system

Abstract

The system according to this embodiment aims to suggest a card deck that suits the user's personality and preferences, and to support practice matches. [Solution] The system according to the embodiment comprises a suggestion unit, a battle unit, an analysis unit, an improvement unit, and a digitization unit. The suggestion unit suggests an optimal card deck based on the user's personality, preferred style, past deck information, and battle history. The battle unit performs a virtual battle against an AI using the deck suggested by the suggestion unit. The analysis unit visually analyzes the results of the battle performed by the battle unit. The improvement unit suggests improvements to the play style based on the results obtained by the analysis unit. The digitization unit digitizes the usage information of the physical deck.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in a card game, it is difficult to construct a deck that suits one's personality and way of thinking, and there are few opportunities for battle practice. Therefore, there is a problem that it is difficult to practice assuming various battle patterns.

[0005] The system according to the embodiment aims to propose a card deck that suits the user's personality and preferences and support battle practice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a suggestion unit, a battle unit, an analysis unit, an improvement unit, and a digitization unit. The suggestion unit suggests an optimal card deck based on the user's personality, preferred style, past deck information, and battle history. The battle unit conducts a virtual battle against an AI using the deck suggested by the suggestion unit. The analysis unit visually analyzes the results of the battle conducted by the battle unit. The improvement unit suggests improvements to the play style based on the results obtained by the analysis unit. The digitization unit digitizes the usage information of the physical deck. [Effects of the Invention]

[0007] The system according to this embodiment can suggest a card deck that suits the user's personality and preferences, and can support practice matches. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The card game support system according to an embodiment of the present invention is a system that uses AI to support users who enjoy card games in building optimal decks and practicing matches. Based on the user's personality, preferred style, past deck information, and battle history, the AI ​​suggests the optimal card deck. Next, it is equipped with a virtual battle function with the AI, allowing users to try out various strategies. Furthermore, it is equipped with a function that allows the AI ​​to control the opponent on the virtual battlefield and visually analyze their strategy. It also provides a service in which the AI ​​analyzes the user's play style and suggests improvements to overcome weaknesses. In addition, IoT functionality for digitizing the usage information of physical decks enables the AI ​​to analyze play data in real time. As a result, the card game support system provides an environment in which people who enjoy card games as a hobby can enjoy them more. For example, users can build decks that suit their personality and play style and learn various strategies through matches with the AI. Furthermore, they can improve their play style through improvement suggestions provided by the AI. In addition, by digitizing the usage information of physical decks, it is possible to check the deck's performance in real time and build the optimal deck.

[0029] The card game support system according to this embodiment comprises a suggestion unit, a battle unit, an analysis unit, an improvement unit, and a digitization unit. The suggestion unit suggests an optimal card deck based on the user's personality, preferred style, past deck information, and battle history. For example, the suggestion unit uses AI to analyze decks and battle results previously used by the user and suggests a deck that suits the user's play style. For example, the suggestion unit suggests a deck containing many cards with high attack power for a user who prefers an aggressive play style. The suggestion unit can also suggest a deck containing many cards with high defensive power for a user who prefers a defensive play style. Furthermore, the suggestion unit can suggest a deck with a balance of attack and defense for a user who prefers a balanced play style. The battle unit conducts a virtual battle against the AI ​​using the deck suggested by the suggestion unit. For example, the battle unit allows the user to practice in an environment similar to an actual battle by playing against the AI. The battle unit allows the AI ​​to manipulate the opponent using different strategies, and the user can learn how to respond to those strategies. For example, the battle unit allows the user to try a defensive strategy when the AI ​​employs an aggressive strategy. Furthermore, the battle section allows users to try offensive strategies when the AI ​​employs a defensive strategy. Additionally, when the battle section employs a balanced strategy, users can try various strategies. The analysis section visually analyzes the results of battles conducted by the battle section. For example, the analysis section includes a function that allows the AI ​​to control an opponent on a virtual battlefield and visually analyze their strategy. The analysis section allows users to visually observe the movements of the AI-controlled opponent and understand their strategy. For instance, the analysis section visually indicates the timing and order in which the AI ​​uses specific cards, allowing users to learn the strategy. The analysis section can also visually demonstrate the effects of strategies employed by the AI ​​during a battle. For example, the analysis section can display the effects of cards used by the AI ​​using graphs or heatmaps. The improvement section suggests improvements to play style based on the results obtained by the analysis section. For example, the improvement section uses the AI ​​to analyze mistakes and areas for improvement made by the user during a battle and provides specific advice.The improvement unit can enhance the user's play style by suggesting changes to the timing of using specific cards or to strategies. For example, the improvement unit can suggest that the user improve the timing of using specific cards. It can also suggest that the user change a specific strategy. Furthermore, it can suggest that the user try a new strategy. The digitization unit digitizes the usage information of the physical deck. For example, the digitization unit collects information about the physical deck actually used by the user using sensors, and the AI ​​analyzes that data. The digitization unit allows the user to check the deck's performance in real time and adjust the deck as needed. For example, the digitization unit digitizes the types and frequency of cards used by the user, and the AI ​​evaluates the deck's performance based on that data. As a result, the card game support system according to the embodiment can suggest an optimal deck based on the user's personality and play style, learn strategies through playing against the AI, and improve their play style.

[0030] The suggestion department proposes the optimal card deck based on the user's personality, preferred style, past deck information, and battle history. For example, the suggestion department uses AI to analyze the decks and battle results the user has used in the past and proposes a deck that suits the user's play style. Specifically, the AI ​​analyzes the user's past battle data in detail, analyzing win rates, frequently used cards, and reactions to opponents' strategies. This allows the AI ​​to understand what kind of cards the user likes and what kind of strategies they excel at. For example, for a user who prefers an aggressive play style, the suggestion department will propose a deck containing many high-attack cards. In this case, the AI ​​will select combinations of high-attack cards and cards suitable for aggressive strategies. The suggestion department can also propose a deck containing many high-defense cards for a user who prefers a defensive play style. In this case, the AI ​​will propose combinations of high-defense cards and cards that effectively defend against the opponent's attacks. Furthermore, for a user who prefers a balanced play style, the suggestion department can propose a deck with a balance of offense and defense. The AI ​​will consider the balance of offense and defense and construct a deck that allows the user to respond to various situations. The suggestion department can collect user feedback when making these suggestions and continuously improve the suggestions. For example, the AI ​​can analyze the results of users using the suggested decks and use that information to make future suggestions more accurate. The suggestion department can also suggest the optimal deck configuration when users add new cards or modify existing ones. In this way, the suggestion department can suggest the best deck tailored to the user's play style and preferences, improving the user's gaming experience.

[0031] The Battle Section conducts virtual matches against AI using decks proposed by the Proposal Section. For example, the Battle Section allows users to practice in an environment similar to actual matches by playing against the AI. Specifically, the Battle Section uses advanced AI algorithms to generate virtual opponents with various strategies. The AI ​​employs different strategies, such as offensive, defensive, and balanced, to provide users with diverse match scenarios. For example, if the AI ​​adopts an offensive strategy, the user can try a defensive strategy. In this case, the AI ​​actively uses high-attack cards to put pressure on the user. Similarly, if the AI ​​adopts a defensive strategy, the user can try an offensive strategy. In this case, the AI ​​uses high-defense cards to effectively block the user's attacks. Furthermore, if the AI ​​adopts a balanced strategy, the user can try various strategies. The AI ​​balances offense and defense while requiring diverse responses from the user. Through these match scenarios, the Battle Section helps users develop their ability to adapt to different strategies. Furthermore, the battle department will record battle results in real time, allowing users to review the effectiveness of their play style and strategies. This enables the battle department to practice in an environment similar to actual battles and refine their strategies. The battle department will also record the choices and actions taken by users during battles, allowing the analysis and improvement departments to utilize this data later. This allows the battle department to support user skill improvement and enhance the gaming experience.

[0032] The analysis unit visually analyzes the results of matches played by the battle unit. For example, the analysis unit includes a function that allows the AI ​​to control an opponent on a virtual battlefield and visually analyze its strategy. Specifically, the analysis unit analyzes each phase of the match in detail, visually showing when the user used which cards and how the AI ​​reacted. For instance, by visually showing the timing and order in which the AI ​​uses specific cards, the analysis unit allows users to learn the strategy. This enables users to understand the AI's strategy and incorporate it into their own play style. The analysis unit can also visually show the effects of strategies employed by the AI ​​during a match. For example, the analysis unit can display the effects of cards used by the AI ​​using graphs and heatmaps. This allows users to intuitively understand how specific cards and strategies influenced the match outcome. Furthermore, the analysis unit can statistically analyze the match results to clearly identify the user's strengths and weaknesses. For example, by showing the success rate of a user using specific cards or the win rate against specific strategies, users can objectively evaluate their own play style. The analytics department provides users with these visual analysis results, enabling them to obtain specific guidance for improving their play style. This allows the analytics department to enable users to analyze match results in detail, learn strategies, and improve their play style.

[0033] The Improvement Department proposes improvements to playstyle based on the results obtained by the Analysis Department. For example, the Improvement Department uses AI to analyze mistakes and areas for improvement made by users during matches and provides specific advice. Specifically, the Improvement Department analyzes the user's match data in detail, showing when and which cards should have been used and which strategies were more effective. For example, the Improvement Department may suggest that the user improve the timing of using a particular card. Based on past match data, the AI ​​analyzes the optimal timing for using a particular card and provides the results to the user. The Improvement Department can also suggest that the user change a particular strategy. For example, if the AI ​​takes an offensive strategy when a defensive strategy should have been taken, it will show how that choice affected the match outcome and suggest areas for improvement in the next match. Furthermore, the Improvement Department can also suggest that the user try a new strategy. Based on the user's playstyle and preferences, the AI ​​suggests new strategies and card combinations, providing the user with an opportunity to try new strategies. Through these suggestions, the Improvement Department enables users to continuously improve their playstyle and enhance their match skills. The Improvement Department can also collect user feedback and continuously improve the suggestions. This allows the improvement team to provide users with specific advice to help them improve their play style, thereby enhancing the gaming experience.

[0034] The digitalization unit digitizes the usage information of physical decks. For example, the digitalization unit collects information about the physical decks actually used by users using sensors, and AI analyzes that data. Specifically, the digitalization unit uses sensors that read RFID tags embedded in cards or barcodes to collect the types of cards used and their frequency of use in real time. This allows for detailed recording of which cards the user used, when, and which cards were most effective. For example, the digitalization unit digitizes the types and frequency of cards used by users, and the AI ​​evaluates the deck's performance based on that data. The AI ​​can analyze the collected data and clearly identify the strengths and weaknesses of the user's deck. Furthermore, the digitalization unit allows users to check the deck's performance in real time and adjust the deck as needed. For example, when a user checks the effect of a specific card during a match or re-evaluates the deck's balance, the digitalization unit can provide that information immediately. In addition, the digitalization unit enables users to seamlessly link physical and digital decks. This allows users to enjoy the convenience of digital decks while still using physical decks. Through these functions, the digitalization section allows users to effectively utilize both physical and digital decks, thereby enhancing their gaming experience.

[0035] The suggestion unit can analyze data on the user's past opponents and suggest effective decks against specific opponents. For example, the suggestion unit can analyze the deck compositions of opponents the user has previously played against and suggest decks containing cards effective against those opponents. If the user is struggling against a particular opponent, the suggestion unit can also suggest decks to break through that opponent's strategy. The suggestion unit can also refer to decks of opponents the user has previously defeated and suggest decks employing similar strategies. This allows the user to improve their win rate by suggesting effective decks against specific opponents. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's past opponents into a generating AI and have the generating AI suggest effective decks against specific opponents.

[0036] The suggestion unit can adjust the difficulty of decks based on the user's current in-game rank and score. For example, if the user is a beginner, the suggestion unit may suggest a deck containing many basic cards. If the user is an intermediate player, the suggestion unit may also suggest a deck containing many strategic cards. If the user is an advanced player, the suggestion unit may also suggest a deck containing complex combos. In this way, by suggesting decks according to the user's rank and score, the system can provide decks of appropriate difficulty. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's in-game rank and score into a generating AI and have the generating AI perform the adjustment of deck difficulty.

[0037] The suggestion unit can propose decks that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit can propose a deck containing many ice-attribute cards. If the user lives in a tropical region, the suggestion unit can also propose a deck containing many fire-attribute cards. If the user lives in an urban area, the suggestion unit can also propose a deck containing many machine-attribute cards. In this way, by proposing decks that reflect region-specific strategies, a deck suitable for the user can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute a deck proposal that reflects region-specific strategies.

[0038] The suggestion unit can analyze a user's social media activity and suggest decks that reflect relevant trends. For example, the suggestion unit can suggest decks that include many cards the user is talking about on social media. The suggestion unit can also suggest decks that are based on decks used by influencers the user follows. The suggestion unit can also suggest decks that include many cards popular in communities the user participates in. In this way, by suggesting decks that reflect social media trends, the suggestion unit can provide users with the latest decks. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI suggest decks that reflect trends.

[0039] The battle unit can refer to the user's past battle history to strengthen its countermeasures against specific strategies. For example, the battle unit can set up an AI with strengthened countermeasures against strategies that the user has struggled against in the past. The battle unit can also set up an AI that employs similar strategies against strategies that the user has won against in the past. The battle unit can also analyze strategies that the user has used in the past and set up an AI with strengthened countermeasures against those strategies. This allows the system to strengthen its countermeasures against specific strategies by referring to past battle history. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's past battle history data into a generating AI and have the generating AI perform strengthening of countermeasures against specific strategies.

[0040] The battle unit can dynamically change the AI's strategy according to the user's play style. For example, if the user adopts an aggressive play style, the AI ​​will adopt a defense-oriented strategy. If the user adopts a defensive play style, the AI ​​can also adopt an offensive strategy. If the user adopts a balanced play style, the AI ​​can flexibly change its strategy. This allows for more effective battles by providing strategies tailored to the user's play style. Some or all of the above processing in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's play style data into a generating AI and cause the generating AI to dynamically change its strategy.

[0041] The battle unit can conduct battles that reflect region-specific strategies by taking into account the user's geographical location information. For example, if the user lives in a cold region, the battle unit can set up an AI that uses many ice-attribute cards. If the user lives in a tropical region, the battle unit can also set up an AI that uses many fire-attribute cards. If the user lives in an urban area, the battle unit can also set up an AI that uses many machine-attribute cards. In this way, by providing battles that reflect region-specific strategies, a battle environment suitable for the user can be provided. Some or all of the above processing in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's geographical location information into a generating AI and have the generating AI execute a battle setting that reflects region-specific strategies.

[0042] The battle unit can analyze users' social media activity and conduct battles that reflect relevant trends. For example, the battle unit can set up an AI that uses many cards that users are talking about on social media. The battle unit can also set up an AI that references decks used by influencers that users follow. The battle unit can also set up an AI that uses many cards that are popular in the communities that users participate in. This allows the battle unit to provide users with the latest battle environment by offering battles that reflect social media trends. Some or all of the above processing in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can input user social media activity data into a generating AI and have the generating AI execute a battle setting that reflects trends.

[0043] The analysis unit can analyze the effectiveness of a particular strategy in detail by referring to the user's past battle data. For example, the analysis unit can analyze the win rate of strategies the user has used in the past and evaluate their effectiveness. The analysis unit can also analyze the strategies of opponents the user has played against in the past and evaluate their effectiveness. The analysis unit can also analyze the combinations of cards the user has used in the past and evaluate their effectiveness. In this way, the effectiveness of a particular strategy can be analyzed in detail by referring to past battle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past battle data into a generating AI and have the generating AI perform a detailed analysis of the effectiveness of a particular strategy.

[0044] The analysis unit can apply different analysis algorithms depending on the user's play style. For example, if the user has an aggressive play style, the analysis unit can apply an attack-oriented analysis algorithm. If the user has a defensive play style, the analysis unit can also apply a defense-oriented analysis algorithm. If the user has a balanced play style, the analysis unit can also apply a balance-oriented analysis algorithm. This allows for more effective analysis results by providing analysis tailored to the user's play style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user play style data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can perform analyses that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the analysis unit can analyze the effects of ice-attribute cards. If the user lives in a tropical region, the analysis unit can also analyze the effects of fire-attribute cards. If the user lives in an urban area, the analysis unit can also analyze the effects of machine-attribute cards. This allows the analysis unit to provide users with analysis results that are appropriate to their needs by providing analyses that reflect region-specific strategies. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform an analysis that reflects region-specific strategies.

[0046] The analytics department can analyze users' social media activity and perform analyses that reflect relevant trends. For example, the analytics department can analyze the effectiveness of cards that users are talking about on social media. The analytics department can also analyze the effectiveness of cards used by influencers that users follow. The analytics department can also analyze the effectiveness of cards that are popular in communities that users participate in. This allows the analytics department to provide users with up-to-date analysis results by providing analyses that reflect social media trends. Some or all of the above processes in the analytics department may be performed using AI, for example, or not using AI. For example, the analytics department can input user social media activity data into a generating AI and have the generating AI perform an analysis that reflects trends.

[0047] The improvement unit can refer to the user's past battle data and propose specific improvement measures for specific weaknesses. For example, the improvement unit can propose improvement measures for strategies that the user has struggled with in the past. The improvement unit can also analyze mistakes the user has made in the past and propose improvement measures. The improvement unit can also analyze the effects of cards the user has used in the past and propose improvement measures. In this way, by referring to past battle data, it is possible to provide specific improvement measures for specific weaknesses. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's past battle data into a generating AI and have the generating AI execute the task of proposing specific improvement measures for specific weaknesses.

[0048] The improvement unit can apply different improvement algorithms depending on the user's play style. For example, if the user has an aggressive play style, the improvement unit will propose an attack-oriented improvement. If the user has a defensive play style, the improvement unit can also propose a defense-oriented improvement. If the user has a balanced play style, the improvement unit can also propose a balance-oriented improvement. By providing improvement measures tailored to the user's play style, more effective improvements can be achieved. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user play style data into a generating AI and have the generating AI execute the application of different improvement algorithms.

[0049] The improvement unit can propose improvement measures that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the improvement unit can propose improvement measures that enhance the effect of ice-attribute cards. If the user lives in a tropical region, the improvement unit can also propose improvement measures that enhance the effect of fire-attribute cards. If the user lives in an urban area, the improvement unit can also propose improvement measures that enhance the effect of machine-attribute cards. In this way, by providing improvement measures that reflect region-specific strategies, the improvement unit can provide improvement measures that are suitable for the user. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without using AI. For example, the improvement unit can input the user's geographical location information into a generating AI and have the generating AI execute suggestions for improvement measures that reflect region-specific strategies.

[0050] The improvement unit can analyze users' social media activity and propose improvements that reflect relevant trends. For example, the improvement unit can propose improvements that enhance the effectiveness of cards that users are talking about on social media. The improvement unit can also propose improvements that enhance the effectiveness of cards used by influencers that users follow. The improvement unit can also propose improvements that enhance the effectiveness of cards that are popular in communities that users participate in. This allows the improvement unit to provide users with the latest improvements by offering improvements that reflect social media trends. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input user social media activity data into a generating AI and have the generating AI propose improvements that reflect trends.

[0051] The digitization unit can refer to the user's past deck usage data and record in detail the frequency of use of specific cards. For example, the digitization unit can record the frequency of use of cards that the user has used frequently in the past. The digitization unit can also record the effects of cards that the user has used in the past. The digitization unit can also record combinations of cards that the user has used in the past. This allows for detailed recording of the frequency of use of specific cards by referring to past deck usage data. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the user's past deck usage data into a generating AI and have the generating AI perform detailed recording of the frequency of use of specific cards.

[0052] The digitization unit can apply different digitization algorithms depending on the user's play style. For example, if the user adopts an aggressive play style, the digitization unit can apply an attack-oriented digitization algorithm. If the user adopts a defensive play style, the digitization unit can also apply a defense-oriented digitization algorithm. If the user adopts a balanced play style, the digitization unit can also apply a balance-oriented digitization algorithm. This allows for the provision of more effective data by offering digitization tailored to the user's play style. Some or all of the above-described processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input user play style data into a generating AI and have the generating AI execute the application of different digitization algorithms.

[0053] The digitization unit can prioritize the digitization of region-specific data, taking into account the user's geographical location information. For example, if the user lives in a cold region, the digitization unit can prioritize the digitization of ice-attribute card data. If the user lives in a tropical region, the digitization unit can also prioritize the digitization of fire-attribute card data. If the user lives in an urban area, the digitization unit can also prioritize the digitization of machine-attribute card data. This allows for the provision of data tailored to the user by prioritizing the digitization of region-specific data. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the user's geographical location information into a generating AI and have the generating AI perform the preferential digitization of region-specific data.

[0054] The digitization unit can analyze users' social media activity and digitize data that reflects relevant trends. For example, the digitization unit can digitize data on cards that users are talking about on social media. The digitization unit can also digitize data on cards used by influencers that users follow. The digitization unit can also digitize data on cards that are popular in communities that users participate in. This allows the digitization unit to provide users with the latest data by digitizing data that reflects social media trends. Some or all of the above processing in the digitization unit may be performed using AI, for example, or not using AI. For example, the digitization unit can input user social media activity data into a generating AI and have the generating AI perform the digitization of trend-reflecting data.

[0055] The digitization unit can prioritize digitizing the most relevant data, taking into account the user's health condition. For example, if the user is tired, the digitization unit can prioritize digitizing important data. If the user is healthy, the digitization unit can also prioritize digitizing detailed data. If the user is unwell, the digitization unit can also prioritize digitizing concise data. This allows for the provision of appropriate data by prioritizing the digitization of data according to the user's health condition. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the user's health condition data into a generating AI and have the generating AI perform priority digitization of data based on the health condition.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The suggestion unit can analyze data on the user's past opponents and suggest effective decks against specific opponents. For example, the suggestion unit can analyze the deck compositions of opponents the user has previously played against and suggest decks containing cards effective against those opponents. If the user is struggling against a particular opponent, the suggestion unit can also suggest decks to break through that opponent's strategy. The suggestion unit can also refer to decks of opponents the user has previously defeated and suggest decks employing similar strategies. This allows the user to improve their win rate by suggesting effective decks against specific opponents. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's past opponents into a generating AI and have the generating AI suggest effective decks against specific opponents.

[0058] The suggestion unit can adjust the difficulty of decks based on the user's current in-game rank and score. For example, if the user is a beginner, the suggestion unit may suggest a deck containing many basic cards. If the user is an intermediate player, the suggestion unit may also suggest a deck containing many strategic cards. If the user is an advanced player, the suggestion unit may also suggest a deck containing complex combos. In this way, by suggesting decks according to the user's rank and score, the system can provide decks of appropriate difficulty. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's in-game rank and score into a generating AI and have the generating AI perform the adjustment of deck difficulty.

[0059] The suggestion unit can propose decks that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit can propose a deck containing many ice-attribute cards. If the user lives in a tropical region, the suggestion unit can also propose a deck containing many fire-attribute cards. If the user lives in an urban area, the suggestion unit can also propose a deck containing many machine-attribute cards. In this way, by proposing decks that reflect region-specific strategies, a deck suitable for the user can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute a deck proposal that reflects region-specific strategies.

[0060] The suggestion unit can analyze a user's social media activity and suggest decks that reflect relevant trends. For example, the suggestion unit can suggest decks that include many cards the user is talking about on social media. The suggestion unit can also suggest decks that are based on decks used by influencers the user follows. The suggestion unit can also suggest decks that include many cards popular in communities the user participates in. In this way, by suggesting decks that reflect social media trends, the suggestion unit can provide users with the latest decks. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI suggest decks that reflect trends.

[0061] The battle unit can refer to the user's past battle history to strengthen its countermeasures against specific strategies. For example, the battle unit can set up an AI with strengthened countermeasures against strategies that the user has struggled against in the past. The battle unit can also set up an AI that employs similar strategies against strategies that the user has won against in the past. The battle unit can also analyze strategies that the user has used in the past and set up an AI with strengthened countermeasures against those strategies. This allows the system to strengthen its countermeasures against specific strategies by referring to past battle history. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's past battle history data into a generating AI and have the generating AI perform strengthening of countermeasures against specific strategies.

[0062] The battle unit can dynamically change the AI's strategy according to the user's play style. For example, if the user adopts an aggressive play style, the AI ​​will adopt a defense-oriented strategy. If the user adopts a defensive play style, the AI ​​can also adopt an offensive strategy. If the user adopts a balanced play style, the AI ​​can flexibly change its strategy. This allows for more effective battles by providing strategies tailored to the user's play style. Some or all of the above processing in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's play style data into a generating AI and cause the generating AI to dynamically change its strategy.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The suggestion department proposes the optimal card deck based on the user's personality, preferred style, past deck information, and battle history. For example, the suggestion department uses AI to analyze the decks the user has used in the past and their battle results, and proposes a deck that suits the user's play style. For users who prefer an aggressive play style, it proposes a deck with many high-attack cards; for users who prefer a defensive play style, it proposes a deck with many high-defense cards; and for users who prefer a balanced play style, it proposes a deck with a good balance of offense and defense. Step 2: The battle team conducts virtual battles against the AI ​​using decks proposed by the proposal team. For example, by playing against the AI, users can practice in an environment similar to actual battles. The battle team allows the AI ​​to manipulate the opponent using different strategies, and the user can learn how to respond to those strategies. If the AI ​​adopts an offensive strategy, the user can try a defensive strategy, and if the AI ​​adopts a defensive strategy, the user can try an offensive strategy. Furthermore, if the AI ​​adopts a balanced strategy, the user can try a variety of strategies. Step 3: The analysis unit visually analyzes the results of the matches played by the battle unit. For example, it includes a function that allows the AI ​​to control the opponent on a virtual battlefield and visually analyze their strategy. Users can visually observe the movements of the AI-controlled opponent and understand their strategy. By visually showing the timing and order in which the AI ​​uses specific cards, users can learn the strategy. It can also visually show the effects of the strategies the AI ​​employed during the match. For example, the effects of the cards used by the AI ​​can be displayed using graphs or heatmaps. Step 4: The Improvement Department proposes improvements to the play style based on the results obtained by the Analysis Department. For example, the AI ​​analyzes the mistakes and areas for improvement made by the user during a match and provides specific advice. By suggesting the timing of using specific cards or changes to strategies, the AI ​​can improve the user's play style. It can suggest improvements to the timing of using specific cards, changes to specific strategies, or suggestions to try new strategies. Step 5: The digitalization unit digitizes the usage information of the physical deck. For example, sensors collect information about the physical deck actually used by the user, and AI analyzes that data. Users can check the deck's performance in real time and adjust it as needed. The types of cards used by the user and their frequency of use are digitized, and the AI ​​evaluates the deck's performance based on that data.

[0065] (Example of form 2) The card game support system according to an embodiment of the present invention is a system that uses AI to support users who enjoy card games in building optimal decks and practicing matches. Based on the user's personality, preferred style, past deck information, and battle history, the AI ​​suggests the optimal card deck. Next, it is equipped with a virtual battle function with the AI, allowing users to try out various strategies. Furthermore, it is equipped with a function that allows the AI ​​to control the opponent on the virtual battlefield and visually analyze their strategy. It also provides a service in which the AI ​​analyzes the user's play style and suggests improvements to overcome weaknesses. In addition, IoT functionality for digitizing the usage information of physical decks enables the AI ​​to analyze play data in real time. As a result, the card game support system provides an environment in which people who enjoy card games as a hobby can enjoy them more. For example, users can build decks that suit their personality and play style and learn various strategies through matches with the AI. Furthermore, they can improve their play style through improvement suggestions provided by the AI. In addition, by digitizing the usage information of physical decks, it is possible to check the deck's performance in real time and build the optimal deck.

[0066] The card game support system according to this embodiment comprises a suggestion unit, a battle unit, an analysis unit, an improvement unit, and a digitization unit. The suggestion unit suggests an optimal card deck based on the user's personality, preferred style, past deck information, and battle history. For example, the suggestion unit uses AI to analyze decks and battle results previously used by the user and suggests a deck that suits the user's play style. For example, the suggestion unit suggests a deck containing many cards with high attack power for a user who prefers an aggressive play style. The suggestion unit can also suggest a deck containing many cards with high defensive power for a user who prefers a defensive play style. Furthermore, the suggestion unit can suggest a deck with a balance of attack and defense for a user who prefers a balanced play style. The battle unit conducts a virtual battle against the AI ​​using the deck suggested by the suggestion unit. For example, the battle unit allows the user to practice in an environment similar to an actual battle by playing against the AI. The battle unit allows the AI ​​to manipulate the opponent using different strategies, and the user can learn how to respond to those strategies. For example, the battle unit allows the user to try a defensive strategy when the AI ​​employs an aggressive strategy. Furthermore, the battle section allows users to try offensive strategies when the AI ​​employs a defensive strategy. Additionally, when the battle section employs a balanced strategy, users can try various strategies. The analysis section visually analyzes the results of battles conducted by the battle section. For example, the analysis section includes a function that allows the AI ​​to control an opponent on a virtual battlefield and visually analyze their strategy. The analysis section allows users to visually observe the movements of the AI-controlled opponent and understand their strategy. For instance, the analysis section visually indicates the timing and order in which the AI ​​uses specific cards, allowing users to learn the strategy. The analysis section can also visually demonstrate the effects of strategies employed by the AI ​​during a battle. For example, the analysis section can display the effects of cards used by the AI ​​using graphs or heatmaps. The improvement section suggests improvements to play style based on the results obtained by the analysis section. For example, the improvement section uses the AI ​​to analyze mistakes and areas for improvement made by the user during a battle and provides specific advice.The improvement unit can enhance the user's play style by suggesting changes to the timing of using specific cards or to strategies. For example, the improvement unit can suggest that the user improve the timing of using specific cards. It can also suggest that the user change a specific strategy. Furthermore, it can suggest that the user try a new strategy. The digitization unit digitizes the usage information of the physical deck. For example, the digitization unit collects information about the physical deck actually used by the user using sensors, and the AI ​​analyzes that data. The digitization unit allows the user to check the deck's performance in real time and adjust the deck as needed. For example, the digitization unit digitizes the types and frequency of cards used by the user, and the AI ​​evaluates the deck's performance based on that data. As a result, the card game support system according to the embodiment can suggest an optimal deck based on the user's personality and play style, learn strategies through playing against the AI, and improve their play style.

[0067] The suggestion department proposes the optimal card deck based on the user's personality, preferred style, past deck information, and battle history. For example, the suggestion department uses AI to analyze the decks and battle results the user has used in the past and proposes a deck that suits the user's play style. Specifically, the AI ​​analyzes the user's past battle data in detail, analyzing win rates, frequently used cards, and reactions to opponents' strategies. This allows the AI ​​to understand what kind of cards the user likes and what kind of strategies they excel at. For example, for a user who prefers an aggressive play style, the suggestion department will propose a deck containing many high-attack cards. In this case, the AI ​​will select combinations of high-attack cards and cards suitable for aggressive strategies. The suggestion department can also propose a deck containing many high-defense cards for a user who prefers a defensive play style. In this case, the AI ​​will propose combinations of high-defense cards and cards that effectively defend against the opponent's attacks. Furthermore, for a user who prefers a balanced play style, the suggestion department can propose a deck with a balance of offense and defense. The AI ​​will consider the balance of offense and defense and construct a deck that allows the user to respond to various situations. The suggestion department can collect user feedback when making these suggestions and continuously improve the suggestions. For example, the AI ​​can analyze the results of users using the suggested decks and use that information to make future suggestions more accurate. The suggestion department can also suggest the optimal deck configuration when users add new cards or modify existing ones. In this way, the suggestion department can suggest the best deck tailored to the user's play style and preferences, improving the user's gaming experience.

[0068] The Battle Section conducts virtual matches against AI using decks proposed by the Proposal Section. For example, the Battle Section allows users to practice in an environment similar to actual matches by playing against the AI. Specifically, the Battle Section uses advanced AI algorithms to generate virtual opponents with various strategies. The AI ​​employs different strategies, such as offensive, defensive, and balanced, to provide users with diverse match scenarios. For example, if the AI ​​adopts an offensive strategy, the user can try a defensive strategy. In this case, the AI ​​actively uses high-attack cards to put pressure on the user. Similarly, if the AI ​​adopts a defensive strategy, the user can try an offensive strategy. In this case, the AI ​​uses high-defense cards to effectively block the user's attacks. Furthermore, if the AI ​​adopts a balanced strategy, the user can try various strategies. The AI ​​balances offense and defense while requiring diverse responses from the user. Through these match scenarios, the Battle Section helps users develop their ability to adapt to different strategies. Furthermore, the battle department will record battle results in real time, allowing users to review the effectiveness of their play style and strategies. This enables the battle department to practice in an environment similar to actual battles and refine their strategies. The battle department will also record the choices and actions taken by users during battles, allowing the analysis and improvement departments to utilize this data later. This allows the battle department to support user skill improvement and enhance the gaming experience.

[0069] The analysis unit visually analyzes the results of matches played by the battle unit. For example, the analysis unit includes a function that allows the AI ​​to control an opponent on a virtual battlefield and visually analyze its strategy. Specifically, the analysis unit analyzes each phase of the match in detail, visually showing when the user used which cards and how the AI ​​reacted. For instance, by visually showing the timing and order in which the AI ​​uses specific cards, the analysis unit allows users to learn the strategy. This enables users to understand the AI's strategy and incorporate it into their own play style. The analysis unit can also visually show the effects of strategies employed by the AI ​​during a match. For example, the analysis unit can display the effects of cards used by the AI ​​using graphs and heatmaps. This allows users to intuitively understand how specific cards and strategies influenced the match outcome. Furthermore, the analysis unit can statistically analyze the match results to clearly identify the user's strengths and weaknesses. For example, by showing the success rate of a user using specific cards or the win rate against specific strategies, users can objectively evaluate their own play style. The analytics department provides users with these visual analysis results, enabling them to obtain specific guidance for improving their play style. This allows the analytics department to enable users to analyze match results in detail, learn strategies, and improve their play style.

[0070] The Improvement Department proposes improvements to playstyle based on the results obtained by the Analysis Department. For example, the Improvement Department uses AI to analyze mistakes and areas for improvement made by users during matches and provides specific advice. Specifically, the Improvement Department analyzes the user's match data in detail, showing when and which cards should have been used and which strategies were more effective. For example, the Improvement Department may suggest that the user improve the timing of using a particular card. Based on past match data, the AI ​​analyzes the optimal timing for using a particular card and provides the results to the user. The Improvement Department can also suggest that the user change a particular strategy. For example, if the AI ​​takes an offensive strategy when a defensive strategy should have been taken, it will show how that choice affected the match outcome and suggest areas for improvement in the next match. Furthermore, the Improvement Department can also suggest that the user try a new strategy. Based on the user's playstyle and preferences, the AI ​​suggests new strategies and card combinations, providing the user with an opportunity to try new strategies. Through these suggestions, the Improvement Department enables users to continuously improve their playstyle and enhance their match skills. The Improvement Department can also collect user feedback and continuously improve the suggestions. This allows the improvement team to provide users with specific advice to help them improve their play style, thereby enhancing the gaming experience.

[0071] The digitalization unit digitizes the usage information of physical decks. For example, the digitalization unit collects information about the physical decks actually used by users using sensors, and AI analyzes that data. Specifically, the digitalization unit uses sensors that read RFID tags embedded in cards or barcodes to collect the types of cards used and their frequency of use in real time. This allows for detailed recording of which cards the user used, when, and which cards were most effective. For example, the digitalization unit digitizes the types and frequency of cards used by users, and the AI ​​evaluates the deck's performance based on that data. The AI ​​can analyze the collected data and clearly identify the strengths and weaknesses of the user's deck. Furthermore, the digitalization unit allows users to check the deck's performance in real time and adjust the deck as needed. For example, when a user checks the effect of a specific card during a match or re-evaluates the deck's balance, the digitalization unit can provide that information immediately. In addition, the digitalization unit enables users to seamlessly link physical and digital decks. This allows users to enjoy the convenience of digital decks while still using physical decks. Through these functions, the digitalization section allows users to effectively utilize both physical and digital decks, thereby enhancing their gaming experience.

[0072] The suggestion unit can estimate the user's emotions and adjust the type of deck suggested based on the estimated emotions. For example, if the user is stressed, the suggestion unit may suggest a defensive deck that promotes relaxation. If the user is excited, the suggestion unit may suggest a deck with many aggressive cards. If the user is calm, the suggestion unit may suggest a balanced deck. This allows for the provision of more appropriate decks by suggesting decks that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI suggest decks based on emotions.

[0073] The suggestion unit can analyze data on the user's past opponents and suggest effective decks against specific opponents. For example, the suggestion unit can analyze the deck compositions of opponents the user has previously played against and suggest decks containing cards effective against those opponents. If the user is struggling against a particular opponent, the suggestion unit can also suggest decks to break through that opponent's strategy. The suggestion unit can also refer to decks of opponents the user has previously defeated and suggest decks employing similar strategies. This allows the user to improve their win rate by suggesting effective decks against specific opponents. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's past opponents into a generating AI and have the generating AI suggest effective decks against specific opponents.

[0074] The suggestion unit can adjust the difficulty of decks based on the user's current in-game rank and score. For example, if the user is a beginner, the suggestion unit may suggest a deck containing many basic cards. If the user is an intermediate player, the suggestion unit may also suggest a deck containing many strategic cards. If the user is an advanced player, the suggestion unit may also suggest a deck containing complex combos. In this way, by suggesting decks according to the user's rank and score, the system can provide decks of appropriate difficulty. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's in-game rank and score into a generating AI and have the generating AI perform the adjustment of deck difficulty.

[0075] The suggestion unit can estimate the user's emotions and adjust the order of cards in the suggested deck based on the estimated emotions. For example, if the user is nervous, the suggestion unit can suggest a deck with many defensive cards in the early stages. If the user is relaxed, the suggestion unit can also suggest a deck with many offensive cards in the early stages. If the user is excited, the suggestion unit can also suggest a deck with powerful cards in the early stages. This allows for the provision of a more effective deck by suggesting a card order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the card order based on the emotions.

[0076] The suggestion unit can propose decks that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit can propose a deck containing many ice-attribute cards. If the user lives in a tropical region, the suggestion unit can also propose a deck containing many fire-attribute cards. If the user lives in an urban area, the suggestion unit can also propose a deck containing many machine-attribute cards. In this way, by proposing decks that reflect region-specific strategies, a deck suitable for the user can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute a deck proposal that reflects region-specific strategies.

[0077] The suggestion unit can analyze a user's social media activity and suggest decks that reflect relevant trends. For example, the suggestion unit can suggest decks that include many cards the user is talking about on social media. The suggestion unit can also suggest decks that are based on decks used by influencers the user follows. The suggestion unit can also suggest decks that include many cards popular in communities the user participates in. In this way, by suggesting decks that reflect social media trends, the suggestion unit can provide users with the latest decks. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI suggest decks that reflect trends.

[0078] The battle unit can estimate the user's emotions and adjust the difficulty of the match based on the estimated emotions. For example, if the user is nervous, the battle unit can set the opponent's strength to a low level. If the user is relaxed, the battle unit can also set the opponent's strength to a medium level. If the user is excited, the battle unit can also set the opponent's strength to a high level. This provides an appropriate battle environment by offering matches with difficulty levels that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can input user emotion data into a generative AI and have the generative AI adjust the difficulty of the match based on the emotions.

[0079] The battle unit can refer to the user's past battle history to strengthen its countermeasures against specific strategies. For example, the battle unit can set up an AI with strengthened countermeasures against strategies that the user has struggled against in the past. The battle unit can also set up an AI that employs similar strategies against strategies that the user has won against in the past. The battle unit can also analyze strategies that the user has used in the past and set up an AI with strengthened countermeasures against those strategies. This allows the system to strengthen its countermeasures against specific strategies by referring to past battle history. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's past battle history data into a generating AI and have the generating AI perform strengthening of countermeasures against specific strategies.

[0080] The battle unit can dynamically change the AI's strategy according to the user's play style. For example, if the user adopts an aggressive play style, the AI ​​will adopt a defense-oriented strategy. If the user adopts a defensive play style, the AI ​​can also adopt an offensive strategy. If the user adopts a balanced play style, the AI ​​can flexibly change its strategy. This allows for more effective battles by providing strategies tailored to the user's play style. Some or all of the above processing in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's play style data into a generating AI and cause the generating AI to dynamically change its strategy.

[0081] The battle unit can estimate the user's emotions and adjust the tempo of the battle based on the estimated emotions. For example, if the user is nervous, the battle unit can slow down the battle tempo. If the user is relaxed, the battle unit can return to a normal tempo. If the user is excited, the battle unit can speed up the battle tempo. This allows for the provision of an appropriate battle environment by offering battles with tempos that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can input user emotion data into a generative AI and have the generative AI adjust the battle tempo based on the emotions.

[0082] The battle unit can conduct battles that reflect region-specific strategies by taking into account the user's geographical location information. For example, if the user lives in a cold region, the battle unit can set up an AI that uses many ice-attribute cards. If the user lives in a tropical region, the battle unit can also set up an AI that uses many fire-attribute cards. If the user lives in an urban area, the battle unit can also set up an AI that uses many machine-attribute cards. In this way, by providing battles that reflect region-specific strategies, a battle environment suitable for the user can be provided. Some or all of the above processing in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's geographical location information into a generating AI and have the generating AI execute a battle setting that reflects region-specific strategies.

[0083] The battle unit can analyze users' social media activity and conduct battles that reflect relevant trends. For example, the battle unit can set up an AI that uses many cards that users are talking about on social media. The battle unit can also set up an AI that references decks used by influencers that users follow. The battle unit can also set up an AI that uses many cards that are popular in the communities that users participate in. This allows the battle unit to provide users with the latest battle environment by offering battles that reflect social media trends. Some or all of the above processing in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can input user social media activity data into a generating AI and have the generating AI execute a battle setting that reflects trends.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By providing a display method that matches the user's emotions, appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results based on the emotions.

[0085] The analysis unit can analyze the effectiveness of a particular strategy in detail by referring to the user's past battle data. For example, the analysis unit can analyze the win rate of strategies the user has used in the past and evaluate their effectiveness. The analysis unit can also analyze the strategies of opponents the user has played against in the past and evaluate their effectiveness. The analysis unit can also analyze the combinations of cards the user has used in the past and evaluate their effectiveness. In this way, the effectiveness of a particular strategy can be analyzed in detail by referring to past battle data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past battle data into a generating AI and have the generating AI perform a detailed analysis of the effectiveness of a particular strategy.

[0086] The analysis unit can apply different analysis algorithms depending on the user's play style. For example, if the user has an aggressive play style, the analysis unit can apply an attack-oriented analysis algorithm. If the user has a defensive play style, the analysis unit can also apply a defense-oriented analysis algorithm. If the user has a balanced play style, the analysis unit can also apply a balance-oriented analysis algorithm. This allows for more effective analysis results by providing analysis tailored to the user's play style. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user play style data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize displaying important information. If the user is relaxed, the analysis unit may also prioritize displaying detailed information. If the user is in a hurry, the analysis unit may also prioritize displaying concise information. This allows the system to provide appropriate information by prioritizing analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results based on emotions.

[0088] The analysis unit can perform analyses that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the analysis unit can analyze the effects of ice-attribute cards. If the user lives in a tropical region, the analysis unit can also analyze the effects of fire-attribute cards. If the user lives in an urban area, the analysis unit can also analyze the effects of machine-attribute cards. This allows the analysis unit to provide users with analysis results that are appropriate to their needs by providing analyses that reflect region-specific strategies. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform an analysis that reflects region-specific strategies.

[0089] The analytics department can analyze users' social media activity and perform analyses that reflect relevant trends. For example, the analytics department can analyze the effectiveness of cards that users are talking about on social media. The analytics department can also analyze the effectiveness of cards used by influencers that users follow. The analytics department can also analyze the effectiveness of cards that are popular in communities that users participate in. This allows the analytics department to provide users with up-to-date analysis results by providing analyses that reflect social media trends. Some or all of the above processes in the analytics department may be performed using AI, for example, or not using AI. For example, the analytics department can input user social media activity data into a generating AI and have the generating AI perform an analysis that reflects trends.

[0090] The improvement unit can estimate the user's emotions and adjust the content of improvement suggestions based on the estimated emotions. For example, if the user is tense, the improvement unit will provide simple and easy-to-implement improvement suggestions. If the user is relaxed, the improvement unit can also provide detailed improvement suggestions. If the user is excited, the improvement unit can also provide proactive improvement suggestions. This allows the improvement unit to provide appropriate solutions by offering improvement suggestions that are tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input user emotion data into a generative AI and have the generative AI adjust the content of improvement suggestions based on the emotions.

[0091] The improvement unit can refer to the user's past battle data and propose specific improvement measures for specific weaknesses. For example, the improvement unit can propose improvement measures for strategies that the user has struggled with in the past. The improvement unit can also analyze mistakes the user has made in the past and propose improvement measures. The improvement unit can also analyze the effects of cards the user has used in the past and propose improvement measures. In this way, by referring to past battle data, it is possible to provide specific improvement measures for specific weaknesses. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's past battle data into a generating AI and have the generating AI execute the task of proposing specific improvement measures for specific weaknesses.

[0092] The improvement unit can apply different improvement algorithms depending on the user's play style. For example, if the user has an aggressive play style, the improvement unit will propose an attack-oriented improvement. If the user has a defensive play style, the improvement unit can also propose a defense-oriented improvement. If the user has a balanced play style, the improvement unit can also propose a balance-oriented improvement. By providing improvement measures tailored to the user's play style, more effective improvements can be achieved. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user play style data into a generating AI and have the generating AI execute the application of different improvement algorithms.

[0093] The improvement unit can estimate the user's emotions and prioritize improvement suggestions based on those emotions. For example, if the user is stressed, the improvement unit will prioritize suggesting important improvements. If the user is relaxed, the improvement unit may also prioritize suggesting detailed improvements. If the user is in a hurry, the improvement unit may also prioritize suggesting concise improvements. This allows the improvement unit to provide appropriate improvements by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI or not. For example, the improvement unit can input user emotion data into a generative AI and have the generative AI determine the priority of improvement suggestions based on emotions.

[0094] The improvement unit can propose improvement measures that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the improvement unit can propose improvement measures that enhance the effect of ice-attribute cards. If the user lives in a tropical region, the improvement unit can also propose improvement measures that enhance the effect of fire-attribute cards. If the user lives in an urban area, the improvement unit can also propose improvement measures that enhance the effect of machine-attribute cards. In this way, by providing improvement measures that reflect region-specific strategies, the improvement unit can provide improvement measures that are suitable for the user. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without using AI. For example, the improvement unit can input the user's geographical location information into a generating AI and have the generating AI execute suggestions for improvement measures that reflect region-specific strategies.

[0095] The improvement unit can analyze users' social media activity and propose improvements that reflect relevant trends. For example, the improvement unit can propose improvements that enhance the effectiveness of cards that users are talking about on social media. The improvement unit can also propose improvements that enhance the effectiveness of cards used by influencers that users follow. The improvement unit can also propose improvements that enhance the effectiveness of cards that are popular in communities that users participate in. This allows the improvement unit to provide users with the latest improvements by offering improvements that reflect social media trends. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input user social media activity data into a generating AI and have the generating AI propose improvements that reflect trends.

[0096] The digitization unit can estimate the user's emotions and adjust the type of data to be digitized based on the estimated emotions. For example, if the user is stressed, the digitization unit can prioritize digitizing important data. If the user is relaxed, the digitization unit can also prioritize digitizing detailed data. If the user is in a hurry, the digitization unit can also prioritize digitizing concise data. This allows for the provision of appropriate data by providing digitized data that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the digitization unit may be performed using AI or not. For example, the digitization unit can input user emotion data into a generative AI and have the generative AI adjust the digitization of data based on emotions.

[0097] The digitization unit can refer to the user's past deck usage data and record in detail the frequency of use of specific cards. For example, the digitization unit can record the frequency of use of cards that the user has used frequently in the past. The digitization unit can also record the effects of cards that the user has used in the past. The digitization unit can also record combinations of cards that the user has used in the past. This allows for detailed recording of the frequency of use of specific cards by referring to past deck usage data. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the user's past deck usage data into a generating AI and have the generating AI perform detailed recording of the frequency of use of specific cards.

[0098] The digitization unit can apply different digitization algorithms depending on the user's play style. For example, if the user adopts an aggressive play style, the digitization unit can apply an attack-oriented digitization algorithm. If the user adopts a defensive play style, the digitization unit can also apply a defense-oriented digitization algorithm. If the user adopts a balanced play style, the digitization unit can also apply a balance-oriented digitization algorithm. This allows for the provision of more effective data by offering digitization tailored to the user's play style. Some or all of the above-described processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input user play style data into a generating AI and have the generating AI execute the application of different digitization algorithms.

[0099] The digitization unit can estimate the user's emotions and determine the priority of data to digitize based on the estimated user emotions. For example, if the user is stressed, the digitization unit can prioritize digitizing important data. If the user is relaxed, the digitization unit can also prioritize digitizing detailed data. If the user is in a hurry, the digitization unit can also prioritize digitizing concise data. This allows for the provision of appropriate data by digitizing data with priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the digitization unit may be performed using AI or not. For example, the digitization unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priorities based on emotions.

[0100] The digitization unit can prioritize the digitization of region-specific data, taking into account the user's geographical location information. For example, if the user lives in a cold region, the digitization unit can prioritize the digitization of ice-attribute card data. If the user lives in a tropical region, the digitization unit can also prioritize the digitization of fire-attribute card data. If the user lives in an urban area, the digitization unit can also prioritize the digitization of machine-attribute card data. This allows for the provision of data tailored to the user by prioritizing the digitization of region-specific data. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the user's geographical location information into a generating AI and have the generating AI perform the preferential digitization of region-specific data.

[0101] The digitization unit can analyze users' social media activity and digitize data that reflects relevant trends. For example, the digitization unit can digitize data on cards that users are talking about on social media. The digitization unit can also digitize data on cards used by influencers that users follow. The digitization unit can also digitize data on cards that are popular in communities that users participate in. This allows the digitization unit to provide users with the latest data by digitizing data that reflects social media trends. Some or all of the above processing in the digitization unit may be performed using AI, for example, or not using AI. For example, the digitization unit can input user social media activity data into a generating AI and have the generating AI perform the digitization of trend-reflecting data.

[0102] The digitization unit can prioritize digitizing the most relevant data, taking into account the user's health condition. For example, if the user is tired, the digitization unit can prioritize digitizing important data. If the user is healthy, the digitization unit can also prioritize digitizing detailed data. If the user is unwell, the digitization unit can also prioritize digitizing concise data. This allows for the provision of appropriate data by prioritizing the digitization of data according to the user's health condition. Some or all of the above processing in the digitization unit may be performed using AI, for example, or without AI. For example, the digitization unit can input the user's health condition data into a generating AI and have the generating AI perform priority digitization of data based on the health condition.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The suggestion unit can estimate the user's emotions and adjust the type of deck suggested based on the estimated emotions. For example, if the user is stressed, the suggestion unit may suggest a defensive deck that promotes relaxation. If the user is excited, the suggestion unit may suggest a deck with many aggressive cards. If the user is calm, the suggestion unit may suggest a balanced deck. This allows for the provision of more appropriate decks by suggesting decks that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI suggest decks based on emotions.

[0105] The suggestion unit can analyze data on the user's past opponents and suggest effective decks against specific opponents. For example, the suggestion unit can analyze the deck compositions of opponents the user has previously played against and suggest decks containing cards effective against those opponents. If the user is struggling against a particular opponent, the suggestion unit can also suggest decks to break through that opponent's strategy. The suggestion unit can also refer to decks of opponents the user has previously defeated and suggest decks employing similar strategies. This allows the user to improve their win rate by suggesting effective decks against specific opponents. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's past opponents into a generating AI and have the generating AI suggest effective decks against specific opponents.

[0106] The suggestion unit can adjust the difficulty of decks based on the user's current in-game rank and score. For example, if the user is a beginner, the suggestion unit may suggest a deck containing many basic cards. If the user is an intermediate player, the suggestion unit may also suggest a deck containing many strategic cards. If the user is an advanced player, the suggestion unit may also suggest a deck containing complex combos. In this way, by suggesting decks according to the user's rank and score, the system can provide decks of appropriate difficulty. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's in-game rank and score into a generating AI and have the generating AI perform the adjustment of deck difficulty.

[0107] The suggestion unit can estimate the user's emotions and adjust the order of cards in the suggested deck based on the estimated emotions. For example, if the user is nervous, the suggestion unit can suggest a deck with many defensive cards in the early stages. If the user is relaxed, the suggestion unit can also suggest a deck with many offensive cards in the early stages. If the user is excited, the suggestion unit can also suggest a deck with powerful cards in the early stages. This allows for the provision of a more effective deck by suggesting a card order that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's emotion data into a generative AI and have the generative AI perform the adjustment of the card order based on the emotions.

[0108] The suggestion unit can propose decks that reflect region-specific strategies, taking into account the user's geographical location information. For example, if the user lives in a cold region, the suggestion unit can propose a deck containing many ice-attribute cards. If the user lives in a tropical region, the suggestion unit can also propose a deck containing many fire-attribute cards. If the user lives in an urban area, the suggestion unit can also propose a deck containing many machine-attribute cards. In this way, by proposing decks that reflect region-specific strategies, a deck suitable for the user can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into a generating AI and have the generating AI execute a deck proposal that reflects region-specific strategies.

[0109] The suggestion unit can analyze a user's social media activity and suggest decks that reflect relevant trends. For example, the suggestion unit can suggest decks that include many cards the user is talking about on social media. The suggestion unit can also suggest decks that are based on decks used by influencers the user follows. The suggestion unit can also suggest decks that include many cards popular in communities the user participates in. In this way, by suggesting decks that reflect social media trends, the suggestion unit can provide users with the latest decks. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's social media activity data into a generating AI and have the generating AI suggest decks that reflect trends.

[0110] The battle unit can estimate the user's emotions and adjust the difficulty of the match based on the estimated emotions. For example, if the user is nervous, the battle unit can set the opponent's strength to a low level. If the user is relaxed, the battle unit can also set the opponent's strength to a medium level. If the user is excited, the battle unit can also set the opponent's strength to a high level. This provides an appropriate battle environment by offering matches with difficulty levels that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can input user emotion data into a generative AI and have the generative AI adjust the difficulty of the match based on the emotions.

[0111] The battle unit can refer to the user's past battle history to strengthen its countermeasures against specific strategies. For example, the battle unit can set up an AI with strengthened countermeasures against strategies that the user has struggled against in the past. The battle unit can also set up an AI that employs similar strategies against strategies that the user has won against in the past. The battle unit can also analyze strategies that the user has used in the past and set up an AI with strengthened countermeasures against those strategies. This allows the system to strengthen its countermeasures against specific strategies by referring to past battle history. Some or all of the above processes in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's past battle history data into a generating AI and have the generating AI perform strengthening of countermeasures against specific strategies.

[0112] The battle unit can dynamically change the AI's strategy according to the user's play style. For example, if the user adopts an aggressive play style, the AI ​​will adopt a defense-oriented strategy. If the user adopts a defensive play style, the AI ​​can also adopt an offensive strategy. If the user adopts a balanced play style, the AI ​​can flexibly change its strategy. This allows for more effective battles by providing strategies tailored to the user's play style. Some or all of the above processing in the battle unit may be performed using AI, for example, or without AI. For example, the battle unit can input the user's play style data into a generating AI and cause the generating AI to dynamically change its strategy.

[0113] The battle unit can estimate the user's emotions and adjust the tempo of the battle based on the estimated emotions. For example, if the user is nervous, the battle unit can slow down the battle tempo. If the user is relaxed, the battle unit can return to a normal tempo. If the user is excited, the battle unit can speed up the battle tempo. This allows for the provision of an appropriate battle environment by offering battles with tempos that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the battle unit may be performed using AI, for example, or not using AI. For example, the battle unit can input user emotion data into a generative AI and have the generative AI adjust the battle tempo based on the emotions.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The suggestion department proposes the optimal card deck based on the user's personality, preferred style, past deck information, and battle history. For example, the suggestion department uses AI to analyze the decks the user has used in the past and their battle results, and proposes a deck that suits the user's play style. For users who prefer an aggressive play style, it proposes a deck with many high-attack cards; for users who prefer a defensive play style, it proposes a deck with many high-defense cards; and for users who prefer a balanced play style, it proposes a deck with a good balance of offense and defense. Step 2: The battle team conducts virtual battles against the AI ​​using decks proposed by the proposal team. For example, by playing against the AI, users can practice in an environment similar to actual battles. The battle team allows the AI ​​to manipulate the opponent using different strategies, and the user can learn how to respond to those strategies. If the AI ​​adopts an offensive strategy, the user can try a defensive strategy, and if the AI ​​adopts a defensive strategy, the user can try an offensive strategy. Furthermore, if the AI ​​adopts a balanced strategy, the user can try a variety of strategies. Step 3: The analysis unit visually analyzes the results of the matches played by the battle unit. For example, it includes a function that allows the AI ​​to control the opponent on a virtual battlefield and visually analyze their strategy. Users can visually observe the movements of the AI-controlled opponent and understand their strategy. By visually showing the timing and order in which the AI ​​uses specific cards, users can learn the strategy. It can also visually show the effects of the strategies the AI ​​employed during the match. For example, the effects of the cards used by the AI ​​can be displayed using graphs or heatmaps. Step 4: The Improvement Department proposes improvements to the play style based on the results obtained by the Analysis Department. For example, the AI ​​analyzes the mistakes and areas for improvement made by the user during a match and provides specific advice. By suggesting the timing of using specific cards or changes to strategies, the AI ​​can improve the user's play style. It can suggest improvements to the timing of using specific cards, changes to specific strategies, or suggestions to try new strategies. Step 5: The digitalization unit digitizes the usage information of the physical deck. For example, sensors collect information about the physical deck actually used by the user, and AI analyzes that data. Users can check the deck's performance in real time and adjust it as needed. The types of cards used by the user and their frequency of use are digitized, and the AI ​​evaluates the deck's performance based on that data.

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0117] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0118] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the proposal unit, battle unit, analysis unit, improvement unit, and digitalization unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14 and proposes an optimal deck based on the user's personality and play style. The battle unit is implemented by the specific processing unit 290 of the data processing unit 12 and conducts a virtual battle against an AI. The analysis unit is implemented by the control unit 46A of the smart device 14 and visually analyzes the battle results. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes improvements to the play style. The digitalization unit is implemented by the control unit 46A of the smart device 14 and digitizes the usage information of the physical deck. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the suggestion unit, battle unit, analysis unit, improvement unit, and digitalization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests an optimal deck based on the user's personality and play style. The battle unit is implemented by the specific processing unit 290 of the data processing unit 12 and conducts a virtual battle against an AI. The analysis unit is implemented by the control unit 46A of the smart glasses 214 and visually analyzes the battle results. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and suggests improvements to the play style. The digitalization unit is implemented by the control unit 46A of the smart glasses 214 and digitizes the usage information of the physical deck. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the suggestion unit, battle unit, analysis unit, improvement unit, and digitalization unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests an optimal deck based on the user's personality and play style. The battle unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts a virtual battle against an AI. The analysis unit is implemented by, for example, the control unit 46A of the headset terminal 314 and visually analyzes the battle results. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and suggests improvements to the play style. The digitalization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and digitizes the usage information of the physical deck. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0160] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements described above, including the proposal unit, battle unit, analysis unit, improvement unit, and digitalization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the proposal unit is implemented by the control unit 46A of the robot 414 and proposes an optimal deck based on the user's personality and play style. The battle unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts a virtual battle against an AI. The analysis unit is implemented by, for example, the control unit 46A of the robot 414 and visually analyzes the battle results. The improvement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes improvements to the play style. The digitalization unit is implemented by, for example, the control unit 46A of the robot 414 and digitizes the usage information of the physical deck. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0169] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0170] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0171] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0172] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0173] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0174] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0175] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0176] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0177] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0178] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0179] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0180] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0181] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0182] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0183] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0184] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0185] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0186] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0187] (Note 1) The proposal department suggests the optimal card deck based on the user's personality, preferred style, past deck information, and battle history. The battle unit conducts a virtual battle against an AI using the deck proposed by the aforementioned proposal unit, An analysis unit visually analyzes the results of the matches conducted by the aforementioned match unit, Based on the results obtained by the aforementioned analysis unit, an improvement unit proposes improvements to the playing style, It includes a digitalization unit that digitizes the usage information of the physical deck. A system characterized by the following features. (Note 2) The aforementioned proposal section is, It estimates the user's emotions and adjusts the type of deck suggested based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, The system analyzes data from the user's past opponents and suggests effective decks against specific opponents. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The difficulty of the deck will be adjusted based on the user's current in-game rank and score. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It estimates the user's emotions and adjusts the order of cards in the suggested deck based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose decks that reflect region-specific strategies, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned proposal section is, We analyze users' social media activity and suggest decks that reflect relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned battle section is, The system estimates the user's emotions and adjusts the difficulty of the match based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned battle section is, By referring to the user's past match history, we can strengthen countermeasures against specific strategies. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned battle section is, The AI's strategy is dynamically changed according to the user's play style. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned battle section is, It estimates the user's emotions and adjusts the pace of the match based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned battle section is, The game incorporates region-specific strategies by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned battle section is, Analyze users' social media activity and conduct matches that reflect relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is By referencing the user's past match data, the effectiveness of specific strategies can be analyzed in detail. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Apply different analysis algorithms depending on the user's play style. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Conduct analysis that reflects region-specific strategies by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is Analyze users' social media activity and perform analyses that reflect relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned improvement unit is, The system estimates the user's emotions and adjusts the content of improvement suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned improvement unit is, By referring to the user's past battle data, we propose specific improvement measures for addressing particular weaknesses. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned improvement unit is, Apply different improvement algorithms depending on the user's play style. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned improvement unit is, It estimates user emotions and prioritizes improvement suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned improvement unit is, We propose improvement measures that reflect region-specific strategies, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned improvement unit is, We analyze users' social media activity and propose improvements that reflect relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned digitization unit, It estimates the user's emotions and adjusts the type of data to digitize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned digitization unit, Referencing the user's past deck usage data, the system meticulously records the frequency of use of specific cards. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned digitization unit, Apply different digitization algorithms depending on the user's play style. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned digitization unit, It estimates user emotions and determines the priority of data to digitize based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned digitization unit, Prioritize digitizing region-specific data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned digitization unit, Analyze users' social media activity and digitize data that reflects relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned digitization unit, Prioritize digitizing the most relevant data, taking into account the user's health status. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0188] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The proposal department suggests the optimal card deck based on the user's personality, preferred style, past deck information, and battle history. A battle unit that performs a virtual battle against an AI using the deck proposed by the aforementioned proposal unit, An analysis unit visually analyzes the results of the matches conducted by the aforementioned match unit, Based on the results obtained by the aforementioned analysis unit, an improvement unit proposes improvements to the playing style, It includes a digitalization unit that digitizes the usage information of the physical deck. A system characterized by the following features.

2. The aforementioned proposal section is, It estimates the user's emotions and adjusts the type of deck suggested based on those emotions. The system according to feature 1.

3. The aforementioned proposal section is, The system analyzes data from the user's past opponents and suggests effective decks against specific opponents. The system according to feature 1.

4. The aforementioned proposal section is, The difficulty of the deck will be adjusted based on the user's current in-game rank and score. The system according to feature 1.

5. The aforementioned proposal section is, It estimates the user's emotions and adjusts the order of cards in the suggested deck based on those emotions. The system according to feature 1.

6. The aforementioned proposal section is, We propose decks that reflect region-specific strategies, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned proposal section is, We analyze users' social media activity and suggest decks that reflect relevant trends. The system according to feature 1.

8. The aforementioned battle section is, The system estimates the user's emotions and adjusts the difficulty of the match based on those emotions. The system according to feature 1.

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