumption: Each player leaves an average of 100 log entries per hour.

In this case, the log total cost will be 100 (number of logs) × 5 (number of participants in the ranking index) × 1 million (number of players) = 500 million times of addition processing.

This adding process may be performed for each player, and may be realized as a totaling process in one database (corresponding to the entire player log DB 122 in FIG. 3 of the present embodiment) even when the log database is dispersed.

In addition, as described above, the intermediate total results are stored in the ranking result DB 123. This makes it possible to always calculate the latest order (rank) simply by performing difference calculations.

As described above, in this embodiment, among m types of various ranking indices, a ranking index suitable for the player's gaming style is presented to the player as a recommended ranking index.

However, manually defining all m types of ranking indices is cumbersome.

Therefore, as shown in FIG. 3 , the server 2 is provided with a ranking index generating unit 106 having a function for automatically generating a new ranking index.

Specifically, the ranking index generating unit 106 may automatically generate new ranking indexes based on a small number of templates as shown in FIG. 13 to FIG. 15 .

FIG. 13 shows an example of a template for automatically generating ranking indicators related to in-game damage.

In the template of the example of FIG. 13 , %ATRRIBUTES% is expanded according to the type of attribute information in the game. That is, a different ranking index is generated for each attribute.

FIG. 14 is an application example different from the example of FIG. 13 , that is, an example of a type of template in which a player who has achieved control of a designated character is ranked higher.

In the template of the example of FIG. 14 , %CHARACTERS% is expanded according to the type of the player character appearing in the game. That is, a different ranking index is generated for each character.

FIG. 15 is an application example different from the examples of FIG. 13 and FIG. 14 , that is, an example of a template for automatically generating a ranking index using a score not related to injury.

In the example of FIG. 15 , the ranking index is generated by targeting recovery events occurring during battle as specified in query:{event_type=battleAttack} and totaling the amount of HP after recovery as specified in targets:{heal}.

By providing only a small number of templates as shown in FIG. 13 to FIG. 15 , etc., various ranking indicators can be easily and automatically generated based on information already held by the gaming system.

The functional structure of the server 2 has been described above.

Next, among the processes executed by the server 2 having such a functional structure, a process for presenting the recommendation ranking indices will be described with reference to FIGS. 16 and 17 .

FIG. 16 is a flowchart for explaining the flow of the ranking recommendation process executed by the server 2 .

The ranking recommendation process is triggered at a predetermined timing (e.g., when the automatic selection button 226 in FIG. 4B is pressed on a predetermined player terminal of the player terminals 1-1 to 1-n). In this process, the predetermined player (e.g., the player who pressed the automatic selection button 226) is set as the player of interest, and the following series of processing steps are executed.

In step S1 , the predicted rank calculation unit 111 of the ranking index recommendation unit 104 in FIG. 3 calculates a predicted rank for each ranking index of at least one ranking index of m types of various ranking indexes.

This type of processing in step S1 is hereinafter referred to as "prediction ranking calculation processing."

FIG. 17 is a flowchart for explaining an example of details of the prediction order calculation process.

In step S21 , the predicted rank calculation unit 111 excludes ranking indices satisfying predetermined conditions from among the m types of ranking indices to be recommended.

It should be understood that in the case where all m types of ranking indicators can be recommended objects, the processing in step S21 is unnecessary.

In addition, the predetermined condition for excluding the ranking index from the recommendation object is not particularly limited and can be arbitrarily set by the game designer, the administrator of the server 2, etc. For example, as described above, the condition that the player of interest is already ranked first in the ranking index can be adopted.

In step S22 , the predicted order calculation unit 111 sets a specific ranking index other than the ranking indexes excluded from the m types of ranking indexes in step S21 as a ranking index of interest.

In step S23 , the predicted order calculation unit 111 calculates the hypothesis scores p for the 1st to nth ranking indicators of interest.

In step S24 , the predicted ranking calculation unit 111 calculates the current score c of the focused player with respect to the focused ranking index.

In step S25 , the predicted ranking calculation unit 111 calculates the predicted ranking of the focused player in the focused ranking index based on the assumed score p and the current score c.

In step S26 , the predicted order calculation unit 111 determines whether all ranking indices other than the ranking indices excluded from the m types of ranking indices in step S21 have been set as the ranking indices of interest.

In a case where there is a ranking index that has not been set as the ranking index of interest, the determination in step S26 is “NO”, the process returns to step S22 , and the subsequent steps are repeated.

That is, for each of the m types of ranking indices except the ranking indices excluded in step S21 , the loop processing through steps S22 to S26 is repeated, and the predicted ranking of the player of interest is sequentially calculated.

As a result, since all ranking indices have been set as the ranking indices of interest, the determination in step S26 is “Yes”, and the predicted ranking calculation process is terminated. The process then proceeds from step S1 in FIG16 to step S2.

In step S2 , the recommended ranking index determining unit 112 of the ranking index recommending unit 104 in FIG. 3 determines a recommended ranking index from at least a portion of the m types of various ranking indexes based on the predicted order.

In step S3 , the recommendation result presentation unit 105 presents the recommended ranking index to the player terminal 1 of the player of interest.

Then, the recommendation ranking index presentation process is terminated.

Although the embodiments of the present invention have been described above, it should be noted that the present invention is not limited to the above embodiments, and modifications, improvements, and the like within the scope that can achieve the objects of the present invention are encompassed in the present invention.

For example, the functional structure of Figure 3 is merely an example, and there are no particular limitations to this example. Specifically, it is sufficient for the information processing system to be equipped with functions that enable the execution of the series of processing steps described above as a whole, and the structure of the functional blocks used to implement these functions is not particularly limited to the example in Figure 3. Furthermore, the locations of these functional blocks are not particularly limited to those shown in Figure 3 and can be arbitrarily set. For example, functional blocks of server 2 can be transferred to terminal 1, etc., and conversely, functional blocks of terminal 1 not shown in Figure 3 can be transferred to server 2, etc.

Furthermore, each functional block may be implemented only by hardware, only by software, or by a combination of hardware and software.

When each functional module is executed by software, a program constituting the software is installed on a computer or the like via a network or from a recording medium.

The computer may be a computer embedded in dedicated hardware. Alternatively, the computer may be a computer capable of executing various functions when various programs are installed on the computer, such as a server, a general-purpose smart phone, or a personal computer.

The recording medium including such a program is realized by a removable medium (not shown) distributed separately from the main body unit of the device in order to provide the program to the player, or a recording medium embedded in the main body unit of the device provided to the player.

In this specification, the steps described in the program recorded on the recording medium may include not only processes that are sequentially executed in chronological order but also processes that are not sequentially executed in chronological order but are executed in parallel or separately.

Furthermore, in this specification, the term "system" should be interpreted as meaning an entire apparatus composed of a plurality of devices or a plurality of components or the like.

In other words, the server in the information processing system to which the present invention is applied is the server 2 including the embodiment of FIG. 2 or 3 described above, and various forms of embodiments having structures described later may be employed.

That is, the server to which the present invention is applied (for example, the server 2 in FIG. 1 ) is the following server.

The server communicates with a plurality of terminals (e.g., player terminals 1-1 to 1-n in FIG. 1 ) capable of executing a game by accepting operations respectively performed by a plurality of players, manages a plurality of types of ranking indicators in which at least one of the plurality of players participates as a participant, and calculates a ranking of the participant for each of the plurality of types of ranking indicators. The server includes:

a prediction unit (e.g., the predicted ranking calculation unit 111 in FIG. 3 ) configured to set a player to be processed as a target player among the plurality of players, and calculate, for at least a portion of the plurality of types of ranking indices, a ranking of the target player when participating in each ranking indicator as a predicted ranking;

a determination component (e.g., the recommended ranking index determination unit 112 in FIG. 3 ) for determining a ranking index to be recommended to the interested player as a recommended ranking index based on the predicted ranking; and

The presentation component (eg, the recommendation result presentation unit 105 in FIG3 ) is configured to present the recommended ranking indicator to the terminal of the concerned player.

This makes it possible to provide each player with ranking indices of far more types (m types) than before, and strongly encourages the player to participate in the recommended ranking indices among these ranking indices.

That is, by providing each player with far more types (m types) of ranking indicators than before, it is possible to provide even ordinary players with the opportunity to be ranked higher according to their gaming style, that is, ranking indicators that suit the gaming style of each player.

However, it is difficult for each player to find a ranking index that suits his or her game style from among the far more types (m types) of ranking indexes than before.

Thus, the server using the present invention automatically determines a ranking index that is suitable for the gamer's gaming style (in other words, a ranking index that would predictably rank the player higher (or obtain a higher ranking) if the player were to participate in the ranking index) as a recommended ranking index. The server then presents the recommended ranking index to the terminal of the gamer.

That is, each player can be presented with a ranking index that suits their gaming style from the server as a recommended ranking index without having to search for it themselves. This allows each player to easily and immediately select a ranking index that they are expected to rank higher for based on their gaming style from among m types of various ranking indexes.

This makes it possible to incentivize many regular gamers to participate in ranked games.

In addition, the server to which the present invention is applied can be realized at a lower operating cost than before.

In other words, the computational cost for each ranking indicator is much lower than that for conventional ranking. This is because the number of ranking indicator types is much greater than before, and different ranking indicators are recommended to each player. As a result, the number of players participating in each ranking indicator is smaller than that of conventional comprehensive ranking.

Thus, when performing calculations related to ranking indices, the number of databases to be referenced becomes smaller, thereby enabling a significant reduction in the load due to log aggregation in a distributed database environment.

Furthermore, the various new ranking metrics managed by the server employing the present invention do not interfere with the existence of traditional comprehensive ranking. That is, while a variety of ranking metrics are used to allow selection of ranking metrics based on the various playing styles of ordinary players, even those ordinary players have the opportunity to be ranked higher, traditional comprehensive ranking can still be used. Thus, the playing styles of existing high-ranking players are not hindered.

That is, it is possible to encourage many ordinary players to participate in the ranking, and if the ranking itself receives more attention, it will also encourage existing players with higher rankings in the traditional comprehensive ranking.

Therefore, ranking using various ranking indicators managed by the server to which the present invention is applied and traditional comprehensive ranking are in a complementary relationship.

In addition, ranking using various ranking indicators managed by the server to which the present invention is applied provides high playability.

That is, as described above, the server of the present invention automatically recommends ranking indicators based on the different game styles of each player, which makes it easy for each player to select a ranking indicator that suits their game style from a variety of ranking indicators.

This makes it possible to prevent a large number of participants from being biased towards only a specific ranking indicator.

Furthermore, ranking using various ranking indicators managed by the server to which this invention is applied enables ranking from the specific perspective of "users of Character A" among the many characters that appear in the game. This allows evaluation of superior play styles with specific unique characteristics, such as the heavy use of "Character A," to be performed. As a result, the effect of increasing the diversity of ways to enjoy the game title is achieved.

In addition, various ranking indices managed by the server to which the present invention is applied may be set, for example, for each type of character or item.

Specifically, for example, various ranking indicators can be set, such as "user of water attribute weapons (based on the ranking of experience points obtained when the player is equipped with water attribute weapons)", "control of character A (based on the ranking of the cumulative damage given to the enemy by using character A)", "control of recovery skill B (based on the ranking of the cumulative HP recovered by using recovery skill B)", etc.

Furthermore, these ranking indicators can be combined to easily define new ranking indicators. For example, you can define "control of the coordination between characters C and D (based on the cumulative ranking of damage dealt to the enemy when characters C and D are organized in the same camp)."

It is also possible to associate so-called game videos with rankings using various ranking indices managed by a server to which the present invention is applied.

In this case, it is possible for each player to immediately obtain a video of an "expert having a game style that the player is interested in" and compete with each other.

Furthermore, for example, by providing a method for viewing game videos of higher-ranked players in a ranking with increased diversity from the ranking user interface (see FIG4 ), a new way of enjoying the game can be created. For example, the excellent playing style of the player ranked first in "User of Character A" can be learned by other players using the same character.

Here, the determining component determines one or more ranking indicators whose predicted order of ranking indicators meets a predetermined condition as the recommended ranking indicators, and

The presentation unit sorts the one or more recommended ranking indices in descending order of the predicted ranking and presents the recommended ranking indices to the interested player.

As described above, one or more types of recommended ranking indicators are presented to interested players in the recommended order. Thus, each player can easily and immediately select a suitable ranking indicator that the player expects to be ranked higher based on his or her gaming style from among the m types of various ranking indicators.

In addition, the server to which the present invention is applied further includes a designation component (e.g., the ranking order designation unit 103 in FIG3 ), which repeats the following processing for each of the plurality of types of ranking indicators: calculating each score of one or more participants participating in the specific ranking indicator according to a score counting method predefined for the specific ranking indicator, and designating a ranking of each of the one or more participants based on each score;

The prediction component may perform the following operations:

setting a ranking indicator to be processed as a focus ranking indicator among the plurality of types of ranking indicators;

Setting assumed scores for the first to nth ranks assumed to be indicators for predicting the ranking based on a plurality of past scores in the focus ranking indicator (e.g., step S23 in FIG. 17 );

Calculating the current score of the player of interest according to a score calculation method predefined for the ranking indicator of interest (eg, step S24 in FIG. 17 ); and

The predicted ranking of the attention player is calculated based on the assumed scores of the 1st to nth places and the current score of the attention player (for example, step S25 in FIG. 17 ).

This makes it possible to accurately calculate the predicted ranking of the player of interest, and as a result, makes it possible to present a more suitable recommended ranking index to the player of interest.

Furthermore, the prediction unit excludes ranking indices that satisfy a predetermined condition from among ranking indices in which the player of interest has participated in the past, from among the ranking indices of interest.

Here, the predetermined condition for excluding a ranking indicator from the candidates for the recommended ranking indicator (focus ranking indicator) is not particularly limited and can be arbitrarily set by the game designer, server administrator, etc. For example, the condition that the focus player is already ranked first in the ranking indicator can be adopted.

By adopting such conditions, it is possible to present the ranking index that the interested player is not yet ranked first but has a high probability of being ranked higher (has a chance of being ranked higher) as a recommended ranking index to the interested player. As a result, the interested player will be encouraged to play the game.

Description of Reference Numerals

1,1-1～1-n Gamer Terminal

2 Servers

51 CPU

101 Qualifying Participation Management Unit

102 Time Management Unit

103 Ranking Order Designation Unit

104 Ranking Index Recommendation Unit

105 Recommendation Result Presentation Unit

106 Ranking Index Generation Unit

111 Prediction ranking calculation unit

112 Recommended ranking indicator determination unit

121 Participation in Ranked Index DB

122 All Player Logs DB

123 Qualifying Results DB

124 Score DB

125 Ranking Index DB

Claims (5)

1. A server for communicating with each of a plurality of terminals capable of executing a game by accepting actions performed by a plurality of players, managing a plurality of ranking metrics in which at least one of the plurality of players participates, and calculating the participant's ranking for each of the plurality of ranking metrics, the server comprising: A prediction component is used to set the player to be processed among the plurality of players as the focus player, and to calculate the ranking of the focus player as the predicted ranking for at least a portion of the ranking indicators of the plurality of types of ranking indicators. The determining component is used to determine, based on the predicted ranking, the ranking metric to be recommended to the interested gamer as the recommended ranking metric; and A presentation component is used to display the recommended ranking indicators to the terminal of the user who is interested in the game. The server uses multiple game logs from one or more games to automatically determine the game style of the player being followed, and the prediction component calculates the player's ranking in each ranking indicator based on the game style of the player being followed as the predicted ranking. 2. The server according to claim 1, wherein the determining component determines one or more ranking indicators that satisfy predetermined conditions for the predicted ranking as the recommended ranking indicator, and The presentation component sorts one or more of the recommended ranking indicators in descending order of the predicted ranking and presents the recommended ranking indicators to the interested gamer. 3. The server according to claim 1 or 2, wherein the server further comprises a designated component, the designated component being configured to repeat the following processing for each of the plurality of ranking indicators: calculating the score of one or more participants participating in the specific ranking indicator according to a predefined score calculation method for the specific ranking indicator, and assigning the respective ranking of the one or more participants based on their respective scores. The prediction component performs the following operations: Set the ranking indicator that is to be processed from among the multiple types of ranking indicators as the ranking indicator of interest; Based on multiple past scores in the aforementioned ranking indicators, assume scores for the 1st to nth positions of the indicator to be used as the predicted ranking are set. The current score of the player being followed is calculated according to a predefined score calculation method for the aforementioned ranking metric; and The predicted ranking of the player is calculated based on the assumed scores from the 1st to the nth position and the current score of the player. 4. The server according to claim 3, wherein the prediction component excludes from the ranked indicators that meet predetermined conditions from the ranked indicators that the player has previously participated in. 5. A non-transitory computer-readable medium storing a program for causing a computer to perform control processing, the computer being configured to control a server, the server being configured to: communicate with each of a plurality of terminals capable of executing a game by receiving actions from a plurality of players; manage a plurality of types of ranking indicators in which at least one of the plurality of players participates; and calculate the participant's ranking for each of the plurality of types of ranking indicators, the control processing comprising: The prediction step is used to set the player to be processed among the plurality of players as the focus player, and to calculate the ranking of the focus player as the predicted ranking for at least a portion of the ranking indicators of the plurality of ranking indicators. The determination step is used to determine, based on the predicted ranking, the ranking indicator to be recommended to the interested gamer as the recommended ranking indicator; and The presentation step is used to present the recommended ranking indicator to the terminal of the player who is interested in the game. The server uses multiple game logs from one or more games to automatically determine the game style of the player being followed. In the prediction step, the ranking of the player being followed in each ranking indicator is calculated based on the game style of the player being followed, and this ranking is used as the predicted ranking.