System, method, and program for generating a game media pack

Abstract

An object is to provide a system capable of generating a game medium group for a more accurate recommendation. A system of one embodiment of the present invention is used for generating a game medium group for clearing one unit of a game in a game using a game medium group composed of a plurality of game media each having a medium state level, and for each game medium, determines a mode of medium state levels of one game medium calculated from game medium groups that have cleared one unit of a game and medium state levels included in a game log as an estimated level of the medium state level of the one game medium of one unit of a game, and generates a game medium group in which each game medium included in a game medium group that has cleared one unit of a game included in a game log is provided with the determined estimated level of the medium state level.

Description

Technical Field

[0001] This invention relates to a system, method, and program for generating game media sets, and more particularly to a system, method, and program for generating game media sets in a game that uses a game media set consisting of multiple game media. Background Technology

[0002] In recent years, information processing devices with communication functions, such as smartphones, have become rapidly popular, and games running on such devices have been released in large numbers. In these games, it is well known that a user (player) uses a set of game media (characters, cards, equipment, etc.) to complete game objectives. For example, Patent Document 1 discloses a system that accumulates performance information whenever other users complete game objectives, and when determining the set of objects to guide a user in completing a game objective, presents the user with information about the set of objects to guide the user in completing the objective, based on the accumulated performance information.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent No. 6345866 Summary of the Invention

[0006] The problem the invention aims to solve

[0007] In a game where players group multiple game media, deciding how to group their media becomes a core part of the gameplay. In this context, a more precise recommendation system is desired when suggesting media groups.

[0008] In one aspect, the present invention was made to solve such a problem, with the aim of providing a system capable of generating recommended game media sets with higher accuracy.

[0009] Solution for solving the problem

[0010] [1] One embodiment of the system of the present invention is used to generate a game medium group for completing a unit game in a game using a game medium group consisting of multiple game media, each having a medium state level.

[0011] The system has a standardized data generation unit. For each game medium, the standardized data generation unit determines the estimated level of the medium status of a game medium for that game medium by taking the mode or range of the median value as the median value, based on the game medium group that has completed the game and the medium status level contained in the game log.

[0012] The standardized data generation unit generates the following game media group: the game media group is a game media group that sets the estimated level of each game media included in the game media group that has completed the game in the game log.

[0013] [2] Regarding one embodiment of the present invention, according to the system described in [1], wherein,

[0014] Each game medium is assigned multiple medium status levels.

[0015] The standardized data generation unit determines the estimated level for each medium status level of each game medium, and generates a game medium group in the game log containing game medium groups that have completed the game of the unit game, each of which has the determined estimated level for each medium status level.

[0016] [3] Regarding one embodiment of the present invention, according to the system described in [1] or [2], wherein,

[0017] Each player is assigned a player level.

[0018] The standardized data generation unit will determine the estimated player level of the unit game by taking the mode or range of the player levels calculated from the player levels of players who have completed the unit game, starting from the median value.

[0019] The system also includes a recommendation candidate generation unit, which extracts a game medium group including e or fewer e game mediums from the game medium group of the unit game generated by the standardized data generation unit. The game mediums are game mediums whose player level of the estimated level of the unit game determined by the standardized data generation unit has a holding rate lower than a specified holding rate or a rarity lower than a specified level.

[0020] [4] Regarding one embodiment of the present invention, according to the system described in [3], wherein,

[0021] The standardized data generation unit, for each game medium, determines the estimated level of the medium status level of that game medium for that unit game by taking the mode or mode range less than the median, the mode or mode range above the median, or the value or range above the median with a frequency of N% or more of the overall value and the highest frequency closest to the median, based on the game medium group that has completed the unit game and the medium status level contained in the game log.

[0022] The standardized data generation unit will determine the estimated player level of the unit game based on the player levels of players who have completed the unit game, which are found in the game logs. The estimated player level is determined by the mode or range of the player level that is less than the median, or the mode or range of the player level that is above the median, or the value or range that is above the median and has a frequency of more than N% of the whole and is the most frequent and closest to the median.

[0023] [5] Regarding one embodiment of the present invention, according to the system described in [3], wherein,

[0024] The recommended candidate generation unit, for each extracted game medium group, replaces game mediums in the game medium group with holding rates lower than the specified holding rate with other game mediums whose estimated player level of the unit game has a holding rate higher than the specified holding rate or a rarity lower than the specified level, thereby generating m game medium groups.

[0025] [6] Regarding one embodiment of the present invention, according to the system described in [5], wherein,

[0026] The recommended candidate generation unit, for each extracted game medium group, replaces game mediums in the game medium group with holding rates lower than the specified holding rates with other game mediums that are highly interchangeable with the game medium, and whose player level of the estimated level of the unit game has a holding rate higher than the specified holding rate or a rarity lower than the specified level, to generate m game medium groups.

[0027] [7] Regarding one embodiment of the present invention, the system according to any one of [1] to [6], wherein,

[0028] The recommended candidate generation unit extracts multiple game media groups from the generated multiple game media groups in a manner that makes each game media appear equally in all game media of the game.

[0029] [8] A method according to one embodiment of the present invention is used to generate a game media group for completing a unit game in a game using a game media group consisting of multiple game media, each having a media state level, the method comprising:

[0030] For each game medium, the estimated level of the medium status of a game medium, calculated based on the game medium group and medium status level of the unit game contained in the game log, is determined by the mode or mode range starting from the median of the medium status level; and

[0031] The following game media group is generated: This game media group is a game media group that sets the estimated level of the media status of each game media included in the game media group that has completed the game in the game log.

[0032] [9] Regarding one embodiment of the present invention, according to the method described in [8], wherein,

[0033] Each player is assigned a player level.

[0034] The method further includes:

[0035] The estimated player level for the unit game is determined by the mode or range of modes, starting from the median, of the player levels calculated based on the player levels of players who have completed the unit game, as contained in the game logs; and

[0036] Extract a game media group comprising e or fewer e game media from the generated game media group of the one unit game, wherein the game media are those whose player level of the determined one unit game has a holding rate lower than a specified holding rate or a rarity lower than a specified level.

[0037]

[10] The program of one embodiment of the present invention is a program that causes a computer to execute the method described in [8] or [9].

[0038] Invention Effects

[0039] In one aspect, according to the present invention, it is possible to generate recommended game media sets with higher accuracy. Attached Figure Description

[0040] Figure 1 This is a block diagram illustrating the hardware structure of a system according to one embodiment of the present invention.

[0041] Figure 2 This is an example of a functional block diagram of a system according to one embodiment of the present invention.

[0042] Figure 3 This is an example of a histogram generated by the standardized data generation department for the character level of a character in a level q.

[0043] Figure 4 This is a diagram illustrating the situation where m records are generated from the records associated with a level q in the extracted grouped data.

[0044] Figure 5 This is an example of a flowchart illustrating the processing of a system according to one embodiment of the present invention. Detailed Implementation

[0045] The recommendation system of an embodiment of the present invention will now be described with reference to the accompanying drawings. The recommendation system 1 of the present invention is a system in which, in a game where players progress by clearing stages using a team (team information) composed of multiple characters, team information for testing each stage can be generated to generate a team (team information) for clearing each stage presented to the player (user). A character is an example of a game medium, but it can also be cards, equipment, or other game mediums. A team or team information is an example of a group of game mediums (game medium group) composed of multiple game mediums, such as a deck, a combination of cards, or equipment. For ease of explanation, the game of the embodiment of the present invention will be referred to as "Game G". Game G is an online game that can be played on a mobile terminal such as a smartphone. In the embodiments of the present invention, an application can refer to an application installed on a smartphone or tablet terminal, or it can refer to all applications. In the embodiments of the present invention, an ID is an example of uniquely identifiable information; for example, a player ID is an example of player identification information that uniquely identifies a player. The structure of the embodiments described below, as well as the functions and effects brought about by such structure, are merely examples and are not limited to the following description. Furthermore, as stated below, the embodiments of the present invention are not limited to systems, but can be methods, programs, etc.

[0046] In the description of the hardware structure of the embodiments of the present invention, for convenience, it is assumed that the recommendation system 1 is implemented by a single device, but this is not a limitation. The recommendation system 1 can be a single recommendation device, or it can be composed of multiple devices, or it can be implemented through a virtual environment such as a virtual server. When the recommendation system 1 is implemented through a virtual environment, it can be implemented using a virtual environment that implements the virtualization of all the elements constituting the recommendation system 1. For example, the technology related to the virtual environment can be used as described in Japanese Patent Application Publication No. 2021-145939.

[0047] Figure 1This is a block diagram illustrating the hardware structure of a recommendation system 1 according to one embodiment of the present invention. The recommendation system 1 includes a processing device 11, an input device 12, an output device 13, a storage device 14, and a communication device 15. These components are connected via a bus 16. Furthermore, interfaces are provided between the bus 16 and each component as needed.

[0048] The processing device 11 controls the overall operation of the recommendation system 1. The processing device 11 is, for example, a CPU. The processing device 11 performs various processes by reading and executing programs and data stored in the storage device 14. The processing device 11 can also be composed of multiple processing devices. For example, the processing device 11 can also be composed of at least one of a CPU, MPU, GPU, and FPGA.

[0049] Input device 12 is a device that accepts user input to recommendation system 1, such as a touch panel, touch pad, keyboard, mouse, or button. Output device 13 is a device that outputs information according to the control of processing device 11, such as a display that shows application screens to the user of recommendation system 1.

[0050] Storage device 14 includes a main storage device and an auxiliary storage device. The main storage device is, for example, a volatile memory capable of high-speed information read / write operations, and is used as a storage area and working area when information is processed by the processing device 11. The auxiliary storage device stores various programs and data used by the processing device 11 when executing these programs. The auxiliary storage device is a non-volatile storage device or non-volatile memory, or it may be a removable storage device. In one example, storage device 14 may include at least one of Blu-ray discs, DVDs, CDs, HDDs, SSDs, and flash memory, and at least one of SRAM and DRAM.

[0051] The communication device 15 is a module, device, or apparatus capable of transmitting and receiving data with a user terminal (player terminal) or other computers such as a server via a network. The communication device 15 can be configured as a device or module for wireless communication or wired communication. Furthermore, if the user's input and output in the recommendation system 1 are solely via the communication device 15, the recommendation system 1 may not require an input device 12 and an output device 13.

[0052] In an embodiment of the present invention, game G is provided by game server GS (not shown). In one example, game server GS may have processing device, input device, output device, storage device, and communication device corresponding to the aforementioned processing device 11, input device 12, output device 13, storage device 14, and communication device 15, respectively. Game server GS has the same structure as a general game server that provides online games, and may be composed of one or more devices or implemented through a virtual environment.

[0053] A game server (GS) is a server accessed by player terminals (not shown) such as smartphones when players actually play the game. The game server (GS) stores the game application (game program) and connects to the player terminals of each player via a network. In one example, when a specified game application A is launched on a player terminal, the player terminal communicates with the game server (GS), and the game server (GS) and player terminal exchange data required to provide game services. In another example, while game application A is running on the player terminal, the game server (GS) communicates with the player terminal periodically or intermittently, executes the game based on the game operation input on the player terminal, and sends the execution result back to the player terminal.

[0054] In one example, the game server GS stores player IDs associated with the player's game-related information. In another example, the game server GS receives the player ID and password from the player's terminal to authenticate the player, and uses the data stored in association with the successfully authenticated player ID to provide game services (Game G) to the player's terminal. Once the game server GS authenticates a player ID, it can store data associated with that player ID until the game ends or a logout operation is initiated. For example, when a player's terminal connects to the game server GS and is identified and authenticated using the player ID and password, the player can play Game G through that player's terminal as the player with that player ID. During the execution of Game G on the player's terminal, for example, during the execution of game application A on the player's terminal, the game server GS stores game logs as game-related log data. The game logs stored by the game server GS are generated from the gameplay of ordinary players (ordinary users).

[0055] The game G in the embodiments of the present invention is the following game.

[0056] Game G includes multiple stages that are battle game scenarios where friendly and enemy characters (described later) engage in combat. The order of these stages is predetermined or set according to prescribed rules. Game G may also include battle games in addition to stages.

[0057] Each stage is associated with a stage ID and has its own unlock conditions. These conditions might include, for example, a player's level being above a certain threshold or completion of other specified battle games. Players can only play battle games that meet these unlock conditions.

[0058] Players can select multiple characters from their allied characters to form a team via a team formation screen (not shown). The team formation screen can be displayed before the start of a battle in a level, or it can be displayed by selecting the team formation screen from the main screen (not shown) using a button. Furthermore, in the description of embodiments of the present invention, for ease of explanation, the scenario where players can select 5 characters to form a team is illustrated, but this is not a limitation.

[0059] Players can use allied characters in their team to fight enemy characters with various statuses and attributes, or navigate through stages of varying difficulty. Stages are cleared by having allied characters in the team defeat enemy characters. Upon clearing a stage, the player receives a reward. Generally, the difficulty of stages increases as the game progresses.

[0060] • When the battle ends in a stage, a completion marker is assigned based on the stage's outcome, indicating the completion level. The completion markers are any one of the following: 3 stars (★★★) for completing the stage with all allies alive, 2 stars (★★) for completing the stage with 3-4 allies alive, 1 star (★) for completing the stage with 1-2 allies alive, and 0 stars for failing to complete the stage. The rewards received by the player vary depending on the completion level.

[0061] Game G features an auto-play mode, which eliminates the need for player intervention during levels.

[0062] The players in game G, according to an embodiment of the present invention, are as follows.

[0063] Players can own friendly characters, and the character IDs of these friendly characters are stored in association with the player's own player ID. Players can view all their owned friendly characters through a friendly character confirmation screen (not shown). Players can acquire friendly characters through in-game rewards, free or paid gacha pulls, or stage completion rewards. When a new friendly character is acquired, the player's player ID is stored in association with the friendly character ID of the acquired friendly character.

[0064] • Player IDs are stored in association with player levels, which represent player rank, allowing players to check their rank on the game screen.

[0065] • When a player clears a stage, they receive a set amount of experience points. Each time a player accumulates a certain amount of experience points, their level increases. When a player's current level is the maximum level of an allied character and the player's level increases by the set amount, the maximum level of the allied character (described later) also increases by the set amount.

[0066] • Store player IDs in association with the player's game information (such as the start date of the game, the latest login date and time).

[0067] The characters in game G according to the embodiments of the present invention are as follows.

[0068] • Store each friendly character (character ID) in association with various status information (status data). For example, status information includes ability values ​​such as attack power and defense power, rarity, experience points, character level, equipment, equipment level, character level, and attributes (such as the type of frontcourt / centercourt / backcourt, physical / magical / all-around, etc.).

[0069] Rarity is a rarity of an allied character defined by the game developers and operators. Specifically, it's a parameter expressed as the number of stars, or categorized as Rare (R) or Super Rare (SR). Each character is assigned an initial rarity upon acquisition, with the following tendency: characters with lower probability of being selected in gacha pulls or those obtainable as rewards for completing more difficult stages tend to have higher initial rarity and lower player ownership rates. Conversely, allied characters with lower initial rarity tend to be more frequently owned even by newcomers (beginner players). Furthermore, during gameplay, rarity can be variable or structured such that increasing an allied character's rarity through items, crafting, etc., thereby improving the character's base stats.

[0070] • Experience points increase, for example, by winning a level or using specified items. Character levels are linked to experience points; each time experience points reach a certain value, the character level increases. For example, each friendly character has a level cap corresponding to the player's level, and by using various items, the character level only increases within that cap. For example, each friendly character has base stats based on their level, corresponding to combat abilities such as health, attack power, and defense; these base stats increase as the friendly character's level increases.

[0071] Equipment refers to weapons, armor, and other gear equipped by friendly characters. It can be obtained through in-game shops, stage rewards, and other means. For example, each piece of equipment has bonuses for attack power, defense, etc. When equipped, these bonuses are added to the base stats, thus enhancing the friendly character's various abilities. Each piece of equipment has a character level requirement; increasing the character level allows them to equip stronger gear. Furthermore, equipment has an equipment level system. As the equipment level increases through the use of various items, the bonuses for attack power, defense, etc., are set higher. Each piece of equipment also has a maximum level cap.

[0072] Regarding character levels, you can increase your level by collecting and using specific equipment, which can improve various abilities of friendly characters and grant you skills that can be used in stages.

[0073] • Attributes are attributes associated with characters. For example, they might refer to the character's position in the team during a level (e.g., front-line / mid-line / back-line type), or their preferred attack / defense style (physical / magical / all-rounder). This metadata is information the player considers when assembling a team. As an example, attributes can be further categorized; for instance, there could be rare all-rounders with higher performance or aptitude, and general all-rounders with lower performance or aptitude. Furthermore, not only friendly characters have attributes, but enemy characters also have attributes. In level progression, it's important to assemble friendly characters with specific attributes into the team.

[0074] In this embodiment, for convenience, the rarity, character level, special equipment level, and character class that can improve the abilities of friendly characters in game G are collectively referred to as character status level. Character status level can also refer to one of the following levels (grades): rarity, character level, equipment level, or character class.

[0075] The game logs stored by the game server GS contain information on the team composition characters (friendly characters) and the status of each team composition character for each level. The game logs stored by the game server GS can also include replay data from when a level is cleared. The status information includes character status levels. In embodiments of this invention, friendly characters can be simply represented as roles.

[0076] Figure 2 This is an example of a functional block diagram of a recommendation system 1 according to an embodiment of the present invention. The recommendation system 1 includes a grouping data storage unit 31, a standardized data generation unit 32, and a recommendation candidate generation unit 33. The recommendation candidate generation unit 33 includes an extraction unit 35, a replacement unit 36, and a sampling unit 37.

[0077] When the recommendation system 1 is a single device, the functions of the recommendation system 1 can be implemented, for example, by having the processing device 11 execute a program and store data in the storage device 14 as needed. For example, the grouping data storage unit 31 can also be implemented by having the storage device 14 store database data (e.g., tables) and programs, and by having the processing device 11 execute the programs. In this way, various functions are implemented by loading programs, so a part or all of a functional unit (e.g., a software module) can also be possessed by other functional units. However, the grouping data storage unit 31 can also be implemented by the storage device 14. For example, when the recommendation system 1 is implemented through a virtual environment, the functions of the recommendation system 1 can be implemented by the operation of each component based on the operation when the recommendation system 1 is implemented by a single device.

[0078] The game logs stored in the game server GS contain at least team-related information or grouping data containing team-related information. The grouping data storage unit 31 stores information obtained from the game logs stored in the game server GS, a portion of the game logs, or both. The grouping data storage unit 31 stores actual player grouping data, which includes team-related information from the game logs stored in the game server GS showing completion of each stage. The actual player grouping data stored in the grouping data storage unit 31 can be either directly obtained from the game logs obtained from the game server GS or data generated based on the game logs. In an embodiment of the present invention, the actual player grouping data stored in the grouping data storage unit 31 is data generated or extracted from the game logs showing initial completion of each stage in the game logs stored in the game server GS. In one example, recommendation system 1 can retrieve game logs from the game server GS, and extract (extract) game logs showing initial completion of each level from these logs. Alternatively, it can retrieve game logs showing initial completion of each level from the game server GS and extract or generate actual player group data containing team-related information from the retrieved game logs. The actual player group data includes the player IDs of the players who played the game, their player levels, level IDs, five team group characters (teams), and the status information of these team group characters. The actual player group data may include completion markers indicating the level completed. The five team group characters (teams) and their status information can be referred to as team information.

[0079] In one example, the actual player grouping data stored in the grouping data storage unit 31 can be set as data generated or extracted from game logs (content-independent) stored in the game logs of the game server GS, showing players clearing each level. For example, in this case, the recommendation system 1 can extract (obtain) game logs showing players clearing each level from the game logs obtained from the game server GS, or it can obtain game logs showing players clearing each level from the game server GS and generate the actual player grouping data. In one example, the actual player grouping data stored in the grouping data storage unit 31 can be set as data generated or extracted from game logs stored in the game server GS, showing players clearing each level with a score above a certain level or by meeting certain conditions (e.g., clearing the level with a level rating of 3 stars (★★★)). For example, in this case, the recommendation system 1 can extract (obtain) game logs from the game logs obtained from the game server GS that show scores above the specified level or that meet the conditions for clearing each level (e.g., clearing each level with a level rating of 3 stars (★★★)), or it can obtain game logs from the game server GS that show scores above the specified level or that meet the conditions for clearing each level, and generate actual player grouping data.

[0080] In one example, the actual player group data can be set as table data in a database. For instance, in this case, the column direction (as columns) contains items such as level ID, player ID, player level, completion flag, and unit data, and the row direction accumulates data for each group. The data (records) of each row can be stored in association with the player ID and level ID. In this case, the unit data contains team information (5 team group characters (teams) and their status information), for example, storing text data described in JSON as retrieval data. For example, the status information stored in the unit data only needs to contain the pre-set information required to present to the player from the status information specified in game G. The actual player group data is not limited to the data structure of this example, but in the following description, for ease of explanation, the data obtained by a player completing a level once in the actual player group data can be referred to as a record.

[0081] In embodiments of this invention, the rarity, character level, special equipment level, and character class (or class number) among the character's status information that enhance the character's abilities are respectively referred to as character status levels. Character status levels can be set by game developers, game operators, etc. Hereinafter, for ease of explanation, unless specifically mentioned otherwise, character status levels will be described as character level, special equipment level, and character class. For example, a character status level is one of character level, special equipment level, and character class. Furthermore, the status information possessed by a character can refer to the status information set for the character, and this status information can refer to the status information stored in association with the character.

[0082] For each level, the standardized data generation unit 32 determines the estimated level of the character's status level or player level for that level as less than the median value of all data (at least one of the character status level and player level of each character in the team) in the actual player group data storage unit 31.

[0083] The standardized data generation unit 32, for a given level, a character, and a character's status level, determines the estimated level of that character's status level for that level as the mode of the calculated character status level (less than the median) based on the data of cleared levels (5 teams, team members, and their respective status levels) contained in the actual player grouping data stored in the grouping data storage unit 31. The standardized data generation unit 32 determines the estimated character status level for all levels and all character status levels in game G, for each level, each character, and each character status level (based on the type of each character status level). Furthermore, the character status information processed by the standardized data generation unit 32 is status information associated with the character's status level.

[0084] In one example, the standardization data generation unit 32 can extract records associated with a level q from the records contained in the actual player grouping data stored in the grouping data storage unit 31, perform statistics on the characters and their status levels contained in the extracted records, and generate histograms for each character and for each character status level (by character level, special equipment level, and character class). The histogram of a character status level for a character in a level q represents the frequency (number of users) of that character status level in the actual player grouping data (game log). The standardization data generation unit 32 can determine the mode of the histogram generated for a character's status level (character level, special equipment level, or character class) that is less than the median as the estimated level of that character's status level in that level q. For example, the standardized data generation unit 32 can extract records associated with a level q from the records contained in the actual player grouping data stored in the grouping data storage unit 31, perform statistics on a character and its level contained in the extracted records to generate a histogram, and determine the mode of the histogram generated for the character's level that is less than the median as the estimated level of the character's level in the level q.

[0085] The standardized data generation unit 32 generates standardized grouping data for each level: This standardized grouping data is data on the status level of each character in the five team groups (teams) that have cleared a level, as determined by the standardized data generation unit 32, within the actual player grouping data stored in the grouping data storage unit 31. In one example, the standardized grouping data includes columns for player level, level ID, and unit data. In another example, the standardized data generation unit 32 generates team data for each level: This team data is data on the status level of each character in the five team groups (teams) that have cleared a level, as determined by the standardized data generation unit 32, within the actual player grouping data stored in the grouping data storage unit 31.

[0086] In one example, the standardization data generation unit 32 can generate standardized grouping data by changing (setting) the character status level of characters contained in the extracted records associated with a stage q in the actual player grouping data stored in the grouping data storage unit 31 to the estimated level determined by the standardization data generation unit 32. For example, in the records associated with a stage q in the actual player grouping data stored in the grouping data storage unit 31, the standardization data generation unit 32 sets the character status level of a character contained in the extracted records to the estimated level of that character's character status level determined by the standardization data generation unit 32. The standardization data generation unit 32 can perform this setting on the character status levels of all characters in the records associated with a stage q.

[0087] The standardized data generation unit 32 generates standardized grouping data for each level of the game G. The standardized data generation unit 32 then stores the generated standardized grouping data in the grouping data storage unit 31.

[0088] For a given level, the standardized data generation unit 32 determines the estimated player level for that level as the mode of the player levels calculated from the player levels of players who have cleared that level, which is less than the median, within the actual player grouping data stored in the grouping data storage unit 31. The standardized data generation unit 32 determines the estimated player level for all levels of the game G, one for each level. In one example, the standardized data generation unit 32 can generate standardized grouping data by changing (setting) the player levels in the records associated with a level q in the actual player grouping data stored in the grouping data storage unit 31, in addition to changing the character status levels of the characters in the extracted records, to the estimated levels determined by the standardized data generation unit 32.

[0089] The standardized data generation unit 32 can perform histogram generation on each parameter p (player level and character status level of each character) separately based on the data of clearing any level q contained in the actual player group data stored in the group data storage unit 31, and determine (estimate) the value of parameter p for level q as the "mode less than the median".

[0090] Figure 3 This is an example of a histogram 40 generated by the standardized data generation unit 32 for the character level of a character in a level q. The standardized data generation unit 32 performs histogram generation on the character level of a character in a level q. Figure 3The data shows the frequency (number of users) of the character level of a character in a given level q, as included in the actual player grouping data (game log). The normalized data generation unit 32 can determine the mode 41, which is less than the median in the histogram 40, as the estimated level of that character in level q. Furthermore, the normalized data generation unit 32 can also determine the estimated level for other character status levels besides player level and character level.

[0091] Extraction unit 35 generates extracted grouping data for each level, specifically for one level, by extracting team information including e or fewer e characters from the team information contained in the standardized grouping data generated by standardized data generation unit 32. The character in question is one whose player level, determined by standardized data generation unit 32, has a player level possession rate below a predetermined possession rate r%. The extracted grouping data is the data extracted by extraction unit 35 from the standardized grouping data. Extraction unit 35 stores the generated (extracted) extracted grouping data in grouping data storage unit 31. In one example, the extracted grouping data includes columns containing player level, level ID, and unit data. Extraction unit 35 determines the estimated player level for each level across all levels of game G.

[0092] In game G, for example, game developers and operators define characters whose estimated player level is above r% as "normal characters," and characters whose estimated player level is below r% as "non-normal characters." In one example, r (%) is 40 (%). In another example, e is 1. Hereinafter, we will use e=1 for explanation.

[0093] Recommendation system 1 stores the ownership rate of each player level for all characters in game G. Extraction unit 35 dynamically generates a list of characters with an ownership rate of r% or higher, i.e., a general character list, and stores this general character list in storage device 14, etc. In one example, recommendation system 1 can dynamically generate and store the ownership rate of each player level for all characters in game G at any time or at a predetermined time, and can also dynamically generate and store a list of general characters with an ownership rate of r% or higher at any time or at a predetermined time. Furthermore, in embodiments of the present invention, the ownership rate can be calculated or arbitrarily determined by the game developer, game operator, etc.

[0094] In one example, the extraction unit 35 generates extracted grouping data by extracting records from the standardized grouping data stored in the grouping data storage unit 31 that are associated with a stage q, and that the number of characters with a player level ownership rate of less than r% for the estimated level of stage q determined by the standardized data generation unit 32 is e (e=1) or less. Each record in the extracted grouping data stored in the grouping data storage unit 31 is associated with a team containing e (e=1) or less non-normal characters.

[0095] The replacement unit 36, for each level, for each level, for each of the 5 team grouping roles (teams) included in the extracted grouping data contained by the extraction unit 35, replaces the role in the team whose player level holding rate for the estimated level of that level, determined by the standardization data generation unit 32, is lower than a specified holding rate r% (in other words, a non-normal role in that level) with another role that is interchangeable with that role and whose player level holding rate for the estimated level of that level is higher than r% (in other words, a normal role in that level), thereby generating m teams. Thus, the replacement unit 36 ​​generates the replaced grouping data. The replacement unit 36 ​​stores the generated replaced grouping data in the grouping data storage unit 31. The replaced grouping data contains information related to m times the number of teams in the extracted grouping data. In one example, the replaced grouping data includes, as columns, items such as player level, level ID, and unit data. In one example, m is 3.

[0096] In one example, for a level q, the replacement unit 36 ​​can replace the non-normal characters in the team in that level with other m normal characters in that level that have high interchangeability (e.g., the same attributes) in each record associated with that level q in the extracted grouped data extracted by the extraction unit 35, thereby generating m records.

[0097] In one example, if the character to be replaced has a Frontline / Physical attribute, the replacement unit 36 ​​uses a character with a Frontline / Physical or Frontline / All-rounder attribute from the general characters in that stage for replacement. In another example, the replacement unit 36 ​​can dynamically calculate the interchangeability of the general character list for the character to be replaced based on the character's status information, and if the evaluation value is high, for example, above a specified value, then that character is determined to be replaced (a character with high interchangeability). For example, the replacement unit 36 ​​can dynamically calculate the interchangeability of the general character list for the character to be replaced based on the character's status information, in descending order of the player level holding rate for that stage's estimated level. For example, the replacement unit 36 ​​can be configured to increase the evaluation value of characters with more consistent attributes among multiple attributes such as Frontline / Frontline / Midline / Backline, Physical / Magic / All-rounder in the character's status information. For example, the replacement unit 36 ​​can also be configured to calculate the evaluation value for a Frontline character, regardless of whether it is Physical / Magic / All-rounder, always treating it as a consistent attribute. For example, the replacement unit 36 ​​can also be configured such that, for a certain type of character, the evaluation value is reduced for changes from a universal attribute to other attributes, and the evaluation value is increased for replacements to universal attributes.

[0098] In one example, the replacement unit 36 ​​can generate grouping data after replacement by further changing (setting) the status level of each role after replacement to the estimated level determined by the standardized data generation unit 32.

[0099] Figure 4 This diagram illustrates the scenario where the replacement unit 36 ​​generates m records from each record associated with a level q in the extracted grouped data. Figure 4 In the example shown, m=3 and e=1, it can be confirmed that the replacement unit 36, for a level q, can replace one non-normal character in the team in that level with three other normal characters in that level that have high interchangeability with that character in each record of the extracted grouped data extracted by the extraction unit 35, thereby generating three records.

[0100] Sampling unit 37 extracts n teams (team information) from the multiple teams (team information) contained in the permutation-post-grouping data generated by permutation unit 36, for each level, in a manner that ensures all characters in all characters of game G appear equally, to generate extracted post-grouping data. In one example, n is 2000.

[0101] In one example, for a level q, the sampling unit 37 generates sampled grouping data by extracting records from the records associated with a level q in the permuted grouping data stored in the grouping data storage unit 31 in such a way that the characters contained in the extracted records equally include all the characters of the game G.

[0102] In an embodiment of the present invention, the team information included in the sampled grouping data is team information used for testing each level. In one example, the five team grouping characters and their status information contained in each record of the sampled grouping data are team information used for testing each level.

[0103] In one example, the test play of each level is performed by an emulator S (not shown), which consists of one or more devices or is implemented through a virtual environment. In an embodiment of the invention, the recommendation system 1 does not include the emulator S.

[0104] The emulator S includes a virtual instance server and a test game server. The test game server has the same structure as the game server GS, but differs in that it is not accessed by regular player terminals and only accepts access from the virtual instance server. In one example, the emulator S uses team information generated or extracted by the recommendation system 1 to play the game and extracts information on teams that can clear a level. In another example, the virtual instance server, according to control signals from the emulator S, generates multiple virtual instances to virtualize the player terminal or the player terminal's software environment. A virtual instance is a virtual instance used to execute game G, configured to connect to the test game server and be capable of executing game G. When the virtual instance server executes game G in a virtual instance, it sets the team information for clearing a level and executes the gameplay of that level. The game is played in an automatic gameplay mode.

[0105] Figure 5 This is an example of a flowchart illustrating the processing of a recommendation system 1 according to an embodiment of the present invention.

[0106] In step S1, the standardization data generation unit 32 standardizes the actual player grouping data stored in the grouping data storage unit 31 to generate standardized grouping data. In one example, in step S1, the standardization data generation unit 32, for each level, performs histogram generation on each parameter p (player level and character status level) based on the data from the actual player grouping data stored in the grouping data storage unit 31 showing completion of a level q, and determines the value of parameter p for that level q as the "mode less than the median". In another example, in step S1, the standardization data generation unit 32 sets the character status levels of each character in the five team grouping characters (teams) that have completed a level, as determined by the standardization data generation unit 32, to the estimated level in that level, thus generating standardized grouping data.

[0107] In step S2, the extraction unit 35 generates extracted grouping data for each level by extracting team information including e or fewer e characters from the team information contained in the standardized grouping data generated by the standardized data generation unit 32. The character is a character whose player level holding rate for the estimated level of that level, determined by the standardized data generation unit 32, is lower than a predetermined holding rate r%.

[0108] In step S3, the replacement unit 36, for each level, for each level, for each of the five team grouping roles included in the extracted grouping data extracted by the extraction unit 35, replaces the role in the team whose player level holding rate for the estimated level of that level, determined by the standardized data generation unit 32, is lower than a predetermined holding rate r% with a role that is interchangeable with the original role and whose player level holding rate for the estimated level of that level is higher than r%, thereby generating m teams.

[0109] In step S4, the sampling unit 37 extracts n team information from the multiple team information contained in the permutation-post-grouping data generated by the permutation unit 36 ​​in a manner that ensures that all characters in all characters of the game G appear equally for each level, in order to generate the extracted post-grouping data.

[0110] Next, the main effects of the recommendation system 1 according to the embodiments of the present invention will be explained.

[0111] To achieve more accurate team recommendations from game systems and other sources, and more specifically, to address the existing cold start and exposure bias issues, the inventors devised the following technique: Using existing player-created teams that have actually cleared levels (team information), the inventors edited the team information to replace harder-to-obtain allied characters with easier-to-obtain ones. This edited team information was then tested in a large-scale simulation environment. This technique of recommending teams based on actual ability to clear levels is unprecedented.

[0112] In an embodiment of the present invention, the standardized data generation unit 32 can perform histogram generation on each parameter p (player level and character status level of each character) separately based on the data of clearing any stage q included in the actual player grouping data stored in the grouping data storage unit 31, and determine (estimate) the value of parameter p for that stage q for the new player (novice player) based on the "mode less than the median". By setting it up in this way, in this embodiment, it is possible to detect the core group of new players with each parameter in each stage. As a result, it is possible to generate a team (team information) that is more suitable for recommending to new players, is guaranteed to clear the target stage, and can be formed by new players.

[0113] Furthermore, in an embodiment of the present invention, the recommended candidate generation unit 33 can generate a team (team information) that only contains general characters and can be assembled by replacing e characters (preferably e=1) based on the data of clearing any stage q contained in the actual player grouping data stored in the grouping data storage unit 31.

[0114] By adopting a structure similar to the recommendation system 1 of this embodiment, team information (parameter P to be input into the simulator S) for testing games can be generated based on existing game logs, significantly reducing the number of simulation attempts. This allows for the generation of game media groups for more accurate recommendations. Furthermore, the recommendation system 1 of this embodiment estimates the parameters P of a team capable of clearing a game entirely based on data evidence, without relying on the experience, intuition, or boldness (so-called KKD) of the game developer (creator, producer). Conventional recommendation grouping functions select roles suitable for user-specified search criteria, lacking the comprehensive and statistical analysis of other users' grouping data (team information) to derive parameters P (team information) for newcomers, as in the recommendation system 1 of this embodiment.

[0115] Unless otherwise specified, the above-mentioned effects are the same in other implementation methods and variations.

[0116] Embodiments of the present invention can also be configured as a method, program, or computer-readable storage medium storing the above-described recommendation system 1, the information processing method shown in the flowchart, or a program for implementing the above-described embodiments of the present invention. Furthermore, embodiments of the present invention can also be configured as a server capable of providing a computer with a program for implementing the functions of the above-described embodiments of the present invention and the information processing method shown in the flowchart. The same applies to other embodiments and variations.

[0117] In embodiments of the present invention, the content of Game G described above is an example and is not limited thereto. In embodiments of the present invention, Game G is not limited to a specific game and can be a game for which the recommendation system 1 of the embodiments of the present invention can be applied. In embodiments of the present invention, a level can be any game other than a level where the game is played using game media such as teams (team information), card decks, or combinations of character equipment. In this specification, this can be referred to as a unit game, which includes the concept of levels. Furthermore, in embodiments of the present invention, clearing a level includes the concept of a player achieving a certain amount of results in a unit game, such as winning in a battle game or obtaining a score higher than a specified score in other games. Additionally, for example, a team of 5 characters is an example; Game G can set the number of characters in a team to any number. Furthermore, for example, the information contained in the previously described status information is an example and is not limited thereto. Moreover, the content of Game G described above is an example and is not limited thereto.

[0118] In one or more embodiments of the present invention, the estimated level determined by the standardized data generation unit 32 may not be a specific level (score), but a specific level width (range).

[0119] In one or more embodiments of the present invention, the recommendation system 1 may include an emulator S. In this embodiment, the virtual instance server and the test game server may each be composed of one or more devices, or implemented through a virtual environment such as a virtual server. In one or more embodiments of the present invention, the recommendation system 1 may not include the emulator S, but may be configured to be communicatively connected to the emulator S.

[0120] In one or more embodiments of the present invention, the recommendation system 1, when including an emulator S or when connected to the emulator S in a communicable manner, can generate teams (team information) for clearing each level by having the emulator S test play the game. In this embodiment, when the recommendation system 1 is connected to the game server GS in a communicable manner, it can send the generated teams (team information) to the game server GS.

[0121] In one or more embodiments of the present invention, the standardized data generation unit 32 can be configured to: determine an estimated level of one character's status level for each target level (a portion or specific levels) based on each character and each character's status level, rather than for all levels of the game G, and generate standardized grouped data for each target level. In this embodiment, the standardized data generation unit 32 can be configured to determine an estimated level of the player's level for each target level. In this embodiment, the extraction unit 35, the substitution unit 36, and the sampling unit 37 can also be configured to generate extracted grouped data, substituted grouped data, and sampled grouped data for each target level.

[0122] In one or more embodiments of the present invention, team information contained in standardized grouping data, or team information contained in grouping data generated based on standardized grouping data that differs from sampled grouping data, can be set as team information for testing each level. For example, the five team grouping characters and their status information contained in the standardized grouping data can be set as team information for testing each level. In this case, the recommendation system 1 may not include the extraction unit 35, the replacement unit 36, and the sampling unit 37.

[0123] In one or more embodiments of the present invention, the team information contained in the extracted grouping data can be set as team information for testing each level. In this case, the recommendation system 1 may also exclude the replacement unit 36 ​​and the sampling unit 37.

[0124] In one or more embodiments of the present invention, the team information contained in the replaced grouping data can be set as team information for testing each level. In this case, the recommendation system 1 may not include the sampling unit 37.

[0125] In one or more embodiments of the present invention, the actual player grouping data stored in the grouping data storage unit 31 may also include data generated or extracted from the game logs of the game server GS after the second time each level has been cleared.

[0126] In one or more embodiments of the present invention, the actual player grouping data stored in the grouping data storage unit 31 can also be generated based on game logs generated by playing games through the emulator S. In this embodiment, for example, the recommendation system 1 can obtain (extract) game logs from game logs obtained from the game server GS and game logs generated by playing games through the emulator S, showing the first completion of each stage, and extract or generate actual player grouping data containing team-related information from the obtained game logs. With this structure, even if sufficient game logs for generating actual player grouping data cannot be obtained from the game server GS, it is possible to generate team information that is relatively easy to obtain and can be presented to players, enabling them to complete each stage.

[0127] In one or more embodiments of the present invention, the actual player grouping data may not include player IDs. In embodiments that include player IDs, it may be configured to extract records from the actual player grouping data associated with player IDs of players who meet certain conditions (e.g., players who started the game within the last 6 months or players who logged in at least once within the last 3 months).

[0128] In one or more embodiments of the present invention, the character status level is an example of the game medium status level of any game medium. In one or more embodiments of the present invention, the character status level can be one of the following: rarity, character level, equipment level, character rank, etc., which can improve the abilities of friendly characters; or it can refer to other levels. In one or more embodiments of the present invention, when there is only one character status level, the standardized data generation unit 32 can generate standardized grouping data by setting one character status level (determined by the standardized data generation unit 32) for each character in the team that has cleared a stage, as included in the actual player grouping data stored in the grouping data storage unit 31, based on the estimated level in that stage.

[0129] In one or more embodiments of the present invention, the standardized data generation unit 32 can, for a level, a character, and a character status level, determine the estimated level of the character status level of the character in that level as the mode, starting from the median, based on the mode of the five team-based characters (teams) that have cleared that level and the character status levels of those characters, which are included in the actual player team data storage unit 31. For example, the mode, starting from the median, can be a mode less than the median, a mode greater than the median, or a value greater than the median and having a frequency of N% or more of the whole, the highest frequency, and closest to the median. Furthermore, in embodiments of the present invention, the mode can be either a single value or a range from a certain value (mode range). In one example, the standardized data generation unit 32 can, for a level, a character, and a character status level, determine the estimated level of the character status level for that character in that level as the mode of the median or higher of the character status levels calculated from the actual player grouping data stored in the grouping data storage unit 31, which includes the five team grouping characters (teams) that have cleared that level and the character status levels of those characters. In this example, the estimated level of each character status level in each level can be determined by assuming information recommendations for intermediate players.

[0130] In one or more embodiments of the present invention, the replacement unit 36 ​​can, for each level, for each level, for each of the five team grouping roles (teams) included in the extracted grouping data extracted by the extraction unit 35, replace the role in the team whose player level holding rate for the estimated level of that level determined by the standardized data generation unit 32 is lower than a predetermined holding rate r% (in other words, a non-normal role in that level) with another role whose player level holding rate for the estimated level of that level is greater than r% (in other words, a normal role in that level), thereby generating m teams.

[0131] In one or more embodiments of the present invention, each functional unit of the recommendation system 1 can also be implemented in hardware by constructing electronic circuits or the like to implement some or all of them.

[0132] In the processes or actions described above, as long as no contradictions arise in the processes or actions, such as using data that should not be available in a certain step, the processes or actions can be freely modified. Furthermore, the embodiments described above are illustrative of the invention, and the invention is not limited to these embodiments. The invention can be implemented in various ways without departing from its spirit.

[0133] Explanation of reference numerals in the attached figures

[0134] 1: Recommendation system; 11: Processor; 12: Input device; 13: Output device; 14: Storage device; 15: Communication device; 16: Bus; 31: Grouped data storage unit; 32: Standardized data generation unit; 33: Recommendation candidate generation unit; 35: Extraction unit; 36: Permutation unit; 37: Sampling unit; 40: Histogram; 41: Mode.

Claims

1. A system for generating a game media group for completing a unit of the game in a game using a game media group consisting of multiple game media, each having a media state level. The system includes a standardized data generation unit. For each game medium, this unit determines the estimated medium status level of that game medium for that unit of game based on the game medium group that has completed the unit game and its medium status level, using the median or its range (less than the median), the median or its range (above the median), or the median or its range that is above the median and has a frequency of N% or higher, is the highest frequency, and is closest to the median. in, The standardized data generation unit generates the following game media group: This game media group is a game media group that sets the estimated level of the media status of each game media included in the game media group that has completed the game in the game log.

2. The system according to claim 1, wherein, Each game medium is assigned multiple medium status levels. The standardized data generation unit determines the estimated level for each medium status level of each game medium, and generates a game medium group in the game log containing game medium groups that have completed the game of the unit game, each of which has the determined estimated level for each medium status level.

3. The system according to claim 1 or 2, wherein, Each player is assigned a player level. The standardized data generation unit will determine the estimated player level of the unit game based on the player levels of players who have completed the unit game, calculated from the player levels contained in the game logs. The estimated player level will be determined by the mode or range of the player level being less than the median, or the mode or range of the player level being greater than the median, or the value or range of the player level being greater than the median and having a frequency of more than N% of the overall value, and being the most frequent and closest to the median. The system also includes a recommendation candidate generation unit, which extracts a game medium group containing e or fewer e game mediums from the game medium group for passing the unit game generated by the standardized data generation unit. The game mediums are game mediums whose player level of the estimated level of the unit game determined by the standardized data generation unit has a possession rate lower than a specified possession rate or a rarity lower than a specified level.

4. The system according to claim 3, wherein, The recommended candidate generation unit, for each extracted game medium group, replaces game mediums in the game medium group with holding rates lower than the specified holding rate with other game mediums whose estimated player level of the unit game has a holding rate higher than the specified holding rate or a rarity lower than the specified level, thereby generating m game medium groups.

5. The system according to claim 4, wherein, The recommended candidate generation unit, for each extracted game medium group, replaces game mediums in the game medium group with holding rates lower than the specified holding rate with other game mediums whose interchangeability evaluation value is above the specified value, and whose player level of the estimated level of the unit game has a holding rate above the specified holding rate or a rarity lower than the specified level, to generate m game medium groups.

6. The system according to claim 3, wherein, The recommended candidate generation unit extracts multiple game media groups from the generated multiple game media groups in a manner that makes each game media appear equally in all game media of the game.

7. A method for generating a game media group for completing a unit game in a game using a game media group consisting of multiple game media, each having a media status level, the method comprising: For each game medium, the estimated level of the medium status level of a game medium is determined by the mode or mode range of the medium status level of the game medium obtained from the game medium group that has completed the game and the medium status level of the game medium contained in the game log, which is less than the median, or the mode or mode range of the medium status level above the median, or the value or range of the medium status level above the median and with a frequency of more than N% of the whole and the highest frequency and closest to the median. as well as The following game media group is generated: This game media group is a game media group that sets the estimated level of the media status of each game media included in the game media group that has completed the game in the game log.

8. The method according to claim 7, wherein, Each player is assigned a player level. The method further includes: The estimated player level for the unit game is determined by the mode or range of player levels calculated based on player levels of players who have completed the unit game, which is less than the median; or the mode or range of player levels above the median; or the value or range of player levels above the median that has a frequency of more than N% of the overall value and is the highest in frequency and closest to the median. Extract from the generated game media set for completing the unit game a set that includes e or fewer e game media, wherein the game media are those whose player level of the determined unit game has a possession rate lower than a specified possession rate or a rarity lower than a specified level.

9. A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method according to claim 7 or 8.

10. A computer program product comprising a computer program that, when executed by a processor, implements the steps of the method of claim 7 or 8.

Images