JP7158771B1 - Systems, methods and programs for supporting competitive betting - Google Patents

Abstract

[Object] To provide a system or the like for supporting betting in competitions such as e-sports by presenting appropriate odds. obtaining means for obtaining data and/or information about at least one player; and a first characterization of said at least one player based on said obtained data and/or information about said at least one player A first calculation means for calculating a score, a second calculation means for calculating a second score representing the characteristics of the competition, and based on the first score and the second score, in the competition and determining means for determining betting odds. [Selection drawing] Fig. 1A

Description

The present invention relates to systems, methods and programs for supporting betting in competitions.

In recent years, e-sports (electronic sports) are becoming popular. For example, Patent Literature 1 discloses streaming distribution of game images for e-sports.

WO2020/026458

The inventor of the present invention provides a platform in which players can compete against each other through a network in e-sports, and spectators can watch the competition, so that many users can enjoy e-sports. I thought it might be. Furthermore, we thought that offering betting on such a platform could provide a new user experience.

An object of the present invention is to provide a system or the like for supporting betting in competitions such as e-sports by presenting appropriate odds.

The present invention provides, for example, the following items.
(Item 1)
A system for supporting betting in a competition, comprising:
obtaining means for obtaining data and/or information regarding at least one player of the competition;
a first calculation means for calculating a first score characteristic of the at least one player based on the obtained data and/or information about the at least one player;
a second calculation means for calculating a second score representing the characteristics of the competition;
and determining means for determining betting odds on said competition based on said first score and said second score.
(Item 2)
Further comprising a change means for changing the rules of the competition,
When the rule is changed by the changing means,
The second calculation means calculates the second score based on the changed rule,
The determining means determines the odds based on the first score and a second score calculated based on the changed rule.
The system of item 1.
(Item 3)
The changing means is
receiving votes from those involved in said competition on changes to said rules;
3. The system of item 2, wherein the rules are changed based on the results of the voting.
(Item 4)
The competition is an e-sports competition,
4. The system of item 3, wherein the affiliate is a person holding a governance token for the competition.
(Item 5)
The competition is an e-sports competition,
the at least one player includes a first player operated by a first user and an avatar of the first user;
The first calculation means calculates a first score of the first player and a first score of the avatar,
The determining means determines the odds based on the first player's first score, the avatar's first score, and the second score.
A system according to any one of items 1-4.
(Item 6)
6. Any one of items 1 to 5, wherein the calculating means calculates the first score according to the extent to which the obtained data and/or information about the at least one player correlates with the concept about personality. The system described in .
(Item 7)
a second obtaining means for obtaining data and/or information relating to said betting user;
a third calculation means for calculating a third score representing characteristics of the user based on the obtained data and/or information regarding the user;
7. The system of any one of items 1-6, wherein the determining means determines the odds further based on the third score.
(Item 8)
further comprising second determining means for determining a bet suitable for the user based on the third score;
8. The system of item 7, wherein the determining means determines odds for the determined bet.
(Item 9)
System according to any one of the preceding items, wherein data and/or information relating to said at least one player are stored in a tamper-proof manner.
(Item 10)
10. The system of any one of items 1-9, wherein the obtaining means obtains the data from a device associated with the at least one player.
(Item 11)
11. The system according to any one of items 1 to 10, wherein said acquisition means acquires information disclosed subjectively by said player.
(Item 12)
A method for supporting betting in a competition, comprising:
obtaining data and/or information regarding at least one player of the competition;
calculating a first score characteristic of the at least one player based on the obtained data and/or information about the at least one player;
calculating a second score representing characteristics of the competition;
determining odds for betting on the competition based on the first score and the second score.
(Item 12A)
13. Method according to item 12, including features according to one or more of the above items.
(Item 13)
1. A program for assisting in betting on a competition, said program being executed on a computer system comprising a processor, said program comprising:
obtaining data and/or information regarding at least one player of the competition;
calculating a first score characteristic of the at least one player based on the obtained data and/or information about the at least one player;
calculating a second score representing characteristics of the competition;
determining odds for betting on the competition based on the first score and the second score.
(Item 13A)
14. Program according to item 13, including features according to one or more of the above items.
(Item 14)
A computer-readable storage medium storing a program for supporting betting in a competition, said program being executed in a computer system comprising a processor, said program comprising:
obtaining data and/or information regarding at least one player of the competition;
calculating a first score characteristic of the at least one player based on the obtained data and/or information about the at least one player;
calculating a second score representing characteristics of the competition;
determining odds for betting on the competition based on the first score and the second score.
(Item 14A)
15. The computer-readable storage medium of item 14, comprising the features of one or more of the above items.

ADVANTAGE OF THE INVENTION According to this invention, the system etc. for supporting the betting in a competition which can show a betting user the appropriate odds can be provided. This may increase users' willingness to bet, leading to the spread of betting in e-sports and the like. In particular, by supporting e-sports betting, it is possible to provide an unprecedented new user experience. New user experiences include, for example, the experience of betting on competitions involving tens of thousands of users, the experience of betting on competitions that are held suddenly, the experience of betting on competitions with frequently changing rules, the experience of betting on competitions that allow players to create their own The experience of placing a bet in a competition against an avatar can be mentioned.

FIG. 2 schematically illustrates an example betting flow on platform 10 for placing bets in esports. FIG. 4 schematically illustrates another example betting flow on platform 10 for placing bets in esports. A diagram showing an example of the configuration of a system 100 for supporting betting in competitions. A diagram showing an example of a specific configuration of a system 100 for supporting betting in competitions. Conceptual diagram showing association of information stored in the database unit 200 FIG. 4 is a diagram showing an example of the configuration of the processor unit 120; FIG. 12 is a diagram showing an example of a configuration of a processor section 120', which is an alternative embodiment of the processor section 120; FIG. 12 is a diagram showing an example of a configuration of a processor section 120', which is an alternative embodiment of the processor section 120; A diagram showing an example of the structure of a neural network model that can be used by the first calculation means 122. Flowchart showing an example of processing (processing 500) in the system 100 for supporting betting in competitions Flowchart showing processing subsequent to processing 500 Flowchart showing another example of processing (processing 600) in the system 100 for supporting betting in competitions

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described with reference to the drawings.

1. Esports Betting Service The inventors of the present invention have developed a new service to support eSports betting. The service provides a platform for esports. Esports can be played on the platform. Esports players can access this platform and play esports. An esports spectator can access the platform to watch esports and even place bets as a betting user. For example, esports sponsors can reach out to esports players and spectators by advertising on this platform.

As used herein, “e-sports” is an abbreviation for “electronic sports” and broadly means competitions that are played using electronic devices. In a narrow sense, "esports" means a competition using a computer game or a video game when viewed as a sports competition. Esports competitions include, but are not limited to, not only sports games, but also shooting games, simulation games, fighting games, racing games, puzzle games, card games, and RPGs. In this example, a golf game will be described as an e-sports competition.

FIG. 1A schematically illustrates an example betting flow on a platform 10 for placing bets in esports.

At step S1, two players 20A and 20B access the platform 10 and enter a golf game on the platform 10. FIG. The two players 20A, 20B can access the platform 10, for example, using their own terminal devices (eg, smart phones, tablets, smart watches, smart glasses, personal computers). In the golf game, player 20A operates character A, player 20B operates character B (not shown), and players 20A and 20B compete against each other.

In this example, two players 20A and 20B compete in a golf game, but the number of players and the format of the competition are not limited to this. For example, it may be a match between three or more players, or a match between a single player and a computer player (non player character: NPU). A computer player may be, for example, an avatar or digital clone that copies the characteristics of the player. The match may be a team match or an individual match.

A golf game has rules, and rules 11 are shown on the platform 10 . Two players 20A and 20B will play a golf game according to Rule 11.

The two players 20A, 20B may provide data and/or information regarding the players 20A, 20B to the platform 10 when entering the golf game.

Here, the player-related data refers to objective information related to the player. Data about the player may be obtained, for example, from the player's terminal and/or an IoT device capable of communicating with the player's terminal. Data related to the user includes, for example, player's action history (for example, exercise history, health activity history, purchase history, saving action history, meal history, web page browsing history, conversation history with AI speaker, SNS usage history, etc.), Including, but not limited to, health information (eg, height, weight, blood pressure, heart rate, pre-existing diseases, etc.), DNA information, and the like. Data about a player may include the player's personal information. Since game-related information such as game results is stored in the platform 10, it does not have to be included in the player-related data.

Here, the player-related information is subjective information about the player. Information about the player can be obtained, for example, by direct input by the player to the terminal device. Alternatively, information about the player may be derived indirectly, such as from interviews or questions of the player. The information about the player includes, but is not limited to, hobbies, preferences, reasons for action (eg, reasons for applying for current or past occupations, reasons for changing jobs from past occupations, etc.).

Players 20A, 20B may each provide data and/or information regarding each to platform 10, eg, via their terminals. Alternatively, players 20A, 20B may each provide data and/or information regarding each to platform 10, eg, via their associated IoT devices.

When the players 20A, 20B enter the game, betting odds in the golf game are determined based on the rules 11 of the golf game and data and/or information about each player 20A, 20B. In this specification, the odds refer to the payout rate for winning the bet. For example, if you win a bet with odds of 2.5 times, the payout will be 2.5 times the bet amount.

The odds may take into account the golf game's performance in addition to the golf game rules 11 and data and/or information regarding each player 20A, 20B. However, a game record is not required. Therefore, even when the player 20A and the player 20B play against each other for the first time (that is, when there is no match record between the player 20A and the player 20B), appropriate odds can be determined. Furthermore, even if one or both of player 20A and player 20B is playing the golf game itself for the first time (i.e., there is no record for one or both of player 20A and player 20B), odds can be determined.

The platform 10 shows the determined odds 12 . In the example shown in FIG. 1A, the odds of character A winning the golf game are 2.5 times, and the odds of character B winning the golf game are 1.8 times. In the golf game according to rule 11, the odds for character B are lower than the odds for character A because it is determined that player 20B is more advantageous.

In this example, it will be described that a bet is made on the outcome of the golf game between character A and character B, but the content of the bet is not limited to this. A bet may be placed on any item other than winning or losing the golf game. For example, bets can be placed on the longest flight distance (which character has the longest flight? Is the longest flight over 300 yards? bets can be made on which character?Is the shortest distance to the pin less than 5 yards?

At step S2, at least one user 30 accesses the platform 10 and places a bet on a golf game between two players 20A, 20B. At least one user 30 can access the platform 10, for example, using his/her terminal device (eg, smart phone, tablet, smart watch, smart glasses, personal computer).

At least one user 30 checks rules 11 and odds 12, predicts which of the two players 20A, 20B will win, and places a bet on the character he thinks will win. The wager may be, for example, a currency (or points) dedicated to the platform 10, a virtual currency, or a legal currency. At least one user 30 may provide a wager to platform 10 by any means.

Although three users 30 are shown in the example shown in FIG. 1A, the number of users 30 is not limited to this. Any number of users 30 may access platform 10 and place bets.

Once betting by user 30 is completed, two players 20A, 20B play a golf game on platform 10 in step S3. At least one user 30 may access platform 10 and watch a golf game by two players 20A, 20B. At least one user 30 will, for example, watch while rooting for the player he or she bet on to win. Then, for example, an e-sports sponsor can appeal to the players 20A, 20B and at least one user 30 by placing an advertisement in the game.

When the golf game is completed, the bet is won and lost, and at least one user 30 is notified of the bet result in step S4. A payout is also provided if at least one user 30 wins the bet. The dividend may be, for example, a currency (or points) dedicated to the platform 10, a virtual currency, or a legal currency. Payouts are preferably in the same currency as wagers. A payout may be provided to at least one user 30 by any means. A prize may be provided to a player who wins a game of golf. As a result, the player's willingness to participate in the game can be promoted.

Thus, providing platform 10 allows players 20A, 20B to play a golf game and at least one user 30 to watch and place bets on the golf game. and sponsors can advertise within the golf game. In particular, betting odds in the golf game are appropriately determined based on the rules 11 of the golf game and data and/or information about each player 20A, 20B, so that the user 30 can make a proper bet. can. Furthermore, since the odds are not determined based on the results in the game, the odds are appropriately determined even if the players are playing against each other for the first time, or even if the players are playing the game for the first time. By doing so, it is possible to provide the user with a new betting experience.

FIG. 1B schematically illustrates another example betting flow on platform 10 for placing bets in esports. The example shown in FIG. 1B is similar to the example shown in FIG. 1A, except that the rules of golf can be changed by stakeholders.

At step S11, at least one stakeholder 40 accesses the platform 10 and votes to change the rules of the golf game. Parties 40 have voting rights (eg, governance tokens). For example, the parties 40 will have voting rights according to the amount of governance tokens they have. Thus, a party 40 with more governance tokens will have more voting rights and may have an advantage in voting. Governance tokens may be issued by platform 10 service providers.

For example, several candidate rule changes are presented at the platform 10 and at least one participant 40 votes for the candidate rule change they wish to change. For example, in a golf game, votes are made as to whether to include no OB in the rules, whether to include mulligans in the rules, whether to include the 6-inch place in the rules, and the like.

A rule change candidate with a majority of votes in favor is adopted, and the rule is changed accordingly. The platform 10 shows the changed rule 11'. The golf game will be played according to the changed rule 11'.

At step S12, two players 20A and 20B access the platform 10 and enter a golf game on the platform 10, similar to step S1 described above. Similar to step S1 described above, the two players 20A, 20B may provide data and/or information regarding the players 20A, 20B to the platform 10 when entering the golf game.

Once the players 20A, 20B enter the game, betting odds in the golf game are determined based on the modified rules 11' of the golf game and data and/or information about each player 20A, 20B.

The platform 10 shows the determined odds 12 . In the example shown in FIG. 1B, the odds of Character A winning the golf game are 2.1 times, and the odds of Character B winning the golf game are 2.4 times. In the golf game according to rule 11' after the change, the odds for character A are lower than the odds for character B because it is determined that player 20A is somewhat more advantageous.

At step S13, as in step S2 described above, at least one user 30 accesses the platform 10 and places a bet on a golf game between the two players 20A, 20B. The at least one party 40 who voted for the rule change may be the user 30, and may place a bet after voting.

Once betting by user 30 is completed, two players 20A, 20B play a golf game on platform 10 in step S14, similar to step S3 described above. At least one user 30 may access platform 10 and watch a golf game by two players 20A, 20B. At least one user 30 will, for example, watch while rooting for the player he or she bet on to win. At least one participant 40, as a user, can access the platform 10 and watch a golf game played by the two players 20A, 20B. Then, for example, an e-sports sponsor can appeal to players 20A, 20B, at least one user 30, and at least one affiliate 40 by placing an advertisement in-game.

When the golf game is completed, the win or loss of the bet is determined, and in step S15, the result of the bet is notified to at least one user 30 in the same manner as in step S4 described above. A payout is also provided if at least one user 30 wins the bet.

Thus, providing platform 10 allows players 20A, 20B to play a golf game, at least one user 30 and at least one participant 40 to watch the golf game, and , can place bets on the golf game, and sponsors can advertise within the golf game. In particular, the betting odds in the golf game are appropriately determined based on the modified rule 11' of the golf game and the data and/or information about each player 20A, 20B, so that the user 30 can make a proper bet. It can be performed. Furthermore, by changing the rules by voting, it is possible to suppress the rut of the game and always provide the players 20A, 20B and the user 30 with a fresh and unexpected user experience.

For example, a more exciting user experience can be provided for player 20A and user 30 if player 20A competes against an avatar or digital clone that copies player 20A's characteristics. This is because the more accurately the avatar or digital clone copies the characteristics of the player 20A, the more the ability of the avatar or digital clone competes with the ability of the player 20A, making it difficult to know who will win. What's more, when the rules change, it becomes even more difficult to predict where the game will go. Even in such a case, since the betting odds are appropriately determined, the user 30 can make an exciting and appropriate bet.

E-sports competitions can be held in virtual space at any time regardless of location and time. Also, anyone can participate in esports competitions from anywhere in the world as long as they are connected to the network. In the platform 10 described above, even if an e-sports competition is suddenly held, no matter who participates in the e-sports competition, or tens of thousands of players participate in the e-sports competition, the rules are frequently changed. However, appropriate odds can be determined based on participant data and/or information. Through such bets, users can experience new entertainment that has never existed before.

In the above example, betting on e-sports has been described, but the present invention is not limited to this. The present invention can cover betting on any sport. In this specification, the term "competition" means that players compete for victory or defeat according to rules. Here, players can include not only humans but also real-world objects such as animals, plants, and robots, and virtual-world objects such as avatars. Therefore, competitions include, but are not limited to, real-world sports, horse racing, bicycle racing, boat racing, casino games, and mahjong, in addition to e-sports played in the virtual world as described above.

The platform 10 described above may be implemented by a system for supporting betting on competitions, as described below.

2. Configuration of a System for Supporting Betting on Competitions FIG. 2A shows an example configuration of a system 100 for supporting betting on competitions.

System 100 is connected to database section 200 . The system 100 is also connected to at least one player terminal 300 , at least one user terminal 400 and at least one voter terminal 500 via a network 600 .

2A shows two player terminals 300, two user terminals 400, and two voter terminals 500, the player terminals 300, the user terminals 400, and the voter terminals 500 is not limited to this. Any number of player terminals 300 , user terminals 400 and voter terminals 500 may be connected to system 100 via network 600 . For example, if a player doubles as a voter or a user doubles as a voter, the voter terminal device 500 may be omitted.

Network 600 may be any type of network. Network 600 may be, for example, the Internet or a LAN. Network 600 may be a wired network or a wireless network.

System 100 is further connected to blockchain network 700 . Blockchain network 700 is a network built by blockchain technology and can be used to manage player and/or user information. Since the use of blockchain technology makes it possible to easily detect falsification of managed information, falsification of personal information managed by the blockchain network 700 is substantially impossible. Information managed by the blockchain network 700 may be utilized to calculate a first score representing a player characteristic and/or a third score representing a user characteristic.

In addition, although personal information is managed by blockchain technology, the present invention is not limited to blockchain technology. Blockchain network 700 can be constructed using any technology as long as information can be managed in a substantially unfalsifiable manner.

An example of system 100 is, but is not limited to, a computer (eg, server device) installed at a service provider that provides a platform for esports. An example of the player terminal device 300 is, but not limited to, a computer (eg, terminal device) used by the players of the competition. A non-limiting example of user terminal 400 is a computer (eg, terminal) utilized by a user to watch and/or place bets on a competition. The voter terminal device 500 is, but is not limited to, a computer (eg, terminal device) used by voters (or related parties, stakeholders) who have the right to vote to change the rules of the competition. Here, the computer (server device or terminal device) can be any type of computer. For example, the terminal device can be any type of terminal device, such as smart phones, tablets, personal computers, smart glasses, smart watches, and the like.

The database unit 200 stores, for example, various information used to calculate a second score that represents the characteristics of the competition (e.g., rules of the competition, characteristics of the competition, advantageous ability in the competition, disadvantageous ability in the competition, etc.). etc.) are stored. The database unit 200 may store, for example, various types of information used to calculate a first score representing player characteristics and/or a third score representing user characteristics. The database unit 200 may store the odds for the game determined by the system 100 .

FIG. 2B shows an example of a specific configuration of system 100 for supporting betting on competitions.

The system 100 comprises an interface section 110, a processor section 120, and a memory section 130. FIG.

The interface unit 110 exchanges information with the outside of the system 100 . The processor unit 120 of the system 100 can receive information from outside the system 100 via the interface unit 110 and can transmit information outside the system 100 . The interface unit 110 can exchange information in any format.

Interface unit 110 includes, for example, an input unit that allows information to be entered into system 100 . It does not matter in what manner the input allows information to be entered into the system 100 . For example, if the input unit is a receiver, the receiver may receive information from outside the system 100 via a network for input. Alternatively, if the input unit is a data reader, information may be input by reading information from a storage medium connected to system 100 .

Interface unit 110 includes, for example, an output unit that allows information to be output from system 100 . It does not matter in what manner the output unit allows information to be output from the system 100 . For example, if the output unit is a transmitter, the transmitter may output information by transmitting it to the outside of system 100 via a network. Alternatively, if the output unit is a data writing device, information may be output by writing information to a storage medium connected to system 100 .

System 100 can send information to and/or receive information from database portion 200 via interface portion 110, for example. System 100 can transmit information to and/or receive information from player terminal 300 via interface 110, for example. System 100 can transmit information to and/or receive information from user terminal 400 via interface 110, for example. System 100 may transmit information to and/or receive information from voter terminals 500 via interface 110, for example.

System 100 can receive data and/or information about the player via interface 110, for example. System 100 can receive data and/or information about the user via interface 110, for example.

System 100 can, for example, receive votes via interface 110 to change the rules of the game. System 100 can receive betting information (eg, how much to bet on what) via interface 110, for example. System 100 may transmit bet results via interface 110, for example. The system 100 can transmit preferred bet information to the user via the interface unit 110, for example.

The processor unit 120 executes processing of the system 100 and controls the operation of the system 100 as a whole. The processor unit 120 reads a program stored in the memory unit 130 and executes the program. This allows the system 100 to function as a system that executes desired steps. The processor unit 120 may be implemented by a single processor or multiple processors.

The memory unit 130 stores programs required for executing the processes of the system 100, data required for executing the programs, and the like. The memory unit 130 stores a program for causing the processor unit 120 to perform processing for supporting betting in a game (for example, a program for realizing processing shown in FIGS. 5A, 5B, and 6, which will be described later). good too. Here, it does not matter how the program is stored in the memory unit 130 . For example, the program may be pre-installed in memory unit 130 . Alternatively, the program may be installed in memory unit 130 by being downloaded via a network. In this case, the type of network does not matter. Memory unit 130 may be implemented by any storage means. Alternatively, the program may be stored in a computer-readable storage medium and installed in memory unit 130 by reading this.

The database unit 200 may store data and/or information regarding players, for example.

Data about the player is objective information about the player. The data regarding the player may be private information known only to the player himself or may be public information shared with a third party. Data about the player may be obtained, for example, from the player's terminal and/or an IoT device capable of communicating with the player's terminal. Data about the player can be obtained from anywhere on the network, for example. Data related to the player includes, for example, the player's action history (for example, exercise history, health activity history, purchase history, saving action history, meal history, web page browsing history, conversation history with AI speaker, SNS usage history, etc.), Including, but not limited to, health information (eg, height, weight, blood pressure, heart rate, pre-existing diseases, etc.), DNA information, assessment by others, and the like. Data about a player may include the player's personal information. Game-related information, such as game scores, may not be included in the player-related data, and preferably not included in the player-related data.

The player-related information is subjective information centered on the player. Information about the player may be private information known only to the player himself or may be public information shared with a third party. The information about the player can be, for example, information that the player has disclosed subjectively, and can be obtained by the player's direct input to the terminal device, for example. Alternatively, information about the player may be derived indirectly, such as from interviews or questions of the player. The information about the player includes, but is not limited to, hobbies, preferences, reasons for action (eg, reasons for applying for current or past occupations, reasons for changing jobs from past occupations, etc.).

The database unit 200 may store data and/or information regarding users, for example.

Data about the user is objective information about the user. Data about the user may be private information known only to the user, or may be public information shared with a third party. Data about the user may be obtained, for example, from the user's terminal and/or an IoT device capable of communicating with the user's terminal. Data about the user can be obtained from anywhere on the network N, for example. Data related to the user includes, for example, the user's action history (for example, exercise history, health activity history, purchase history, saving action history, meal history, web page browsing history, conversation history with AI speaker, SNS usage history, etc.), Including, but not limited to, health information (eg, height, weight, blood pressure, heart rate, pre-existing diseases, etc.), DNA information, assessment by others, and the like. Data about a user may include the user's personal information.

User-related information is subjective information centered on the user. The information about the user may be private information known only to the user himself/herself, or may be public information shared with a third party. The information about the user can be, for example, information that the user has disclosed subjectively, and can be obtained by the user's direct input to the terminal device, for example. Alternatively, information about the user may be derived indirectly, such as from interviews or questions of the user. The information about the user includes, but is not limited to, for example, hobbies, preferences, reasons for action (for example, reasons for applying for current or past jobs, reasons for changing jobs from past jobs, etc.).

In the database unit 200, various information used to calculate the second score representing the characteristics of the competition (e.g., rules of the competition, characteristics of the competition, advantageous ability in the competition, disadvantageous ability in the competition, etc.) is stored.

The database unit 200 stores various types of information used to calculate the first score representing the characteristics of the player.

Here, the player's characteristics can be a concept that represents what kind of person the player is, that is, the character of the player. Player characteristics can also describe a player's personality in terms of the player's personality. For example, a player's characteristics may describe the player's personality in terms of "personality." In addition, player traits may describe a player's personality in terms of "money" and/or may describe a player's personality in terms of "health." Therefore, the score representing the characteristics of the player may include a feature amount related to "personality", and the score representing the characteristics of the player may additionally include a feature amount related to "money" and/or a feature amount related to "health". can be a multidimensional score containing in The multi-dimensional axes of score can include, for example, a "personality" aspect axis, a "money" aspect axis, and/or a "health" aspect axis. Here, "personality" is information indicating whether or not a person's character and/or personality is trustworthy, and includes, for example, evaluations from others. "Money" is information about the person's money, and includes information such as annual income, assets, and whether the person is saving. "Health" is information about the health of the person, and includes, for example, information such as disease, eating habits, activities for health, and the like.

As an example, when a score representing a player's characteristics has an axis of viewpoints related to "personality", points are given for multiple items related to "personality", and based on the scores for each of the multiple items, A person's character will be expressed. For example, in a person who has an angry but honest personality, among the items related to "personality", the score of the item related to "honest person" is high or the score related to "liar" is low, and the score related to "impatient" Higher scores for items or lower scores for items related to "patience".

As another example, if the score representing the characteristics of the player has an axis of the viewpoint related to "money", points are given for multiple items related to "money", and based on the scores for each of the multiple items, the "money" The personality of the person will be expressed in relation to. For example, among items related to "money," a person who spends a lot of money has a high score for an item related to "thrifty person" or a low score for an item related to "thrifty person."

As another example, when the score representing the characteristics of the player has an axis of viewpoints related to "health", points are given for a plurality of items related to "health", and based on the scores of each of the multiple items, the "health" The personality of the person will be expressed in relation to. For example, a person with an obese body type has a high score for items related to "degree of obesity" or "BMI" among items related to "health."

These items are examples, and each multidimensional axis can have any item. As the number of items on each axis increases, the character of the player can be expressed in more detail from the viewpoint of that axis, but the amount of data also increases accordingly. The smaller the number of items on each axis, the smaller the amount of score data representing the characteristics of the player and the easier it is to handle, but the more roughly the character of the player is represented. For example, the score representing the characteristics of the player is specified for at least two of the multiple axes from the score represented for the multiple axes having a large number of items (the score representing the character of the player in detail). It may be a score derived by extracting the items of (a score that roughly represents the personality of the player).

For example, the database unit 200 may store concepts related to parameters used for score calculation and various information in association with each other. Parameters used for score calculation may include, for example, “personality,” “money,” and “health.”

For example, for the parameter "personality", the database unit 200 may store the concept of personality and various pieces of information in association with each other. For example, the concept of "altruistic" (or "transpersonal") related to "personality" includes keywords such as "volunteer", "donation", "caring", "consultation", "frequently participate in volunteering", Statuses such as "frequently consulted" and "has contributed" can be stored in association with each other. For example, the concept of 'SDGs' (Sustainable Development Goals, or 'Sustainable Development Goals') in relation to 'personality' includes keywords related to 17 goals and/or 169 targets (e.g., 'equality', 'environmental Conservation”, etc.), “I am taking actions related to 17 goals and/or 169 targets”, “I have thoughts related to 17 goals and/or 169 targets”, etc. can be stored as

For example, regarding the parameter "money", the database unit 200 may store the concept of money and various information in association with each other. For example, the concept of "rich" (or "cash flow rich") related to "money" is stored in association with keywords such as "Harvard University" and "MBA" and status such as "annual income of 20 million yen or more". can be For example, the concept of "rich" (or "stock rich") related to "money" can be stored in association with keywords such as "landlord" and "shareholder" and status such as "asset of 100 million yen or more". .

For example, regarding the parameter "health", the database unit 200 may store concepts related to health and various information in association with each other. For example, in the concept of "good body" (or "physical health") related to "health", keywords such as "non-smoking" and "normal blood pressure", statuses such as "BMI 18-27", etc. are associated and stored. obtain. For example, the concept of "mindfulness" (or "mental health") related to "health" includes keywords such as "rewarding" and "stressless", "stress check result is less than a predetermined value", and "sense of coherence". (SOC: Sense of coherence) is high” and the like can be associated and stored.

FIG. 2C is a conceptual diagram showing association of information stored in the database unit 200. As shown in FIG.

In the example shown in FIG. 2C, keywords such as "volunteer", "contribution", "caring", and "consultation" are associated with the concept of "altruistic" regarding personality. Further, these keywords are associated with other keywords (for example, "trouble", "tension", "polite", etc.). Further, further keywords may be associated with these keywords.

Such associations can be made, for example, using artificial intelligence (AI) capable of performing semantic searches, ie artificial intelligence that has learned correlations between keywords.

This artificial intelligence learns the correlation between keywords from a large amount of sentences. For example, the artificial intelligence parses the text to extract multiple keywords in the text and identify relationships between the multiple keywords in the text. For example, if a certain keyword is used together with another certain keyword in many sentences, this artificial intelligence learns those keywords as having a strong correlation. The artificial intelligence that has learned the correlation between keywords in this way can output keywords that are correlated with the input keyword based on the learned correlation.

For example, if you input the keyword "altruistic" into the artificial intelligence that has learned the correlation between keywords, the keywords correlated with "altruistic" include "volunteer", "donation", "consideration", and "consultation". can be output. For example, if the keywords "altruistic" and "considerate" are input to artificial intelligence that has learned the correlation between keywords, "polite" and the like can be output as keywords that correlate with "altruistic" and "consideration." .

In this way, the database unit 200 can store keywords input to the artificial intelligence that has learned correlations between keywords and output keywords in association with each other.

The above description of the various information used to calculate the first score representing the player's characteristics also applies to the various information used to calculate the third score representing the user's characteristics. .

A user's characteristics can be a concept that represents what kind of person the user is, that is, the user's personality. User characteristics can also describe a user's personality in terms of the user's personality. For example, user characteristics can describe a user's personality in terms of "personality." In addition, the user characteristics may describe the user's personality in terms of "money" and/or may describe the user in terms of "health". Therefore, the score representing the characteristics of the user can include a feature amount related to "personality", and the score representing the characteristics of the user additionally includes a feature amount related to "money" and/or a feature amount related to "health". can be a multidimensional score containing in The multi-dimensional axes of score can include, for example, a "personality" aspect axis, a "money" aspect axis, and/or a "health" aspect axis. Here, "personality" is information indicating whether or not a person's character and/or personality is trustworthy, and includes, for example, evaluations from others. "Money" is information about the person's money, and includes information such as annual income, assets, and whether the person is saving. "Health" is information about the health of the person, and includes, for example, information such as disease, eating habits, activities for health, and the like.

In the example shown in FIGS. 2A and 2B, database unit 200 is provided outside system 100, but the present invention is not limited to this. It is also possible to provide at least part of the database unit 200 inside the system 100 . At this time, at least part of the database section 200 may be implemented by the same storage means as the storage means implementing the memory section 130, or may be implemented by a storage means different from the storage means implementing the memory section 130. may In any event, at least a portion of database unit 200 is configured as a storage unit for system 100 . The configuration of database unit 200 is not limited to a specific hardware configuration. For example, the database unit 200 may be configured with a single hardware component, or may be configured with a plurality of hardware components. For example, the database unit 200 may be configured as an external hard disk device of the system 100, may be configured as storage on the cloud connected via a network, or may be configured as a distributed type using blockchain technology or the like. It may be configured as a network.

For example, data and/or information about the player are stored in the database unit 200 configured as a distributed network using blockchain technology or the like. It becomes possible. This ensures the reliability of the data and/or information regarding the player. Competition record data can also be stored in the database unit 200 configured as a distributed network using blockchain technology or the like, making falsification of the competition record data virtually impossible. Note that "substantially impossible to falsify" means that even if falsification is performed, it can be easily detected.

FIG. 3 shows an example of the configuration of the processor unit 120. As shown in FIG.

The processor unit 120 includes acquisition means 121 , first calculation means 122 , second calculation means 123 and determination means 124 .

Acquisition means 121 are arranged to acquire data and/or information about the player.

The acquisition means 121 may acquire data and/or information regarding players stored in the database section 200 via the interface section 110, for example. Alternatively, the acquiring means 121 may acquire data and/or information regarding the player received from the player terminal device 300 via the interface section 110, for example. The obtained data and/or information about the player is passed to the first computing means 122 .

Acquisition means 121 may further be configured to acquire data on the results of the competition. The data of the acquired match record can be passed to the first calculation means 122 .

The first calculating means 122 is configured to calculate a first score characteristic of the player based on data and/or information about the player. A player's characteristics can be a concept that represents what kind of person the player is, that is, the character of the player. Player characteristics can also describe a player's personality in terms of the player's personality. For example, a player's characteristics may describe the player's personality in terms of "personality." A player's characteristics may additionally describe the player's personality in terms of "money" and/or may describe the player's personality in terms of "health." Therefore, the first score representing player characteristics may include a feature related to "personality", and the first score representing player characteristics may include a feature related to "money" and/or "health". It can be a multi-dimensional score that additionally includes features for . Although the term "digital twin" is known, it can be said that the first score that multidimensionally represents the characteristics of the player is the score that represents the player's digital twin.

The first score multi-dimensional axis can include, for example, a "personality" perspective axis, a "money" perspective axis, and/or a "health" perspective axis.

In one embodiment, the first calculator 122 may calculate the first score depending on, for example, the extent to which the data and/or information about the player correlates with the parameters used to calculate the first score. can. The first calculation means 122, for example, refers to the database unit 200 storing various information correlated with the concept of the parameters used for calculating the first score, and the data and/or information about the player is the first score. determines the degree of correlation with the concept of the parameter used to calculate the score of Data and/or information about a player may include, for example, information about "personality," "money," and "health."

The first calculation means 122 may, for example, determine the extent to which data and/or information about the player correlates with the concept of "personality" and calculate a first score according to the determined extent of correlation. can. For example, if the data and/or information about the player are highly correlated with a particular concept among the concepts about "personality", the first calculating means 122 includes the feature quantity corresponding to that concept in the first score. Alternatively, it is possible to increase the score of the item corresponding to the concept. For example, the more strongly the data and/or information about the player is correlated with "altruistic" (or "transpersonal"), the more or more features corresponding to "altruistic" are included in the first score, or The score of the item corresponding to "altruistic" can be increased. For example, the more strongly the data and/or information about the player correlates with "high pressure", the more or more features corresponding to "pressure strength" are included in the first score or the "pressure strength". It is possible to increase the score of the item corresponding to

The first calculation means 122 can, for example, determine the degree of correlation between the data and/or information regarding the player and the concept regarding "money" and calculate the first score according to the determined degree of correlation. can. For example, if the data and/or information about the player are highly correlated with a particular concept among the concepts about "money", the first calculating means 122 includes the feature amount corresponding to that concept in the first score. Alternatively, it is possible to increase the score of the item corresponding to the concept. For example, the more strongly the data and/or information about the player correlates with "cash flow rich", the more or more features corresponding to "cash flow rich" are included in the first score or "cash flow rich". items corresponding to the concept of For example, the more strongly the data and/or information about the player is correlated with "stockrich", the more or more features corresponding to "stockrich" are included in the first score or the concept of "stockrich". The score of the corresponding item can be increased.

The first calculating means 122 may, for example, determine the extent to which the data and/or information regarding the player correlates with the concept relating to "health", and calculate the first score according to the determined extent of correlation. can. For example, if the data and/or information about the player are highly correlated with a particular concept among the concepts about "health", the first calculating means 122 includes the feature quantity corresponding to that concept in the first score. Alternatively, it is possible to increase the score of the item corresponding to the concept. For example, the more strongly the data and/or information about the player correlates with "excellent body" (or "physical health"), the more or more features corresponding to "excellent body" are included in the first score. Alternatively, it is possible to increase the score of the item corresponding to the concept of "excellent". For example, the more strongly the data and/or information about the player is correlated with "mindfulness" (or "mental health"), the more the feature quantity corresponding to "mindfulness" (or "mental health") becomes. Items that are more or more strongly included in the first score or correspond to the concept of "mindfulness" can be scored higher.

In one embodiment, the first calculation means 122 multidimensionally calculates the player characteristics, for example, using a machine learning model that has learned the relationship between data and/or information about a plurality of players and the first score. can be calculated as a first score. The data and/or information about the player includes, for example, information about "personality" and may further include information about "money" and "health."

A machine learning model can be constructed using any machine learning model. A machine learning model can be, for example, a neural network model.

FIG. 4 shows an example of the structure of a neural network model that can be used by the first calculation means 122. As shown in FIG.

A neural network model has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network model corresponds to the number of dimensions of input data and/or information about the player. The hidden layer of a neural network model can contain any number of nodes. The number of nodes in the output layer of the neural network model corresponds to the number of dimensions of output data. For example, the number of nodes in the output layer may be 1 when outputting a first score representing player characteristics.

The neural network model can be trained in advance using data and/or information regarding the player acquired by the acquisition means 121 . The learning process is a process of calculating the weight coefficient of each node of the hidden layer of the neural network model using the data and/or information regarding the player previously acquired by the acquisition means 121 .

The learning process is, for example, supervised learning. In supervised learning, for example, data and/or information about a player is used as input teacher data, a first score representing the player's characteristics is used as output teacher data, and information of a plurality of players is used to create a neural network model. A machine learning model can be built that can correlate data and/or information about a player with the first score by calculating a weighting factor for each node of the hidden layer of .

For example, a set of (teacher data for input, teacher data for output) for supervised learning is (data and/or information about the first player, first score of the first player), (second (data and/or information about the player, the first score of the second player), ... (data and/or information about the i-th player, the first score of the i-th player), ... etc. could be. When the input layer of such a trained neural network model is fed with data and/or information about the player newly acquired from the player, the player's first score is output to the output layer.

In supervised learning, for example, the degree of correlation between data and/or information about the player and parameters used to calculate the first score can be used as input teacher data.

The calculated first score is passed to the decision means 124 .

Refer again to FIG. 3A. The second calculation means 123 is configured to calculate a second score representing the characteristics of the game. The second score representing the characteristics of the game may be a score that multidimensionally represents the characteristics of the game. For example, the second score may characterize the game by the abilities or characteristics required or preferred to win the game or to play well in the game.

In one example, the second score includes parameters of mental-technical-physical (ie, mental-technical-physical strength) required or preferred to perform well in a competition. In another example, the second score includes parameters of muscular strength, muscular endurance, flexibility, agility, explosive power, jumping ability, and dexterity necessary or suitable for successful performance in a sport.

Each competition may be given a reference score in advance, subject to normal rules. For example, if the rules of the game are the same as the assumed normal rules, the second calculation means 123 can regard the reference score as the second score. For example, if the rules of the game differ from the normal rules that are assumed, the second calculation means 123 may determine the necessary or preferred abilities or characteristics to win the game or to play well in the game. A second score can be calculated by adjusting the base score to take into account the impact of the rules of the game on For example, the second calculation means 123 may calculate the reference score by considering the impact of the conditions under which the competition is held on the abilities or characteristics necessary or desirable to win the competition or to play well in the competition. A second score can be calculated by adjusting. Examples of conditions include, but are not limited to, the size of the competition, the number of spectators, prize money, and the like.

For example, the second computing means 123 may include a machine learning model that has learned the impact of the rules and/or circumstances of a game on the abilities or characteristics required or preferred to win the game or to play well in the game. can be used to calculate the second score. For example, a machine learning model can be trained such that given a reference score, rules, and context, it can then output a second score for the game.

The calculated second score is passed to the decision means 124 .

The determining means 124 is configured to determine betting odds on the competition based on the first score and the second score.

For example, the determining means 124 can determine odds based on the correlation between the first score and the second score. At this time, the determining means 124 can extract a feature amount that can be compared with the second score from the first score, and obtain a correlation between the extracted feature amount and the second score. The feature amount extracted from the first score may be, for example, a feature amount representing parameters of the player's mental, physical, and physical strength (that is, mental strength-technical strength-physical strength), or the player's muscle strength and muscle endurance. , flexibility, agility, instantaneous power, jumping ability, and dexterity. Since the first score is a score representing the character of the player, it is possible to extract such a feature amount from the first score.

For example, the determining means 124 lowers the odds as the correlation between the first score and the second score is higher, and raises the odds as the correlation between the first score and the second score is higher. can. This is because the higher the correlation between the first score and the second score, the higher the possibility that the player will be able to play well in the competition, and therefore the higher the possibility of winning the competition. On the other hand, the lower the correlation between the first score and the second score, the less likely the player will be able to play well in the competition, and therefore less likely to win the competition.

If there are multiple players in the competition, the determining means 124 may determine the odds independently for each player, or may consider the correlation of the first scores of each player. , may determine the odds. For example, if a first player's first score is compared with a second player's first score and it is determined that the first player is compatible with the second player, then the first player's It can be subtracted from the player's odds and added to the second player's odds.

The determining means 124 may determine the odds on a rule basis as described above, or may determine the odds using a machine learning model as described later.

A machine learning model can be constructed using any machine learning model. A machine learning model can be, for example, a neural network model.

A neural network model has an input layer, at least one hidden layer, and an output layer. The number of nodes in the input layer of the neural network model corresponds to the total number of dimensions of the input first and second scores. The hidden layer of a neural network model can contain any number of nodes. The number of nodes in the output layer of the neural network model corresponds to the number of dimensions of the output data, and the odds are output here, so the number of nodes in the output layer can be one.

The neural network model may be pre-trained using first scores of multiple players and second scores for at least one competition. The learning process is the process of calculating a weighting factor for each node of the hidden layer of the neural network model using first scores of multiple players and second scores for at least one game.

The learning process is, for example, supervised learning. In supervised learning, for example, a certain player's first score and a certain game's second score are used as input teacher data, and betting odds in the game are used as output teacher data, and information of a plurality of players is used. We can build a machine learning model that can correlate the first score with the second score and the odds by calculating the weighting factor for each node in the hidden layer of the neural network model using . The machine learning model may be a sport-specific model or a general-purpose model that can be used for multiple sports. A generic model can be built, for example, by learning using a combination of multiple player first scores and multiple competition second scores.

For example, the (input training data, output training data) tuple for supervised learning of one sport-specific model is ((first player's first score, sport's second score), odds), ((second player first score, competition second score), odds), ... ((ith player first score, competition second score), odds ), . . . A new player's first score and a game's second score are input to the input layer of such a trained neural network model, and the betting odds for that game for that player are output to the output layer.

For example, a set of (teacher data for input, teacher data for output) for supervised learning of a general-purpose model for multiple sports is ((first player's first score, first game's second ), odds), ((second player's first score, first competition's second score), odds), ... ((i-th player's first score, first second score of the competition), odds), ... ((first player's first score, second competition's second score), odds), ((second player's first score, second score of second competition), odds), ... ((first score of i-th player, second score of second competition), odds), ... ( (first player first score, jth competition second score), odds), ((second player first score, jth competition second score), odds) , . Inputting a new player's first score and a second score for one of a plurality of sports into the input layer of such a trained neural network model, the betting odds for that player for that sport is output to the output layer.

If there are multiple players in the competition, the determining means 124 may independently determine the odds for each player using a machine learning model, or may determine the odds for each player. may be input into a machine learning model to determine odds. This may be done, for example, using a machine learning model built by learning with a first score of each of a plurality of players and a second score of the competition.

The plurality of players may, for example, be non-human players, such as computer players (non player characters: NPU), preferably avatars or digital clones that copy the characteristics of the human player. In this case, the first calculation means 122 calculates the first score of the first human player and the first score of the avatar of the first player. Since the first score of the first player's avatar will be calculated based on the first player's data and/or information, the first player's first score and the first player's It is the same as or substantially the same as the avatar's first score. The determining means 124 will determine odds based on the first player's first score, the first player's avatar's first score, and the second score. At this time, the determining means 124 determines that humans have uncertain factors that are easily influenced by circumstances, etc., and that avatars are not good at responding to first-time rules, while humans are good at responding to first-time rules. and/or other factors, the first player's first score and the first player's avatar's first score may be weighted to determine the odds.

The odds determined in this manner are output outside the system 100 . For example, the odds are transmitted to the user terminal device 400 via the interface unit 110 and provided to the user.

FIG. 3B shows an example of the configuration of processor section 120 ′, which is an alternative embodiment of processor section 120 .

The processor section 120 ′ has the same configuration as the processor section 120 except that it includes a changing means 125 . In FIG. 3B, the same reference numerals are given to the same components as those in the configuration described above with reference to FIG. 3A, and the description thereof is omitted here.

The processor unit 120 ′ includes an acquisition means 121 , a first calculation means 122 , a second calculation means 123 , a determination means 124 and a change means 125 .

The changing means 125 is configured to be able to change the rules of the competition.

The changing means 125 can change the rules of the competition, for example, in response to requests from the outside. For example, the change means 125 can receive votes from outside the system 100 via the interface unit 110 and change the rules of the competition based on the results of the votes. A voter can be, for example, a stakeholder 40 as described above. Parties 40 have voting rights (eg, governance tokens). The person concerned 40 may be, for example, the player 20, the user 30, or a person who is neither the player 20 nor the user 30. FIG. For example, the parties 40 will have voting rights according to the amount of governance tokens they have. Thus, a party 40 with more governance tokens will have more voting rights and may have an advantage in voting. Governance tokens may be issued by system 100 .

The changing means 125 can present at least one rule change candidate to the voter terminal device 500 via the interface unit 110, for example. The change means 125 can receive a vote for at least one rule change candidate from the voter terminal device 500 via the interface unit 110 . As a result of tallying the votes, if the change to at least one rule change candidate has a majority of votes, the rule change candidate is adopted and the current rule is changed. Alternatively, the rule change candidate that has obtained the largest number of votes among the plurality of rule change candidates is adopted, and the current rule is changed.

Voting by parties with governance tokens may be conducted using techniques known in the art. For example, it can be done using blockchain technology.

The changing means 125 can change the rules of the competition, for example, regardless of requests from the outside. For example, when a predetermined condition is satisfied (for example, when at least one of a predetermined time, a predetermined number of spectators, a predetermined number of players, and a predetermined total bet amount is satisfied), the changing means 125 rules can be changed. For example, the changing means 125 may change the current rule to a predetermined rule, or may change the current rule to a rule according to the extent to which a predetermined condition is satisfied.

When the rule is changed by the changing means 125, the second calculating means 123 can calculate the second score based on the changed rule.

The second calculating means 123 adjusts the reference score in consideration of the effect of the changed rules on the ability or characteristics necessary or preferable to win the competition or to play well in the competition. A second score can be calculated by The second calculation means 123 can obtain the output of the second score by, for example, inputting the modified rule to the constructed machine learning model.

Then, the determining means 124 determines the betting odds in the competition under the changed rule based on the output score.

In this way, the processing by the processor unit 120' changes the rules of the game and determines appropriate odds even under the changed rules. This allows the user to always have a fresh and unexpected experience while still making fair bets.

FIG. 3C shows an example of the configuration of processor section 120 ′, which is an alternative embodiment of processor section 120 .

The processor unit 120 ″ has the same configuration as the processor unit 120 except that it includes a second acquisition unit 126 and a third calculation unit 127 . In FIG. 3C, the same reference numerals are given to the same components as those in the configuration described above with reference to FIG. 3A, and the description thereof is omitted here.

The processor unit 120' includes acquisition means 121, first calculation means 122, second calculation means 123, determination means 124', second acquisition means 126, and third calculation means 127. . The processor unit 120'' may further comprise modifying means 125 as described above with reference to FIG. 3B. Also, the processor unit 120 ″ may further comprise a second determining means 128 .

The second obtaining means 126 may be configured to obtain data and/or information regarding the betting user.

The second acquisition means 126 may acquire data and/or information about the user stored in the database section 200 via the interface section 110, for example. Alternatively, the acquisition means 121 may acquire data and/or information regarding the user received from the user terminal device 400 via the interface section 110, for example. The obtained data and/or information about the user are passed to the third computing means 127 .

The third calculating means 127 is configured to calculate a third score representing characteristics of the user based on data and/or information about the user. A user's characteristics can be a concept that represents what kind of person the user is, that is, the user's personality. User characteristics can also describe a user's personality in terms of the user's personality. For example, user characteristics can describe a user's personality in terms of "personality." A user's characteristics may additionally describe the user's personality in terms of "money" and/or may describe the user's personality in terms of "health." Therefore, the third score representing the characteristics of the user may include the feature quantity related to "personality", and the third score representing the user characteristics may include the feature quantity related to "money" and/or "health". It can be a multi-dimensional score that additionally includes features for . Although the term “digital twin” is well known, it can be said that the third score that multidimensionally represents the features of the user is a score that represents the user's digital twin.

The third score multidimensional axis can include, for example, a "personality" perspective axis, a "money" perspective axis, and/or a "health" perspective axis.

The third calculator 127 can calculate the third score by using the same method as the method described above for the first calculator 122 . A detailed description is omitted here.

The calculated third score is passed to decision means 124'.

Determining means 124' is similar to determining means 124, except that in addition to the first and second scores, the determining means 124' may also determine odds for betting on the competition based on a third score. The determining means 124' is also based on the third score and thus can determine odds according to the user. For example, for good or regular users, favorable odds may be determined, and for bad or seeming users, normal or discredited odds may be determined. can do.

The determining means 124' may determine odds, for example, by adjusting the odds determined based on the first score and the second score based on the third score. For example, the odds determined based on the first score and the second score can be increased or decreased depending on whether the predetermined feature included in the third score is higher or lower than the average. can.

Alternatively, the determining means 124' can determine the odds, for example, using a machine learning model that has learned the relationship between the first score, the second score, the third score, and the odds.

The odds determined in this manner are output outside the system 100 . For example, the odds are transmitted to the user terminal device 400 via the interface unit 110 and provided to the user.

In one embodiment, the processor portion 120 ″ may further comprise a second determining means 128 .

A second determining means 128 is configured to determine a preferred bet for the user based on the third score.

In one example, each of a plurality of features that can be included in the third score is associated with a specific game or a bet on that game, and the second determining means 128 determines the features included in the third score. A bet on the corresponding competition can be determined as the preferred bet for the user.

In another example, second determining means 128 may derive a particular feature from the third score and determine a bet based on the derived feature. For example, each of a plurality of features that can be derived from the third score is associated with a particular game or bet on a game, and the second determining means 128 corresponds to the feature derived from the third score. A bet on a competition to be played may be determined as a preferred bet for the user. For example, a bet on a specific game or game is associated with each combination of some of the plurality of feature quantities that can be derived from the third score, and the second determining means 128 determines that the third A bet on a game corresponding to a combination of feature amounts derived from the scores of , can be determined as a bet suitable for the user.

The second determining means 128 can derive a specific feature from the third score in any manner. In one example, the second determining means 128 can extract specific features from the features included in the third score. Here, the extraction of the feature amount means extracting the feature amount as it is without changing the value of the feature amount. In another example, the second determining means 128 can derive a specific feature by applying a specific function to the third score or features included in the third score. Here, the specific function can be any function. For example, a particular function can be a hash function.

The features extracted from the third score can be changed according to the sport for which bets are made. For example, the second determining means 128 can extract a specific feature amount according to the game from the feature amounts included in the third score. For example, the second determining means 128 can derive a specific feature amount by applying a specific function according to the game to the score or the feature amount included in the score.

In this way, the processing by the processor unit 120 ″ determines the betting odds in the competition in consideration of the characteristics of the betting user, and can provide the user with a betting experience suitable for the user. Also, the bets made by the user may themselves be suitable for the user.

In the examples shown in FIGS. 3A, 3B, and 3C described above, each component of the processor units 120, 120′, and 120″ is provided within the same processor unit 120, but the present invention is limited to this. not. It is within the scope of the invention that each component of the processor portions 120, 120', 120'' is distributed over multiple processor portions. At this time, the plurality of processor units may be located within the same hardware component, or may be located within separate nearby or remote hardware components.

Note that each component of the system 100 described above may be configured with a single hardware component, or may be configured with a plurality of hardware components. When configured with a plurality of hardware components, it does not matter how the hardware components are connected. Each hardware component may be connected wirelessly or by wire. The system 100 of the present invention is not limited to any particular hardware configuration. It is also within the scope of the present invention for the processor portions 120, 120', 120'' to be implemented with analog circuitry rather than digital circuitry. The configuration of the system 100 of the present invention is not limited to those described above as long as its functions can be realized.

3. Processes in a System for Supporting Betting on Sports FIG. 5A shows an example of processes (process 500) in system 100 for supporting wagering on sports. Process 500 is performed in processor portion 120 of system 100 . Process 500 may be performed by similar processes in processor portion 120' or processor portion 120''. Process 500 is performed, for example, in the example described above with reference to FIG. 1A, after players 20A and 20B have entered in step S1 and before the user places a bet in step S2.

At step S501, the acquisition means 121 of the processor unit 120 acquires data and/or information regarding at least one player. The acquisition means 121 may acquire data and/or information regarding players stored in the database section 200 via the interface section 110, for example. Alternatively, the acquiring means 121 may acquire data and/or information regarding the player received from the player terminal device 300 via the interface section 110, for example. Acquisition means 121 may further acquire data on the results of competitions.

At step S502, the first calculation means 122 of the processor unit 120 calculates a first score based on the data and/or information regarding at least one player obtained at step S501. If data and/or information regarding a plurality of players are acquired in step S501, a first score is calculated for each of the plurality of players. The first score is a score that represents the characteristics of the player. The first calculation means 122 can calculate the first score depending on, for example, the extent to which the data and/or information about the player correlates with the parameters used to calculate the first score. The first calculation means 122, for example, refers to the database unit 200 storing various information correlated with the concept of the parameters used for calculating the first score, and the data and/or information about the player is the first score. determines the degree of correlation with the concept of the parameter used to calculate the score of Data and/or information about a player may include, for example, information about "personality," "money," and "health."

The first calculation means 122 may, for example, determine the extent to which the data and/or information about the player correlates with the concept of "personality" and calculate the first score according to the determined extent of correlation. can. The first calculation means 122 can, for example, determine the degree of correlation between the data and/or information regarding the player and the concept regarding "money" and calculate the first score according to the determined degree of correlation. can. The first calculating means 122 may, for example, determine the extent to which the data and/or information regarding the player correlates with the concept relating to "health", and calculate the first score according to the determined extent of correlation. can.

The first calculation means 122 uses, for example, a machine learning model that has learned the relationship between data and/or information about a plurality of players and the first score, and multidimensionally expresses the characteristics of the player. A score can be calculated. The data and/or information about the player includes, for example, information about "personality" and may further include information about "money" and "health."

For example, inputting data and/or information about the player obtained in step S501 into a machine learning model outputs a first score for the player.

In step S503, the second calculator 123 of the processor unit 120 calculates a second score. A second score is a score that represents the characteristics of the game.

The second calculation means 123 can calculate the second score, for example, based on a reference score preset for each game. For example, if the rules of the competition are the same as the normal rules, the second calculation means 123 can regard the reference score as the second score. For example, if the rules of the game differ from the normal rules, the second calculation means 123 may apply the rules of the game to the abilities or characteristics necessary or suitable for winning the game or playing well in the game. A second score can be calculated by adjusting the reference score to account for the influence of . For example, the second calculation means 123 may calculate the reference score by considering the impact of the conditions under which the competition is held on the abilities or characteristics necessary or desirable to win the competition or to play well in the competition. A second score can be calculated by adjusting.

For example, the second computing means 123 may include a machine learning model that has learned the impact of the rules and/or circumstances of a game on the abilities or characteristics required or preferred to win the game or to play well in the game. can be used to calculate the second score.

In step S504, the determination unit 124 of the processor unit 120 determines betting odds for the game based on the first score calculated in step S502 and the second score calculated in step S503. The determining means 124 may determine the betting odds based on the data of the performance of the competition that may be obtained in step S501, but the data of the performance of the competition is not essential. Rather, betting odds can be appropriately determined without using data on the performance of the competition.

For example, the determining means 124 can determine odds based on the correlation between the first score and the second score. At this time, the determining means 124 can extract a feature amount that can be compared with the second score from the first score, and obtain a correlation between the extracted feature amount and the second score. For example, the determining means 124 lowers the odds as the correlation between the first score and the second score is higher, and raises the odds as the correlation between the first score and the second score is higher. can.

When the first score is calculated for each of the plurality of players in step S502, the determining means 124 may independently determine the odds for each player, or may determine the odds for each player. Odds may be determined by taking into account the correlation of scores of 1. For example, if a first player's first score is compared with a second player's first score and it is determined that the first player is compatible with the second player, then the first player's It can be subtracted from the player's odds and added to the second player's odds.

The determining means 124 may determine the odds based on rules or may determine the odds using a machine learning model.

For example, odds are output by inputting a first score and a second score into a machine learning model.

The odds determined by process 500 will be output to the outside of system 100 . For example, the odds are transmitted to the user terminal device 400 via the interface unit 110 and provided to the user. A user can place a bet by referring to the odds.

In the process 500, since the data of the performance of the competition is not essential, even if the players are playing against each other for the first time, or even if the players are playing the game for the first time, the odds are set appropriately. can decide. This is particularly useful in esports competitions. This is because appropriate odds can be determined by the process 500 regardless of when the e-sports competition is held or who participates in the e-sports competition.

FIG. 5B shows the continuation of process 500 . This process is a process for changing the rules of the competition after step S504 and accordingly changing the odds. This processing can be performed in the processor unit 120'. This processing can be performed by the same processing in the processor unit 120''.

At step S505, the changing means 125 of the processor unit 120' changes the rules of the competition. The changing means 125 can change the rules of the competition, for example, in response to an external request or regardless of the external request.

For example, the change means 125 can receive votes from outside the system 100 via the interface unit 110 and change the rules of the competition based on the results of the votes. The changing means 125 presents, for example, at least one rule change candidate to the voter terminal device 500 via the interface unit 110, and receives a vote for the at least one rule change candidate from the voter terminal device 500 via the interface unit 110. to receive. Then, as a result of tallying the votes, the change means 125 can adopt the rule change candidate and change the rules of the competition when the change to at least one rule change candidate has a majority of votes. Alternatively, the changing means 125 can adopt the rule change candidate that has obtained the largest number of votes among the plurality of rule change candidates, and change the rules of the competition.

For example, when a predetermined condition is satisfied (for example, when at least one of a predetermined time, a predetermined number of spectators, a predetermined number of players, and a predetermined total bet amount is satisfied), the changing means 125 rules can be changed. For example, the changing means 125 may change the current rule to a predetermined rule, or may change the current rule to a rule according to the extent to which a predetermined condition is satisfied.

At step S506, the second calculation means 123 of the processor unit 120' can change the second score based on the rule changed at step S505. The second calculating means 123 may, for example, take into account the impact of the changed rules on abilities or characteristics necessary or desirable to win the competition or to play well in the competition. A second score can be adjusted. The second calculation means 123 may newly obtain the output of the second score by, for example, inputting the changed rule to the constructed machine learning model.

At step S507, the determining means 124 of the processor unit 120' can determine and update the odds based on the first score calculated at step S502 and the second score changed at step S506. . The processing of step S507 is the same as the processing of step S504.

In this way, even if the rules in the competition are changed, the proper odds will be determined. This can provide the user with a constantly fresh and unexpected user experience while still allowing the user to place fair bets. This is particularly useful in esports competitions. This is because, in e-sports competitions, it is particularly easy to change the rules of the competition, and appropriate bets can be provided even though the rules are frequently changed.

In the above example, it was described that the odds are updated by changing the rules after the betting odds have been determined. It is also within the scope of this invention to change the rules before they are determined and determine the odds based on the changed rules.

FIG. 6 shows another example of a process (process 600) in system 100 for supporting betting on a competition. Process 600 also occurs in processor portion 120 ″ of system 100 .

At step S601, the acquisition means 121 of the processor unit 120'' acquires data and/or information regarding at least one player. Step S601 is the same as step S501.

At step S602, the first calculation means 122 of the processor unit 120'' calculates a first score based on the data and/or information regarding at least one player acquired at step S601. Step S602 is similar to step S502.

In step S603, the second calculation means 123 of the processor unit 120'' calculates a second score. Step S603 is similar to step S503.

In step S604, the second acquiring means 126 of the processor unit 120'' acquires data and/or information regarding at least one betting user. The second acquisition means 126 may acquire data and/or information about the user stored in the database section 200 via the interface section 110, for example. Alternatively, the second acquisition means 126 may acquire data and/or information about the user received from the user terminal device 400 via the interface unit 110, for example.

In step S605, the third calculation means 127 of the processor unit 120'' calculates a third score based on the data and/or information regarding at least one user obtained in step S501. If data and/or information regarding multiple users are acquired in step S604, a third score is calculated for each of the multiple users. A third score is a score that represents a user's characteristics. The third calculator 127 can calculate the third score depending on, for example, the extent to which the data and/or information about the user are correlated with the parameters used to calculate the third score. The third calculation means 127, for example, refers to the database unit 200 storing various information correlated with the concept of the parameter used for calculating the third score, and the data and/or information about the user is the third score. determines the degree of correlation with the concept of the parameter used to calculate the score of Data and/or information about the user may include, for example, information about "personality," "money," and "health."

The third calculation means 127 may, for example, determine the extent to which the data and/or information about the user correlates with the concept of "personality" and calculate a third score according to the determined extent of correlation. can. The third calculation means 127 can, for example, determine the degree of correlation between the data and/or information regarding the user and the concept regarding "money" and calculate the third score according to the determined degree of correlation. can. The third calculation means 127 can, for example, determine the degree of correlation between the data and/or information regarding the user and the concept regarding "health" and calculate the third score according to the determined degree of correlation. can.

The third calculation means 127 uses, for example, a machine learning model that has learned the relationship between data and/or information about a plurality of users and the third score, and multidimensionally represents the user's characteristics. A score can be calculated. The data and/or information about the user includes, for example, information about "personality" and may further include information about "money" and "health."

For example, inputting the data and/or information about the user obtained in step S604 into a machine learning model outputs the user's third score.

In step S606, the determination unit 124' of the processor unit 120'' determines the first score calculated in step S602, the second score calculated in step S603, and the third score calculated in step S605. and determine the odds for betting on the competition. The determining means 124' may also determine the betting odds based on the competition performance data that may be obtained in step S601, although the competition performance data is not essential. Rather, betting odds can be appropriately determined without using data on the performance of the competition.

For example, the determining means 124' may determine odds by, for example, adjusting the odds determined based on the first score and the second score based on the third score. For example, the odds determined based on the first score and the second score can be increased or decreased depending on whether the predetermined feature included in the third score is higher or lower than the average. can. This allows for favorable odds to be determined for good or regular users, and normal or discredited odds for bad or seeming users. can decide.

When the first score is calculated for each of the plurality of players in step S602, the determining means 124' may independently determine the odds for each player, or may determine the odds for each player. Odds may be determined in consideration of the correlation of the first scores. For example, if a first player's first score is compared with a second player's first score and it is determined that the first player is compatible with the second player, then the first player's It can be subtracted from the player's odds and added to the second player's odds.

The determiner 124' may determine the odds on a rule basis or may utilize a machine learning model to determine the odds.

For example, inputting a first score, a second score, and a third score into a machine learning model outputs odds.

The odds determined by process 600 will be output to the outside of system 100 . For example, the odds are transmitted to the user terminal device 400 via the interface unit 110 and provided to the user. A user can place a bet by referring to the odds.

In the process 600, since the data of the performance of the competition is not essential, even if the players are playing against each other for the first time, or even if the players are playing the game for the first time, the odds are set appropriately. can decide. This is particularly useful in esports competitions. This is because appropriate odds can be determined by the process 600 regardless of when the e-sports competition is held or who participates in the e-sports competition. Furthermore, the process 600 will also consider the characteristics of the betting user to determine the odds of betting on the competition, thereby providing a betting experience suitable for the user.

Although the examples described above with reference to FIGS. 5A, 5B, and 6 illustrate that the operations are performed in a particular order, the order of the operations is not limited to that described and is logically possible. It can be done in any order. For example, step S503 can be performed before performing step S501. For example, step S603 or step S604 can be performed before performing step S601.

In the examples described above with reference to FIGS. 5A, 5B, and 6, the processing of each step shown in FIGS. Although described as being implemented by stored programs, the present invention is not so limited. At least one of the processing of each step shown in FIGS. 5A, 5B, and 6 may be implemented by a hardware configuration such as a control circuit.

The invention is not limited to the embodiments described above. It is understood that the invention is to be construed in scope only by the claims. It is understood that a person skilled in the art can implement an equivalent range from the description of specific preferred embodiments of the present invention based on the description of the present invention and common technical knowledge.

INDUSTRIAL APPLICABILITY The present invention is useful in providing a system or the like for supporting betting in competitions, capable of presenting appropriate odds to betting users.

10 platform 11 rule 12 odds 20A, 20B player 30 user 40 stakeholder 100 system 200 database unit 300 player terminal device 400 user terminal device 500 voter terminal device

Claims (11)

A system for supporting betting in a competition, comprising:
obtaining means for obtaining data and/or information regarding at least one player of the competition;
a first calculating means for calculating a first score characteristic of said at least one player based on said obtained data and/or information about said at least one player, said first calculating means means for extracting said first information of said at least one player from said first machine learning model by inputting said obtained data and/or information relating to said at least one player into said first machine learning model; A score is obtained, and the first machine learning model learns about a plurality of players using player data and/or information as input teacher data and a first score representing the characteristics of the player as output teacher data. a first calculating means,
A second calculation means for calculating a second score representing a characteristic of said game, said second score being the ability or characteristic necessary to win said game or suitable for winning said game. ability or characteristic, or ability or characteristic necessary for playing well in the competition, or ability or characteristic suitable for playing well in the competition , wherein the second calculating means is a second Obtaining the second score for the game from the second machine learning model by inputting the rules for the game and a reference score into a machine learning model, the second machine learning model receiving the rules for the game and a second calculation means for learning about a plurality of competitions using a reference score as input teacher data and a second score representing the characteristics of the competition as output teacher data ;
determining means for determining betting odds for a player participating in said competition based on said correlation by correlating said first score with said second score. Further comprising a change means for changing the rules of the competition,
When the rule is changed by the changing means,
The second calculation means calculates the second score based on the changed rule,
The determining means determines the odds based on the first score and a second score calculated based on the changed rule.
The system of claim 1. The changing means is
receiving votes from those involved in said competition on changes to said rules;
3. The system of claim 2, modifying the rules based on results of the voting. The competition is an e-sports competition,
4. The system of claim 3, wherein the affiliate is a person holding a governance token for the competition. The competition is an e-sports competition,
The system according to any one of the preceding claims, wherein said at least one player and said at least one player's avatar participate in competition in said e-sports . a second obtaining means for obtaining data and/or information relating to said betting user;
a third calculation means for calculating a third score representing characteristics of the user based on the obtained data and/or information regarding the user;
A system according to any preceding claim, wherein said determining means determines said odds further based on said third score. A system according to any one of the preceding claims, wherein data and/or information relating to said at least one player are stored in a tamper-proof manner. A system according to any preceding claim, wherein said obtaining means obtains said data from a device associated with said at least one player. 9. The system according to any one of claims 1 to 8 , wherein said acquisition means acquires information disclosed subjectively by said player. A method for supporting betting in a competition, said method being executed in a computer system comprising a processor unit, said method comprising:
said processor unit obtaining data and /or information regarding at least one player in a competition;
calculating, by the processor unit, a first score characteristic of the at least one player based on the obtained data and/or information about the at least one player; Calculating a score includes calculating the score of the at least one player from the first machine learning model by inputting the obtained data and/or information about the at least one player into the first machine learning model. The first machine learning model includes obtaining the first score, and the first machine learning model uses data and/or information about the player as input teacher data, and the first score representing the characteristics of the player as output teacher data. , that we are learning about multiple players, and
The processor unit calculates a second score representing a characteristic of the game, the second score being the ability or characteristic required to win the game or representing a favorable ability or characteristic, or a necessary ability or characteristic to play well in said competition, or a preferable ability or characteristic to play well in said competition, and calculating said second score; , obtaining the second score of the game from the second machine learning model by inputting the rules of the game and a reference score into the second machine learning model ; The learning model learns a plurality of competitions using the competition rules and standard scores as input teacher data and the second scores representing the characteristics of the competition as output teacher data ;
the processor unit correlating the first score with the second score to determine betting odds for a player participating in the competition based on the correlation. . 1. A program for assisting in betting on a competition, said program being executed on a computer system comprising a processor, said program comprising:
obtaining data and /or information regarding at least one player of the competition;
Calculating a first score characteristic of the at least one player based on the obtained data and/or information about the at least one player, wherein the first score is calculated. calculating said first score of said at least one player from said first machine learning model by inputting said obtained data and/or information regarding said at least one player into said first machine learning model; The first machine learning model uses data and/or information about players as input training data, and a first score representing the characteristics of the player as output training data for a plurality of players learning and
Calculating a second score representing a characteristic of the game, wherein the second score is the ability or characteristic required to win the game, or the ability or characteristic preferable to win the game or representing an ability or characteristic required to play well in said competition, or an ability or characteristic suitable for playing well in said competition, and calculating said second score is performed by a second machine obtaining the second score for the game from the second machine learning model by inputting rules for the game and a reference score into a learning model , wherein the second machine learning model is configured to: learning about a plurality of competitions using the rules and reference scores of the above as training data for input, and the second score representing the characteristics of the competition as training data for output ;
correlating the first score with the second score to determine betting odds for a player participating in the competition based on the correlation. ,program.

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