System

Abstract

The system provided by the embodiment of the invention aims to recommend cards and card groups which conform to the characters and preferences of users and support battle exercises. The system according to the present embodiment includes a proposal unit, a battle unit, an analysis unit, an improvement unit, and a digitization unit. And the proposal part proposes an optimal card group based on the character, the preference style, the past card group information and the battle history of the user. And the fight part performs virtual fight with the AI by using the card group proposed by the proposal part. The analysis unit visually analyzes the result of the battle performed by the battle unit. The improvement unit proposes a suggestion to improve the game style based on a result obtained by the analysis unit. The digitizing unit digitizes use information of the physical card group.

Description

Technical Field

[0001] The technology disclosed herein relates to a system. Background Technology

[0002] Patent Document 1 discloses a personalized chatbot control method executed by at least one processor, the method comprising: receiving user speech; adding the user speech to a prompt containing instructions related to a chatbot role; encoding the prompt; and inputting the encoded prompt into a language model to generate chatbot speech in response to the user speech.

[0003] Patent document 1: Japanese Patent Application Publication No. 2022-180282. Summary of the Invention

[0004] In existing technologies, due to the difficulty in building suitable card decks based on one's own personality and way of thinking in card games, and the lack of opportunities for practice in battles, it is difficult to practice for multiple battle modes, resulting in the aforementioned problems.

[0005] The purpose of the system involved in this technical solution is to recommend card decks that match the user's personality and preferences, and to support battle practice.

[0006] The system involved in this technical solution includes a proposal department, a battle department, an analysis department, an improvement department, and a digitization department. The proposal department proposes optimal card decks based on the user's personality, preferred playstyle, past deck information, and battle history. The battle department uses the decks proposed by the proposal department to conduct virtual battles against AI. The analysis department performs visual analysis of the battle results conducted by the battle department. The improvement department proposes suggestions for improving the game style based on the results obtained by the analysis department. The digitization department digitizes the usage information of the physical card decks.

[0007] The system involved in this technical solution can recommend card decks that match the user's personality and preferences, and supports battle practice. Attached Figure Description

[0008] Figure 1 This is a conceptual diagram illustrating an example of the configuration of a data processing system according to the first embodiment.

[0009] Figure 2 This is a conceptual diagram illustrating an example of the main functions of the data processing apparatus and smart device according to the first embodiment.

[0010] Figure 3 This is a conceptual diagram illustrating an example of the data processing system configuration in the second embodiment.

[0011] Figure 4This is a conceptual diagram illustrating an example of the main functions of the data processing device and smart glasses according to the second embodiment.

[0012] Figure 5 This is a conceptual diagram illustrating an example of the data processing system configuration in the third embodiment.

[0013] Figure 6 This is a conceptual diagram illustrating an example of the main functions of the data processing device and head-mounted terminal according to the third embodiment.

[0014] Figure 7 This is a conceptual diagram illustrating an example of the data processing system configuration in the fourth embodiment.

[0015] Figure 8 This is a conceptual diagram illustrating an example of the functions of the main parts of the data processing device and robot according to the fourth embodiment.

[0016] Figure 9 It represents an emotion graph that maps multiple emotions.

[0017] Figure 10 It represents an emotion graph that maps multiple emotions.

[0018] Explanation of reference numerals in the attached figures

[0019] Data processing systems 10, 210, 310, and 410

[0020] 12 Data processing device

[0021] 14 Smart devices

[0022] 214 Smart Glasses

[0023] 314 Head-mounted terminal

[0024] 414 Robot. Detailed Implementation

[0025] Hereinafter, an example of an implementation of the system involved in this disclosure will be described with reference to the accompanying drawings.

[0026] First, let's explain the terms used in the following description.

[0027] In the following embodiments, the processor (hereinafter referred to as "processor"), as indicated by the reference numerals, can be a single computing device or a combination of multiple computing devices. Furthermore, a processor can be a single computing device or a combination of multiple computing devices. Examples of computing devices 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), etc.

[0028] In the following embodiments, RAM (Random Access Memory), as indicated in the figures, is a memory for temporary information storage that is used by the processor as working memory.

[0029] In the following embodiments, the memory, as indicated by the reference numerals, is one or more non-volatile storage devices used to store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), disk (e.g., hard disk), or magnetic tape, etc.

[0030] In the following embodiments, the communication I / F (Interface) with reference numerals is an interface including a communication processor and an antenna, etc. The communication I / F is responsible for communication between multiple computers. Examples of communication standards applicable 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).

[0031] In the following implementation, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it can be only A, only B, or a combination of A and B. Furthermore, in this specification, when "and / or" connects more than three items, the same approach as "A and / or B" applies.

[0032] [First Implementation Method]

[0033] Figure 1 An example of the configuration of the data processing system 10 according to the first embodiment is shown.

[0034] like Figure 1As shown, the data processing system 10 includes a data processing device 12 and an intelligent device 14. An example of the data processing device 12 is a server.

[0035] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0036] The smart device 14 includes a computer 36, a receiver 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. In addition, the receiver 38, output device 40, and camera 42 are also connected to the bus 52.

[0037] The receiving device 38 includes a touchscreen 38A and a microphone 38B, etc., for receiving user input. The touchscreen 38A receives user input generated by contact with an indicator (e.g., a pen or finger). The microphone 38B receives user input generated by sound by detecting the user's voice. The control unit 46A sends data representing user input received via the touchscreen 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, a specific processing unit 290 (see reference...) Figure 2 Get the data that represents the user input.

[0038] The output device 40 includes a display 40A and a speaker 40B, etc., and presents data to the user by outputting data in a user-perceptible form (e.g., sound and / or text). The display 40A displays visual information such as text and images according to instructions from the processor 46. The speaker 40B outputs sound 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 imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0039] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for sending and receiving various information between processor 46 and processor 28 via network 54.

[0040] Figure 2 An example of the main functions of the data processing device 12 and the smart device 14 is shown.

[0041] like Figure 2 As shown, in the data processing apparatus 12, specific processing is performed by the processor 28. A specific processing program 56 is stored in the memory 32. The specific processing program 56 is an example of a "program" as understood in this disclosure. The processor 28 reads the specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. Specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0042] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0043] In the smart device 14, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The specific processing program 60 is used in conjunction with the data processing system 10. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart device 14 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0044] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. Furthermore, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of the processing performed by the data processing system 10 of the first embodiment will be described.

[0045] Implementation Method 1

[0046] The card game assistance system described in this invention utilizes AI to provide optimal deck building and practice support for card game enthusiasts. Based on the user's personality, preferred playstyle, past deck information, and match history, the AI ​​proposes the optimal deck. The system then allows users to engage in virtual battles against the AI, trying various strategies. Furthermore, the system enables the AI ​​to manipulate opponents on a virtual battlefield and visually analyze their strategies. The system also provides a service where the AI ​​analyzes the user's playstyle and offers suggestions for overcoming weaknesses. Moreover, through IoT functionality that digitizes the usage information of physical decks, the AI ​​can analyze game data in real time. Thus, the card game assistance system provides a more engaging environment for card game enthusiasts. For example, users can build decks that match their personality and playstyle, and learn various strategies by playing against the AI. Additionally, with the improvement suggestions provided by the AI, users can enhance their playstyle. Furthermore, by digitizing the usage information of physical decks, users can monitor deck performance in real time and build the optimal deck.

[0047] The card game assistance system described in this embodiment includes a proposal department, a battle department, an analysis department, an improvement department, and a digitization department. The proposal department proposes optimal card decks based on the user's personality, preferred playstyle, past deck information, and battle history. For example, the proposal department uses AI to analyze the user's past deck usage and battle results to propose decks that match the user's playstyle. For instance, for users who prefer an aggressive playstyle, the proposal department recommends decks containing more high-attack cards. For users who prefer a defensive playstyle, the proposal department may also recommend decks containing more high-defense cards. Furthermore, for users who prefer a balanced playstyle, the proposal department may also recommend decks that are both offensive and defensive. The battle department uses the decks proposed by the proposal department to conduct virtual battles against the AI. For example, the battle department allows users to practice in an environment close to real-world battles by having them play against the AI. The battle department allows users to learn how to counter different strategies employed by the AI. For example, when the AI ​​adopts an aggressive strategy, the user can try a defensive strategy; when the AI ​​adopts a defensive strategy, the user can try an offensive strategy. Furthermore, when the AI ​​adopts a balanced strategy, users can try multiple strategies. The Analysis Department performs visual analysis of the battle results conducted by the Battle Department. For example, the Analysis Department has the function of visually analyzing the AI's actions against opponents in a virtual battlefield. Users can intuitively see the AI's actions against opponents and understand its strategies. For example, the Analysis Department can visualize the timing and order in which the AI ​​uses specific cards, allowing users to learn the strategy. In addition, the Analysis Department can also visualize the effects of the strategies adopted by the AI ​​in battle. For example, the Analysis Department can display the effects of the cards used by the AI ​​through charts or heatmaps. Based on the results obtained by the Analysis Department, the Improvement Department proposes suggestions for improving the game style. For example, the Improvement Department uses AI to analyze user mistakes and areas for improvement in battles and provides specific suggestions. The Improvement Department can improve the user's game style by suggesting the timing of specific card usage or strategy changes. For example, the Improvement Department can suggest that users improve the timing of specific card usage, change specific strategies, or try new strategies. The Digitization Department digitizes the usage information of physical decks. For example, the Digitization Department collects information on the physical decks actually used by users through sensors, and the AI ​​performs data analysis. The digitization department allows users to monitor deck performance in real time and adjust their decks as needed. For example, the digitization department can digitize the types and frequency of cards used by the user, and AI can evaluate deck performance based on this data. Therefore, the card game assistance system described in this embodiment can propose optimal decks based on the user's personality and playing style, learn strategies through playing against AI, and improve their playing style.

[0048] The proposal department proposes optimal decks based on users' personalities, playstyle preferences, past deck information, and match history. For example, the department uses AI to analyze users' past deck usage and match results to suggest decks that match their playstyle. Specifically, the AI ​​analyzes users' past match data in detail, including win rates, frequently used cards, and reactions to opponent strategies. This allows them to understand which cards users prefer and which strategies they excel at. For instance, for users who prefer an aggressive playstyle, the proposal department recommends decks with more high-attack cards. In this case, the AI ​​will combine high-attack cards and select cards suitable for offensive strategies. For users who prefer a defensive playstyle, the proposal department can also recommend decks with more high-defense cards. In this case, the AI ​​will recommend a combination of high-defense cards and cards that effectively defend against opponent attacks. Furthermore, for users who prefer a balanced playstyle, the proposal department can also recommend decks that are both offensive and defensive. The AI ​​will balance offense and defense to build decks that can handle various situations. When making these suggestions, the proposal department can collect user feedback to continuously improve the content. For example, AI can analyze the results of users using suggested decks and improve the accuracy of future suggestions based on those results. Furthermore, the proposal department can also suggest optimal deck compositions when users add new cards or replace existing ones. Thus, the proposal department can propose optimal decks based on users' playstyles and preferences, enhancing the user's gaming experience.

[0049] The Battle Department utilizes decks proposed by the Proposal Department to conduct virtual battles against AI. For example, by having users battle against AI, the Battle Department allows users to practice in an environment close to real-world combat. Specifically, the Battle Department employs advanced AI algorithms to generate virtual opponents with various strategies. The AI ​​flexibly utilizes different strategies, such as offensive, defensive, and balanced approaches, providing users with diverse battle scenarios. For instance, when the AI ​​adopts an offensive strategy, the user can try a defensive strategy; in this case, the AI ​​will actively use high-attack cards to apply pressure. Conversely, when the AI ​​adopts a defensive strategy, the user can try an offensive strategy; in this case, the AI ​​will use high-defense cards to effectively defend against user attacks. Furthermore, when the AI ​​adopts a balanced strategy, the user can try multiple strategies; the AI ​​will flexibly switch between offense and defense, requiring users to adopt diverse approaches. Through these battle scenarios, the Battle Department helps users improve their ability to adapt to different strategies. The Battle Department also records battle results in real time, allowing users to confirm their own playstyle and the effectiveness of their strategies. Thus, the Battle Department enables users to practice and improve their strategies in an environment close to real-world combat. The battle department also records users' choices and behaviors during battles, allowing the analysis and improvement departments to utilize this data. This enables the battle department to support user skill improvement and enhance the gaming experience.

[0050] The Analysis Department provides visual analysis of the battle results conducted by the Battle Department. For example, the Analysis Department has the capability to visualize and analyze the strategies of AI operating opponents on a virtual battlefield. Specifically, the Analysis Department analyzes each stage of the battle in detail, visually demonstrating when the user used which cards and how the AI ​​reacted. For instance, the Analysis Department can visualize the timing and order in which the AI ​​uses specific cards, allowing users to learn the strategy. This enables users to understand the AI's strategy and integrate it into their own gameplay. Furthermore, the Analysis Department can visualize the effects of the strategies employed by the AI ​​in battle. For example, the Analysis Department can display the effects of the cards used by the AI ​​through charts or heatmaps, allowing users to intuitively understand the impact of specific cards or strategies on the battle results. Further, the Analysis Department can perform statistical analysis of the battle results to identify the user's strengths and weaknesses. For example, by displaying the success rate of using specific cards or the win rate against specific strategies, users can objectively evaluate their own gameplay style. The Analysis Department provides these visual analysis results to users, giving them specific guidance to improve their gameplay style. Thus, the Analysis Department helps users analyze battle results in detail, learn strategies, and improve their gameplay style.

[0051] Based on the results obtained by the Analysis Department, the Improvement Department proposes suggestions for improving gameplay style. For example, the Improvement Department uses AI to analyze user mistakes and areas for improvement during matches, providing specific suggestions. Specifically, the Improvement Department analyzes user match data in detail, indicating when to use which cards and which strategies are more effective. For instance, the Improvement Department may suggest improving the timing of users using specific cards. The AI ​​analyzes the optimal timing for using specific cards based on past match data and provides the results to the user. Furthermore, the Improvement Department can suggest users change specific strategies. For example, if the AI ​​detects that a user has adopted an offensive strategy when a defensive strategy should have been used, it will point out the impact of this choice on the match result and suggest areas for improvement in the next match. Further, the Improvement Department may suggest trying new strategies. The AI ​​will suggest new strategies or card combinations based on the user's playstyle and preferences, providing the user with opportunities to try new strategies. Through these suggestions, the Improvement Department enables users to continuously improve their gameplay style and enhance their combat skills. The Improvement Department also collects user feedback to continuously refine the suggestions. Therefore, the Improvement Department can provide users with specific suggestions for improving their gameplay style, thus enhancing the gaming experience.

[0052] The Digitalization Department digitizes the usage information of physical decks. For example, it collects information about the actual physical decks used by users through sensors, which is then analyzed by AI. Specifically, the Digitalization Department uses RFID tags or barcode readers embedded in the cards to collect real-time data on the types and frequency of cards used by users. This allows for detailed recording of when and which cards were used and which were most effective. For instance, the Digitalization Department can digitize the types and frequency of cards used by users, and AI can evaluate deck performance based on this data. The AI ​​analyzes the collected data to identify the strengths and weaknesses of the user's deck. Furthermore, the Digitalization Department allows users to check deck performance in real time and adjust their decks as needed. For example, during a match, users can instantly check the effects of specific cards or reassess deck balance, and the Digitalization Department can provide relevant information immediately. Moreover, the Digitalization Department can achieve seamless integration between physical and digital decks, allowing users to enjoy the convenience of digital decks while using physical decks. Through these functions, the Digitalization Department enables users to efficiently utilize both physical and digital decks, enhancing the gaming experience.

[0053] The proposal department can analyze data from a user's past opponents and suggest decks effective against specific opponents. For example, it analyzes the deck composition of past opponents and suggests decks containing cards effective against them. If a user struggles against a particular opponent, the proposal department can also suggest decks that break that opponent's strategy. The department can also refer to decks from opponents the user has defeated and suggest decks employing similar strategies. Therefore, by suggesting decks effective against specific opponents, the win rate can be improved. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input data from the user's past opponents into a generative AI, which will then generate deck suggestions effective against specific opponents.

[0054] The proposal department can adjust deck difficulty based on the user's current in-game level or score. For example, it might recommend decks with more basic cards for beginners, more strategic cards for intermediate players, and complex combos for advanced players. Thus, by recommending decks based on user level or score, decks of appropriate difficulty can be provided. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's in-game level or score into a generative AI, which will then adjust the deck difficulty.

[0055] The proposal department can consider a user's geographical location information and suggest decks that reflect region-specific strategies. For example, if a user lives in a cold region, the proposal department will recommend a deck with more Ice cards; if a user lives in a tropical region, it may recommend a deck with more Fire cards; and if a user lives in an urban area, it may recommend a deck with more Machine cards. Thus, by suggesting decks that reflect region-specific strategies, suitable decks can be provided to users. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's geographical location information into a generative AI, which will then generate deck suggestions that reflect region-specific strategies.

[0056] The proposal department can analyze users' social media activity and suggest decks that reflect relevant trends. For example, it might recommend decks containing cards that users frequently discuss on social media; it can also refer to decks used by opinion leaders followed by users and offer corresponding suggestions; and it can recommend decks containing popular cards in the user's community. Thus, by suggesting decks that reflect social media trends, the department can provide users with the latest deck options. Some or all of the above processing in the proposal department can be achieved using AI, or it can be done without AI. For example, the proposal department can input users' social media activity data into generative AI, which will then generate trend-reflecting deck suggestions.

[0057] The battle department can reference users' past battle history to enhance countermeasures against specific strategies. For example, the battle department can set up AI with enhanced countermeasures for strategies that users have struggled with; it can also set up AI that adopts similar strategies for strategies that users have won; and it can analyze the strategies that users have used to set up AI with enhanced countermeasures. Thus, by referring to past battle history, countermeasures against specific strategies can be strengthened. Some or all of the above processing in the battle department can be achieved through AI, or it can be done without AI. For example, the battle department can input users' past battle history data into generative AI, which will then execute enhanced countermeasures against specific strategies.

[0058] The battle department can dynamically change the AI's strategy based on the user's playstyle. For example, when the user adopts an aggressive playstyle, the AI ​​adopts a defensive strategy; when the user adopts a defensive playstyle, the AI ​​can also adopt an aggressive strategy; and when the user adopts a balanced playstyle, the AI ​​can flexibly change its strategy. Therefore, by providing corresponding strategies based on the user's playstyle, more effective battles can be achieved. Some or all of the above processing in the battle department can be done by AI, or it can be done without AI. For example, the battle department can input the user's playstyle data into a generative AI, which will then dynamically change the AI's strategy.

[0059] The battle department can take into account the user's geographical location information and implement battle strategies that reflect the specific region. For example, if the user lives in a cold region, the AI ​​can be configured to use more ice-attribute cards; if the user lives in a tropical region, the AI ​​can be configured to use more fire-attribute cards; and if the user lives in an urban area, the AI ​​can be configured to use more machine-attribute cards. Thus, by providing battles that reflect region-specific strategies, a suitable battle environment can be provided for the user. Some or all of the above processing in the battle department can be implemented by AI, or it can be done without AI. For example, the battle department can input the user's geographical location information into a generative AI, which will then set up battles that reflect region-specific strategies.

[0060] The Battle Department can analyze users' social media activity to create matches that reflect relevant trends. For example, it can configure AI to use cards that users frequently discuss on social media; it can also configure AI to reference the decks used by opinion leaders followed by users; and it can even configure AI to use users to play popular cards in the community. Thus, by providing matches that reflect social media trends, it can offer users the latest battle environment. Some or all of the above processing in the Battle Department can be done using AI, or it can be done without AI. For example, the Battle Department can input users' social media activity data into generative AI, which will then set up matches that reflect these trends.

[0061] The analysis department can refer to a user's past match data to conduct detailed analysis of the effectiveness of specific strategies. For example, the analysis department can analyze the win rate of strategies used by the user in the past and evaluate their effectiveness; it can also analyze the strategies of the user's past opponents and evaluate their effectiveness; it can also analyze the card combinations used by the user in the past and evaluate their effectiveness. Thus, by referring to past match data, the effectiveness of specific strategies can be analyzed in detail. Some or all of the above processing in the analysis department can be achieved by AI, or it can be done without AI. For example, the analysis department can input the user's past match data into generative AI, which will then perform a detailed analysis of the effectiveness of specific strategies.

[0062] The analytics department can apply different analytics algorithms based on a user's playstyle. For example, an offensive analytics algorithm can be applied when the user plays an aggressive style; a defensive analytics algorithm can be applied when the user plays a defensive style; and a balanced analytics algorithm can be applied when the user plays a balanced style. Therefore, by providing corresponding analysis based on the user's playstyle, more effective analytical results can be provided. Some or all of the above processing in the analytics department can be implemented using AI, or it can be done without AI. For example, the analytics department can input the user's playstyle data into a generative AI, which can then apply different analytics algorithms.

[0063] The analysis department can take into account the user's geographical location information and perform analysis reflecting region-specific strategies. For example, if the user lives in a cold region, the effects of ice-attribute cards can be analyzed; if the user lives in a tropical region, the effects of fire-attribute cards can be analyzed; and if the user lives in an urban area, the effects of machine-attribute cards can be analyzed. Thus, by providing analysis reflecting region-specific strategies, suitable analytical results can be provided to the user. Some or all of the above processing in the analysis department can be implemented using AI, or it can be done without AI. For example, the analysis department can input the user's geographical location information into a generative AI, which will then perform the analysis reflecting region-specific strategies.

[0064] The analytics department can analyze users' social media activity to reflect relevant trends. For example, it can analyze the effectiveness of cards frequently discussed on social media; it can also analyze the effectiveness of cards used by opinion leaders followed by users; and it can analyze the effectiveness of users participating in popular cards within communities. Thus, by providing analysis reflecting social media trends, it can offer users the latest analytical results. Some or all of the above processing in the analytics department can be achieved using AI, or it can be done without AI. For example, the analytics department can input users' social media activity data into generative AI, which will then perform trend-reflecting analysis.

[0065] The Improvement Department can refer to users' past battle data to propose specific improvement measures targeting particular weaknesses. For example, the Improvement Department can propose improvements to strategies that users have struggled with; it can also analyze users' past mistakes and propose improvement measures; it can also analyze the effectiveness of cards used by users in the past and propose improvement measures. Thus, by referring to past battle data, specific improvement measures targeting particular weaknesses can be provided. Some or all of the above processing in the Improvement Department can be implemented by AI, or it can be done without AI. For example, the Improvement Department can input users' past battle data into generative AI, which will then propose specific improvement measures targeting particular weaknesses.

[0066] The improvement department can apply different improvement algorithms based on the user's playstyle. For example, it can propose offensive improvement measures when the user adopts an aggressive playstyle, defensive improvement measures when the user adopts a defensive playstyle, and balanced improvement measures when the user adopts a balanced playstyle. Therefore, by providing corresponding improvement measures based on the user's playstyle, more effective improvements can be achieved. Some or all of the above processing in the improvement department can be implemented using AI, or it can be done without AI. For example, the improvement department can input the user's playstyle data into a generative AI, which will then apply different improvement algorithms.

[0067] The Improvement Department can consider users' geographical location information and propose improvement measures that reflect region-specific strategies. For example, if a user lives in a cold region, improvements can be made to enhance the effects of ice-attribute cards; if a user lives in a tropical region, improvements can be made to enhance the effects of fire-attribute cards; and if a user lives in an urban area, improvements can be made to enhance the effects of machine-attribute cards. Thus, by providing improvement measures that reflect region-specific strategies, suitable improvement measures can be offered to users. Some or all of the above processing in the Improvement Department can be implemented using AI, or it can be done without AI. For example, the Improvement Department can input the user's geographical location information into a generative AI, which will then propose improvement measures that reflect region-specific strategies.

[0068] The Improvement Department can analyze users' social media activity and propose improvement measures that reflect relevant trends. For example, it can propose measures to enhance the effectiveness of cards that users discuss more frequently on social media; it can also propose measures to improve the effectiveness of cards used by opinion leaders followed by users; and it can also propose measures to enhance the effectiveness of popular cards in user communities. Thus, by providing improvement measures that reflect social media trends, the department can offer users the latest improvements. Some or all of the above processing in the Improvement Department can be achieved using AI, or it can be done without AI. For example, the Improvement Department can input users' social media activity data into generative AI, which will then propose improvement measures that reflect the trends.

[0069] The digitization department can reference users' past deck usage data to meticulously record the frequency of use for specific cards. For example, the department can record the frequency of frequently used cards, the effects of cards used in the past, and card combinations used by users. Thus, by referencing past deck usage data, the frequency of use for specific cards can be recorded in detail. Some or all of the above processing in the digitization department can be achieved using AI, or it can be done without AI. For example, the digitization department can input users' past deck usage data into a generative AI, which will then record the frequency of use for specific cards in detail.

[0070] The digitization department can apply different digitization algorithms based on a user's playstyle. For example, an offensive digitization algorithm can be applied when the user plays an aggressive style; a defensive digitization algorithm can be applied when the user plays a defensive style; and a balanced digitization algorithm can be applied when the user plays a balanced style. Therefore, by providing corresponding digitization based on the user's playstyle, more effective data can be provided. Some or all of the above processing in the digitization department can be achieved through AI, or it can be done without AI. For example, the digitization department can input the user's playstyle data into a generative AI, which will then apply different digitization algorithms.

[0071] The Digitalization Department can consider users' geographic location information and prioritize digitizing region-specific data. For example, if a user lives in a cold region, data on ice-attribute cards can be prioritized; if a user lives in a tropical region, data on fire-attribute cards can be prioritized; and if a user lives in an urban area, data on mechanical-attribute cards can be prioritized. Thus, by prioritizing the digitization of region-specific data, suitable data can be provided to users. Some or all of the above processing in the Digitalization Department can be achieved through AI, or it can be done without AI. For example, the Digitalization Department can input users' geographic location information into generative AI, which will then prioritize the digitization of region-specific data.

[0072] The Digital Transformation Department can analyze users' social media activity, digitizing data that reflects relevant trends. For example, it can digitize data on which cards users discuss most frequently on social media; it can also digitize data on the cards used by opinion leaders followed by users; and it can digitize data on which users participate in popular cards within communities. Thus, by digitizing data reflecting social media trends, the department can provide users with the latest data. Some or all of the above processing in the Digital Transformation Department can be achieved using AI, or it can be done without AI. For example, the Digital Transformation Department can input users' social media activity data into generative AI, which will then digitize trend-reflecting data.

[0073] The digitization department can consider the user's health status and prioritize the digitization of optimal data. For example, when a user is fatigued, important data is prioritized; when a user is healthy, detailed data is prioritized; and when a user is unwell, key data is prioritized. Therefore, by prioritizing data digitization based on the user's health status, suitable data can be provided. Some or all of the above processing in the digitization department can be achieved through AI, or it can be done without AI. For example, the digitization department can input the user's health status data into a generative AI, which will then prioritize the digitization of health-based data.

[0074] The system involved in this embodiment is not limited to the above examples. For example, various modifications can be made as follows.

[0075] The proposal department can analyze data from a user's past opponents and suggest decks effective against specific opponents. For example, it analyzes the deck composition of past opponents and suggests decks containing cards effective against them. If a user struggles against a particular opponent, the proposal department can also suggest decks that break that opponent's strategy. The department can also refer to decks from opponents the user has defeated and suggest decks employing similar strategies. Therefore, by suggesting decks effective against specific opponents, the win rate can be improved. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input data from the user's past opponents into a generative AI, which will then generate deck suggestions effective against specific opponents.

[0076] The proposal department can adjust deck difficulty based on the user's current in-game level or score. For example, it might recommend decks with more basic cards for beginners, more strategic cards for intermediate players, and complex combos for advanced players. Thus, by recommending decks based on user level or score, decks of appropriate difficulty can be provided. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's in-game level or score into a generative AI, which will then adjust the deck difficulty.

[0077] The proposal department can consider a user's geographical location information and suggest decks that reflect region-specific strategies. For example, if a user lives in a cold region, the proposal department will recommend a deck with more Ice cards; if a user lives in a tropical region, it may recommend a deck with more Fire cards; and if a user lives in an urban area, it may recommend a deck with more Machine cards. Thus, by suggesting decks that reflect region-specific strategies, suitable decks can be provided to users. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's geographical location information into a generative AI, which will then generate deck suggestions that reflect region-specific strategies.

[0078] The proposal department can analyze users' social media activity and suggest decks that reflect relevant trends. For example, it might recommend decks containing cards that users frequently discuss on social media; it can also refer to decks used by opinion leaders followed by users and offer corresponding suggestions; and it can recommend decks containing popular cards in the user's community. Thus, by suggesting decks that reflect social media trends, the department can provide users with the latest deck options. Some or all of the above processing in the proposal department can be achieved using AI, or it can be done without AI. For example, the proposal department can input users' social media activity data into generative AI, which will then generate trend-reflecting deck suggestions.

[0079] The battle department can reference users' past battle history to enhance countermeasures against specific strategies. For example, the battle department can set up AI with enhanced countermeasures for strategies that users have struggled with; it can also set up AI that adopts similar strategies for strategies that users have won; and it can analyze the strategies that users have used to set up AI with enhanced countermeasures. Thus, by referring to past battle history, countermeasures against specific strategies can be strengthened. Some or all of the above processing in the battle department can be achieved through AI, or it can be done without AI. For example, the battle department can input users' past battle history data into generative AI, which will then execute enhanced countermeasures against specific strategies.

[0080] The battle department can dynamically change the AI's strategy based on the user's playstyle. For example, when the user adopts an aggressive playstyle, the AI ​​adopts a defensive strategy; when the user adopts a defensive playstyle, the AI ​​can also adopt an aggressive strategy; and when the user adopts a balanced playstyle, the AI ​​can flexibly change its strategy. Therefore, by providing corresponding strategies based on the user's playstyle, more effective battles can be achieved. Some or all of the above processing in the battle department can be done by AI, or it can be done without AI. For example, the battle department can input the user's playstyle data into a generative AI, which will then dynamically change the AI's strategy.

[0081] The following is a brief description of the processing flow of Implementation Method 1.

[0082] Step 1: The proposal department proposes the optimal deck based on the user's personality, preferred playstyle, past deck information, and match history. For example, the proposal department uses AI to analyze the user's past deck usage and match results to propose decks that match the user's playstyle. For users who prefer an aggressive playstyle, decks with more high-attack cards are recommended; for users who prefer a defensive playstyle, decks with more high-defense cards are recommended; and for users who prefer a balanced playstyle, decks with both offensive and defensive capabilities are recommended.

[0083] Step 2: The Battle Department uses the decks proposed by the Proposal Department to conduct virtual battles against the AI. For example, users can practice against the AI ​​in an environment close to real battles. The Battle Department uses different strategies to control the opponent through the AI, and users can learn how to counter these strategies. When the AI ​​adopts an aggressive strategy, users can try a defensive strategy; when the AI ​​adopts a defensive strategy, users can try an aggressive strategy. In addition, when the AI ​​adopts a balanced strategy, users can try a variety of strategies.

[0084] Step 3: The Analysis Department performs visual analysis of the battle results conducted by the Battle Department. For example, the Analysis Department has the function of visually analyzing the AI's actions against opponents on a virtual battlefield. Users can intuitively see the AI's actions against the opponent and understand its strategy. The AI ​​can visualize the timing and order of specific card usage, allowing users to learn the strategy. In addition, the AI ​​can also visualize the effects of the strategies adopted in the battle. For example, the AI ​​can display the effects of the cards used through charts or heatmaps.

[0085] Step 4: Based on the results obtained by the Analysis Department, the Improvement Department proposes suggestions for improving the game style. For example, AI analyzes the user's mistakes and areas for improvement during battles, providing specific suggestions. This can include suggesting the timing of specific card usage or strategy changes to enhance the user's game style. Suggestions may include improving the timing of specific card usage, changing strategies, and trying new strategies.

[0086] Step 5: The digitization department digitizes the usage information of the physical card decks. For example, sensors collect information on the actual physical card decks used by users, and AI analyzes the data. Users can check the deck performance in real time and adjust the deck as needed. The types and frequency of cards used by users can be digitized, and AI evaluates the deck performance based on this data.

[0087] Implementation Method 2

[0088] The card game assistance system described in this invention utilizes AI to provide optimal deck building and practice support for card game enthusiasts. Based on the user's personality, preferred playstyle, past deck information, and match history, the AI ​​proposes the optimal deck. The system then allows users to engage in virtual battles against the AI, trying various strategies. Furthermore, the system enables the AI ​​to manipulate opponents on a virtual battlefield and visually analyze their strategies. The system also provides a service where the AI ​​analyzes the user's playstyle and offers suggestions for overcoming weaknesses. Moreover, through IoT functionality that digitizes the usage information of physical decks, the AI ​​can analyze game data in real time. Thus, the card game assistance system provides a more engaging environment for card game enthusiasts. For example, users can build decks that match their personality and playstyle, and learn various strategies by playing against the AI. Additionally, with the improvement suggestions provided by the AI, users can enhance their playstyle. Furthermore, by digitizing the usage information of physical decks, users can monitor deck performance in real time and build the optimal deck.

[0089] The card game assistance system described in this embodiment includes a proposal department, a battle department, an analysis department, an improvement department, and a digitization department. The proposal department proposes optimal card decks based on the user's personality, preferred playstyle, past deck information, and battle history. For example, the proposal department uses AI to analyze the user's past deck usage and battle results to propose decks that match the user's playstyle. For instance, for users who prefer an aggressive playstyle, the proposal department recommends decks containing more high-attack cards. For users who prefer a defensive playstyle, the proposal department may also recommend decks containing more high-defense cards. Furthermore, for users who prefer a balanced playstyle, the proposal department may also recommend decks that are both offensive and defensive. The battle department uses the decks proposed by the proposal department to conduct virtual battles against the AI. For example, the battle department allows users to practice in an environment close to real-world battles by having them play against the AI. The battle department allows users to learn how to counter different strategies employed by the AI. For example, when the AI ​​adopts an aggressive strategy, the user can try a defensive strategy; when the AI ​​adopts a defensive strategy, the user can try an offensive strategy. Furthermore, when the AI ​​adopts a balanced strategy, users can try multiple strategies. The Analysis Department performs visual analysis of the battle results conducted by the Battle Department. For example, the Analysis Department has the function of visually analyzing the AI's actions against opponents in a virtual battlefield. Users can intuitively see the AI's actions against opponents and understand its strategies. For example, the Analysis Department can visualize the timing and order in which the AI ​​uses specific cards, allowing users to learn the strategy. In addition, the Analysis Department can also visualize the effects of the strategies adopted by the AI ​​in battle. For example, the Analysis Department can display the effects of the cards used by the AI ​​through charts or heatmaps. Based on the results obtained by the Analysis Department, the Improvement Department proposes suggestions for improving the game style. For example, the Improvement Department uses AI to analyze user mistakes and areas for improvement in battles and provides specific suggestions. The Improvement Department can improve the user's game style by suggesting the timing of specific card usage or strategy changes. For example, the Improvement Department can suggest that users improve the timing of specific card usage, change specific strategies, or try new strategies. The Digitization Department digitizes the usage information of physical decks. For example, the Digitization Department collects information on the physical decks actually used by users through sensors, and the AI ​​performs data analysis. The digitization department allows users to monitor deck performance in real time and adjust their decks as needed. For example, the digitization department can digitize the types and frequency of cards used by the user, and AI can evaluate deck performance based on this data. Therefore, the card game assistance system described in this embodiment can propose optimal decks based on the user's personality and playing style, learn strategies through playing against AI, and improve their playing style.

[0090] The proposal department proposes optimal decks based on users' personalities, playstyle preferences, past deck information, and match history. For example, the department uses AI to analyze users' past deck usage and match results to suggest decks that match their playstyle. Specifically, the AI ​​analyzes users' past match data in detail, including win rates, frequently used cards, and reactions to opponent strategies. This allows them to understand which cards users prefer and which strategies they excel at. For instance, for users who prefer an aggressive playstyle, the proposal department recommends decks with more high-attack cards. In this case, the AI ​​will combine high-attack cards and select cards suitable for offensive strategies. For users who prefer a defensive playstyle, the proposal department can also recommend decks with more high-defense cards. In this case, the AI ​​will recommend a combination of high-defense cards and cards that effectively defend against opponent attacks. Furthermore, for users who prefer a balanced playstyle, the proposal department can also recommend decks that are both offensive and defensive. The AI ​​will balance offense and defense to build decks that can handle various situations. When making these suggestions, the proposal department can collect user feedback to continuously improve the content. For example, AI can analyze the results of users using suggested decks and improve the accuracy of future suggestions based on those results. Furthermore, the proposal department can also suggest optimal deck compositions when users add new cards or replace existing ones. Thus, the proposal department can propose optimal decks based on users' playstyles and preferences, enhancing the user's gaming experience.

[0091] The Battle Department utilizes decks proposed by the Proposal Department to conduct virtual battles against AI. For example, by having users battle against AI, the Battle Department allows users to practice in an environment close to real-world combat. Specifically, the Battle Department employs advanced AI algorithms to generate virtual opponents with various strategies. The AI ​​flexibly utilizes different strategies, such as offensive, defensive, and balanced approaches, providing users with diverse battle scenarios. For instance, when the AI ​​adopts an offensive strategy, the user can try a defensive strategy; in this case, the AI ​​will actively use high-attack cards to apply pressure. Conversely, when the AI ​​adopts a defensive strategy, the user can try an offensive strategy; in this case, the AI ​​will use high-defense cards to effectively defend against user attacks. Furthermore, when the AI ​​adopts a balanced strategy, the user can try multiple strategies; the AI ​​will flexibly switch between offense and defense, requiring users to adopt diverse approaches. Through these battle scenarios, the Battle Department helps users improve their ability to adapt to different strategies. The Battle Department also records battle results in real time, allowing users to confirm their own playstyle and the effectiveness of their strategies. Thus, the Battle Department enables users to practice and improve their strategies in an environment close to real-world combat. The battle department also records users' choices and behaviors during battles, allowing the analysis and improvement departments to utilize this data. This enables the battle department to support user skill improvement and enhance the gaming experience.

[0092] The Analysis Department provides visual analysis of the battle results conducted by the Battle Department. For example, the Analysis Department has the capability to visualize and analyze the strategies of AI operating opponents on a virtual battlefield. Specifically, the Analysis Department analyzes each stage of the battle in detail, visually demonstrating when the user used which cards and how the AI ​​reacted. For instance, the Analysis Department can visualize the timing and order in which the AI ​​uses specific cards, allowing users to learn the strategy. This enables users to understand the AI's strategy and integrate it into their own gameplay. Furthermore, the Analysis Department can visualize the effects of the strategies employed by the AI ​​in battle. For example, the Analysis Department can display the effects of the cards used by the AI ​​through charts or heatmaps, allowing users to intuitively understand the impact of specific cards or strategies on the battle results. Further, the Analysis Department can perform statistical analysis of the battle results to identify the user's strengths and weaknesses. For example, by displaying the success rate of using specific cards or the win rate against specific strategies, users can objectively evaluate their own gameplay style. The Analysis Department provides these visual analysis results to users, giving them specific guidance to improve their gameplay style. Thus, the Analysis Department helps users analyze battle results in detail, learn strategies, and improve their gameplay style.

[0093] Based on the results obtained by the Analysis Department, the Improvement Department proposes suggestions for improving gameplay style. For example, the Improvement Department uses AI to analyze user mistakes and areas for improvement during matches, providing specific suggestions. Specifically, the Improvement Department analyzes user match data in detail, indicating when to use which cards and which strategies are more effective. For instance, the Improvement Department may suggest improving the timing of users using specific cards. The AI ​​analyzes the optimal timing for using specific cards based on past match data and provides the results to the user. Furthermore, the Improvement Department can suggest users change specific strategies. For example, if the AI ​​detects that a user has adopted an offensive strategy when a defensive strategy should have been used, it will point out the impact of this choice on the match result and suggest areas for improvement in the next match. Further, the Improvement Department may suggest trying new strategies. The AI ​​will suggest new strategies or card combinations based on the user's playstyle and preferences, providing the user with opportunities to try new strategies. Through these suggestions, the Improvement Department enables users to continuously improve their gameplay style and enhance their combat skills. The Improvement Department also collects user feedback to continuously refine the suggestions. Therefore, the Improvement Department can provide users with specific suggestions for improving their gameplay style, thus enhancing the gaming experience.

[0094] The Digitalization Department digitizes the usage information of physical decks. For example, it collects information about the actual physical decks used by users through sensors, which is then analyzed by AI. Specifically, the Digitalization Department uses RFID tags or barcode readers embedded in the cards to collect real-time data on the types and frequency of cards used by users. This allows for detailed recording of when and which cards were used and which were most effective. For instance, the Digitalization Department can digitize the types and frequency of cards used by users, and AI can evaluate deck performance based on this data. The AI ​​analyzes the collected data to identify the strengths and weaknesses of the user's deck. Furthermore, the Digitalization Department allows users to check deck performance in real time and adjust their decks as needed. For example, during a match, users can instantly check the effects of specific cards or reassess deck balance, and the Digitalization Department can provide relevant information immediately. Moreover, the Digitalization Department can achieve seamless integration between physical and digital decks, allowing users to enjoy the convenience of digital decks while using physical decks. Through these functions, the Digitalization Department enables users to efficiently utilize both physical and digital decks, enhancing the gaming experience.

[0095] The proposal department can infer users' emotions and adjust the type of decks proposed based on these inferences. For example, when a user is stressed, the department might recommend a defensive deck to help them relax; when the user is excited, it might recommend a deck with more offensive cards; and when the user is calm, it might recommend a balanced deck. Thus, by recommending decks based on user emotions, more suitable decks can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text-generated AI (such as LLM) or multimodal generative AI, but is not limited to these. Some or all of the above processing in the proposal department can be achieved through AI, or AI can be omitted. For example, the proposal department can input users' emotional data into generative AI, which will then generate emotion-based deck suggestions.

[0096] The proposal department can analyze data from a user's past opponents and suggest decks effective against specific opponents. For example, it analyzes the deck composition of past opponents and suggests decks containing cards effective against them. If a user struggles against a particular opponent, the proposal department can also suggest decks that break that opponent's strategy. The department can also refer to decks from opponents the user has defeated and suggest decks employing similar strategies. Therefore, by suggesting decks effective against specific opponents, the win rate can be improved. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input data from the user's past opponents into a generative AI, which will then generate deck suggestions effective against specific opponents.

[0097] The proposal department can adjust deck difficulty based on the user's current in-game level or score. For example, it might recommend decks with more basic cards for beginners, more strategic cards for intermediate players, and complex combos for advanced players. Thus, by recommending decks based on user level or score, decks of appropriate difficulty can be provided. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's in-game level or score into a generative AI, which will then adjust the deck difficulty.

[0098] The proposal department can infer users' emotions and adjust the card order in the proposed deck based on these inferences. For example, when a user is tense, the department might recommend a deck with more defensive cards in the early game; when the user is relaxed, it might recommend a deck with more offensive cards; and when the user is excited, it might recommend a deck with more powerful cards. Thus, by recommending card order based on user emotions, more effective decks can be provided. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the proposal department can be achieved through AI, or AI can be omitted. For example, the proposal department can input users' emotional data into generative AI, which can then adjust the card order based on the emotion.

[0099] The proposal department can consider a user's geographical location information and suggest decks that reflect region-specific strategies. For example, if a user lives in a cold region, the proposal department will recommend a deck with more Ice cards; if a user lives in a tropical region, it may recommend a deck with more Fire cards; and if a user lives in an urban area, it may recommend a deck with more Machine cards. Thus, by suggesting decks that reflect region-specific strategies, suitable decks can be provided to users. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's geographical location information into a generative AI, which will then generate deck suggestions that reflect region-specific strategies.

[0100] The proposal department can analyze users' social media activity and suggest decks that reflect relevant trends. For example, it might recommend decks containing cards that users frequently discuss on social media; it can also refer to decks used by opinion leaders followed by users and offer corresponding suggestions; and it can recommend decks containing popular cards in the user's community. Thus, by suggesting decks that reflect social media trends, the department can provide users with the latest deck options. Some or all of the above processing in the proposal department can be achieved using AI, or it can be done without AI. For example, the proposal department can input users' social media activity data into generative AI, which will then generate trend-reflecting deck suggestions.

[0101] The battle department can infer the user's emotions and adjust the difficulty of the battle accordingly. For example, when the user is tense, the opponent's strength is set to low; when the user is relaxed, the opponent's strength is set to medium; and when the user is excited, the opponent's strength is set to high. Thus, by providing battles with appropriate difficulty based on the user's emotions, a suitable battle environment can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the battle department can be achieved through AI, or AI can be omitted. For example, the battle department can input the user's emotional data into the generative AI, which will then adjust the battle difficulty based on the emotion.

[0102] The battle department can reference users' past battle history to enhance countermeasures against specific strategies. For example, the battle department can set up AI with enhanced countermeasures for strategies that users have struggled with; it can also set up AI that adopts similar strategies for strategies that users have won; and it can analyze the strategies that users have used to set up AI with enhanced countermeasures. Thus, by referring to past battle history, countermeasures against specific strategies can be strengthened. Some or all of the above processing in the battle department can be achieved through AI, or it can be done without AI. For example, the battle department can input users' past battle history data into generative AI, which will then execute enhanced countermeasures against specific strategies.

[0103] The battle department can dynamically change the AI's strategy based on the user's playstyle. For example, when the user adopts an aggressive playstyle, the AI ​​adopts a defensive strategy; when the user adopts a defensive playstyle, the AI ​​can also adopt an aggressive strategy; and when the user adopts a balanced playstyle, the AI ​​can flexibly change its strategy. Therefore, by providing corresponding strategies based on the user's playstyle, more effective battles can be achieved. Some or all of the above processing in the battle department can be done by AI, or it can be done without AI. For example, the battle department can input the user's playstyle data into a generative AI, which will then dynamically change the AI's strategy.

[0104] The battle department can infer users' emotions and adjust the pace of battles accordingly. For example, it can slow down the pace when users are tense, maintain a normal pace when users are relaxed, and speed up the pace when users are excited. Thus, by providing battles with an appropriate pace based on user emotions, a suitable battle environment can be provided. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text-generating AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the battle department can be achieved through AI, or AI can be omitted. For example, the battle department can input users' emotional data into generative AI, which can then adjust the battle pace based on emotions.

[0105] The battle department can take into account the user's geographical location information and implement battle strategies that reflect the specific region. For example, if the user lives in a cold region, the AI ​​can be configured to use more ice-attribute cards; if the user lives in a tropical region, the AI ​​can be configured to use more fire-attribute cards; and if the user lives in an urban area, the AI ​​can be configured to use more machine-attribute cards. Thus, by providing battles that reflect region-specific strategies, a suitable battle environment can be provided for the user. Some or all of the above processing in the battle department can be implemented by AI, or it can be done without AI. For example, the battle department can input the user's geographical location information into a generative AI, which will then set up battles that reflect region-specific strategies.

[0106] The Battle Department can analyze users' social media activity to create matches that reflect relevant trends. For example, it can configure AI to use cards that users frequently discuss on social media; it can also configure AI to reference the decks used by opinion leaders followed by users; and it can even configure AI to use users to play popular cards in the community. Thus, by providing matches that reflect social media trends, it can offer users the latest battle environment. Some or all of the above processing in the Battle Department can be done using AI, or it can be done without AI. For example, the Battle Department can input users' social media activity data into generative AI, which will then set up matches that reflect these trends.

[0107] The analysis department can infer users' emotions and adjust the display of analysis results accordingly. For example, when users are tense, the analysis department provides a concise and easily identifiable display; when users are relaxed, it provides a display with detailed information; and when users are anxious, it provides a display highlighting key points. Thus, by providing appropriate display methods based on user emotions, appropriate analysis results can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the analysis department can be achieved through AI, or AI can be omitted. For example, the analysis department can input users' emotion data into generative AI, which can then adjust the display of emotion-based analysis results.

[0108] The analysis department can refer to a user's past match data to conduct detailed analysis of the effectiveness of specific strategies. For example, the analysis department can analyze the win rate of strategies used by the user in the past and evaluate their effectiveness; it can also analyze the strategies of the user's past opponents and evaluate their effectiveness; it can also analyze the card combinations used by the user in the past and evaluate their effectiveness. Thus, by referring to past match data, the effectiveness of specific strategies can be analyzed in detail. Some or all of the above processing in the analysis department can be achieved by AI, or it can be done without AI. For example, the analysis department can input the user's past match data into generative AI, which will then perform a detailed analysis of the effectiveness of specific strategies.

[0109] The analytics department can apply different analytics algorithms based on a user's playstyle. For example, an offensive analytics algorithm can be applied when the user plays an aggressive style; a defensive analytics algorithm can be applied when the user plays a defensive style; and a balanced analytics algorithm can be applied when the user plays a balanced style. Therefore, by providing corresponding analysis based on the user's playstyle, more effective analytical results can be provided. Some or all of the above processing in the analytics department can be implemented using AI, or it can be done without AI. For example, the analytics department can input the user's playstyle data into a generative AI, which can then apply different analytics algorithms.

[0110] The analysis department can infer users' emotions and prioritize analysis results based on these inferred emotions. For example, when a user is tense, the analysis department may prioritize displaying important information; when a user is relaxed, it may prioritize displaying detailed information; and when a user is anxious, it may prioritize displaying key information. Thus, by providing analysis results with appropriate priorities based on user emotions, suitable information can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the analysis department can be achieved through AI, or AI may not be used. For example, the analysis department can input users' emotion data into generative AI, which can then determine the priority of emotion-based analysis results.

[0111] The analysis department can take into account the user's geographical location information and perform analysis reflecting region-specific strategies. For example, if the user lives in a cold region, the effects of ice-attribute cards can be analyzed; if the user lives in a tropical region, the effects of fire-attribute cards can be analyzed; and if the user lives in an urban area, the effects of machine-attribute cards can be analyzed. Thus, by providing analysis reflecting region-specific strategies, suitable analytical results can be provided to the user. Some or all of the above processing in the analysis department can be implemented using AI, or it can be done without AI. For example, the analysis department can input the user's geographical location information into a generative AI, which will then perform the analysis reflecting region-specific strategies.

[0112] The analytics department can analyze users' social media activity to reflect relevant trends. For example, it can analyze the effectiveness of cards frequently discussed on social media; it can also analyze the effectiveness of cards used by opinion leaders followed by users; and it can analyze the effectiveness of users participating in popular cards within communities. Thus, by providing analysis reflecting social media trends, it can offer users the latest analytical results. Some or all of the above processing in the analytics department can be achieved using AI, or it can be done without AI. For example, the analytics department can input users' social media activity data into generative AI, which will then perform trend-reflecting analysis.

[0113] The improvement department can infer users' emotions and adjust the content of improvement suggestions based on these inferred emotions. For example, when users are tense, the improvement department can offer concise and easy-to-implement improvement suggestions; when users are relaxed, it can offer detailed improvement suggestions; and when users are excited, it can offer positive improvement suggestions. Thus, by providing appropriate improvement suggestions based on users' emotions, suitable improvement measures can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the improvement department can be achieved through AI, or AI can be omitted. For example, the improvement department can input users' emotion data into generative AI, which can then adjust the content of emotion-based improvement suggestions.

[0114] The Improvement Department can refer to users' past battle data to propose specific improvement measures targeting particular weaknesses. For example, the Improvement Department can propose improvements to strategies that users have struggled with; it can also analyze users' past mistakes and propose improvement measures; it can also analyze the effectiveness of cards used by users in the past and propose improvement measures. Thus, by referring to past battle data, specific improvement measures targeting particular weaknesses can be provided. Some or all of the above processing in the Improvement Department can be implemented by AI, or it can be done without AI. For example, the Improvement Department can input users' past battle data into generative AI, which will then propose specific improvement measures targeting particular weaknesses.

[0115] The improvement department can apply different improvement algorithms based on the user's playstyle. For example, it can propose offensive improvement measures when the user adopts an aggressive playstyle, defensive improvement measures when the user adopts a defensive playstyle, and balanced improvement measures when the user adopts a balanced playstyle. Therefore, by providing corresponding improvement measures based on the user's playstyle, more effective improvements can be achieved. Some or all of the above processing in the improvement department can be implemented using AI, or it can be done without AI. For example, the improvement department can input the user's playstyle data into a generative AI, which will then apply different improvement algorithms.

[0116] The improvement department can infer users' emotions and prioritize improvement suggestions based on these inferred emotions. For example, when users are tense, the improvement department prioritizes important improvement measures; when users are relaxed, it prioritizes detailed improvement measures; and when users are anxious, it prioritizes key improvement measures. Thus, by providing improvement suggestions with appropriate priorities based on user emotions, suitable improvement measures can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the improvement department can be achieved through AI, or AI can be omitted. For example, the improvement department can input users' emotional data into generative AI, which will then determine the priority of emotion-based improvement suggestions.

[0117] The Improvement Department can consider users' geographical location information and propose improvement measures that reflect region-specific strategies. For example, if a user lives in a cold region, improvements can be made to enhance the effects of ice-attribute cards; if a user lives in a tropical region, improvements can be made to enhance the effects of fire-attribute cards; and if a user lives in an urban area, improvements can be made to enhance the effects of machine-attribute cards. Thus, by providing improvement measures that reflect region-specific strategies, suitable improvement measures can be offered to users. Some or all of the above processing in the Improvement Department can be implemented using AI, or it can be done without AI. For example, the Improvement Department can input the user's geographical location information into a generative AI, which will then propose improvement measures that reflect region-specific strategies.

[0118] The Improvement Department can analyze users' social media activity and propose improvement measures that reflect relevant trends. For example, it can propose measures to enhance the effectiveness of cards that users discuss more frequently on social media; it can also propose measures to improve the effectiveness of cards used by opinion leaders followed by users; and it can also propose measures to enhance the effectiveness of popular cards in user communities. Thus, by providing improvement measures that reflect social media trends, the department can offer users the latest improvements. Some or all of the above processing in the Improvement Department can be achieved using AI, or it can be done without AI. For example, the Improvement Department can input users' social media activity data into generative AI, which will then propose improvement measures that reflect the trends.

[0119] The digitization department can infer users' emotions and adjust the data types of digitization accordingly. For example, when users are nervous, the department prioritizes digitizing important data; when users are relaxed, it may prioritize digitizing detailed data; and when users are anxious, it may prioritize digitizing key data. Thus, by providing appropriate data digitization based on user emotions, suitable data can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the digitization department can be achieved through AI, or AI may not be used. For example, the digitization department can input users' emotional data into generative AI, which will then adjust the emotion-based data digitization.

[0120] The digitization department can reference users' past deck usage data to meticulously record the frequency of use for specific cards. For example, the department can record the frequency of frequently used cards, the effects of cards used in the past, and card combinations used by users. Thus, by referencing past deck usage data, the frequency of use for specific cards can be recorded in detail. Some or all of the above processing in the digitization department can be achieved using AI, or it can be done without AI. For example, the digitization department can input users' past deck usage data into a generative AI, which will then record the frequency of use for specific cards in detail.

[0121] The digitization department can apply different digitization algorithms based on a user's playstyle. For example, an offensive digitization algorithm can be applied when the user plays an aggressive style; a defensive digitization algorithm can be applied when the user plays a defensive style; and a balanced digitization algorithm can be applied when the user plays a balanced style. Therefore, by providing corresponding digitization based on the user's playstyle, more effective data can be provided. Some or all of the above processing in the digitization department can be achieved through AI, or it can be done without AI. For example, the digitization department can input the user's playstyle data into a generative AI, which will then apply different digitization algorithms.

[0122] The digitization department can infer users' emotions and prioritize digitized data based on these inferred emotions. For example, when users are tense, the department prioritizes digitizing important data; when users are relaxed, it prioritizes digitizing detailed data; and when users are anxious, it prioritizes digitizing key data. Thus, by prioritizing data digitization according to user emotions, appropriate data can be provided. Emotion inference can be achieved, for example, through emotion inference engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the digitization department can be achieved through AI, or AI can be omitted. For example, the digitization department can input users' emotional data into generative AI, which will then determine the emotion-based data digitization priority.

[0123] The Digitalization Department can consider users' geographic location information and prioritize digitizing region-specific data. For example, if a user lives in a cold region, data on ice-attribute cards can be prioritized; if a user lives in a tropical region, data on fire-attribute cards can be prioritized; and if a user lives in an urban area, data on mechanical-attribute cards can be prioritized. Thus, by prioritizing the digitization of region-specific data, suitable data can be provided to users. Some or all of the above processing in the Digitalization Department can be achieved through AI, or it can be done without AI. For example, the Digitalization Department can input users' geographic location information into generative AI, which will then prioritize the digitization of region-specific data.

[0124] The Digital Transformation Department can analyze users' social media activity, digitizing data that reflects relevant trends. For example, it can digitize data on which cards users discuss most frequently on social media; it can also digitize data on the cards used by opinion leaders followed by users; and it can digitize data on which users participate in popular cards within communities. Thus, by digitizing data reflecting social media trends, the department can provide users with the latest data. Some or all of the above processing in the Digital Transformation Department can be achieved using AI, or it can be done without AI. For example, the Digital Transformation Department can input users' social media activity data into generative AI, which will then digitize trend-reflecting data.

[0125] The digitization department can consider the user's health status and prioritize the digitization of optimal data. For example, when a user is fatigued, important data is prioritized; when a user is healthy, detailed data is prioritized; and when a user is unwell, key data is prioritized. Therefore, by prioritizing data digitization based on the user's health status, suitable data can be provided. Some or all of the above processing in the digitization department can be achieved through AI, or it can be done without AI. For example, the digitization department can input the user's health status data into a generative AI, which will then prioritize the digitization of health-based data.

[0126] The system involved in this embodiment is not limited to the above examples. For example, various modifications can be made as follows.

[0127] The proposal department can infer users' emotions and adjust the type of decks proposed based on these inferences. For example, when a user is stressed, the department might recommend a defensive deck to help them relax; when the user is excited, it might recommend a deck with more offensive cards; and when the user is calm, it might recommend a balanced deck. Thus, by recommending decks based on user emotions, more suitable decks can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text-generated AI (such as LLM) or multimodal generative AI, but is not limited to these. Some or all of the above processing in the proposal department can be achieved through AI, or AI can be omitted. For example, the proposal department can input users' emotional data into generative AI, which will then generate emotion-based deck suggestions.

[0128] The proposal department can analyze data from a user's past opponents and suggest decks effective against specific opponents. For example, it analyzes the deck composition of past opponents and suggests decks containing cards effective against them. If a user struggles against a particular opponent, the proposal department can also suggest decks that break that opponent's strategy. The department can also refer to decks from opponents the user has defeated and suggest decks employing similar strategies. Therefore, by suggesting decks effective against specific opponents, the win rate can be improved. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input data from the user's past opponents into a generative AI, which will then generate deck suggestions effective against specific opponents.

[0129] The proposal department can adjust deck difficulty based on the user's current in-game level or score. For example, it might recommend decks with more basic cards for beginners, more strategic cards for intermediate players, and complex combos for advanced players. Thus, by recommending decks based on user level or score, decks of appropriate difficulty can be provided. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's in-game level or score into a generative AI, which will then adjust the deck difficulty.

[0130] The proposal department can infer users' emotions and adjust the card order in the proposed deck based on these inferences. For example, when a user is tense, the department might recommend a deck with more defensive cards in the early game; when the user is relaxed, it might recommend a deck with more offensive cards; and when the user is excited, it might recommend a deck with more powerful cards. Thus, by recommending card order based on user emotions, more effective decks can be provided. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the proposal department can be achieved through AI, or AI can be omitted. For example, the proposal department can input users' emotional data into generative AI, which can then adjust the card order based on the emotion.

[0131] The proposal department can consider a user's geographical location information and suggest decks that reflect region-specific strategies. For example, if a user lives in a cold region, the proposal department will recommend a deck with more Ice cards; if a user lives in a tropical region, it may recommend a deck with more Fire cards; and if a user lives in an urban area, it may recommend a deck with more Machine cards. Thus, by suggesting decks that reflect region-specific strategies, suitable decks can be provided to users. Some or all of the above processing in the proposal department can be done using AI, or it can be done without AI. For example, the proposal department can input the user's geographical location information into a generative AI, which will then generate deck suggestions that reflect region-specific strategies.

[0132] The proposal department can analyze users' social media activity and suggest decks that reflect relevant trends. For example, it might recommend decks containing cards that users frequently discuss on social media; it can also refer to decks used by opinion leaders followed by users and offer corresponding suggestions; and it can recommend decks containing popular cards in the user's community. Thus, by suggesting decks that reflect social media trends, the department can provide users with the latest deck options. Some or all of the above processing in the proposal department can be achieved using AI, or it can be done without AI. For example, the proposal department can input users' social media activity data into generative AI, which will then generate trend-reflecting deck suggestions.

[0133] The battle department can infer the user's emotions and adjust the difficulty of the battle accordingly. For example, when the user is tense, the opponent's strength is set to low; when the user is relaxed, the opponent's strength is set to medium; and when the user is excited, the opponent's strength is set to high. Thus, by providing battles with appropriate difficulty based on the user's emotions, a suitable battle environment can be provided. Emotion inference can be achieved, for example, through emotion inference functions such as emotion engines or generative AI. Generative AI can be text generation AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the battle department can be achieved through AI, or AI can be omitted. For example, the battle department can input the user's emotional data into the generative AI, which will then adjust the battle difficulty based on the emotion.

[0134] The battle department can reference users' past battle history to enhance countermeasures against specific strategies. For example, the battle department can set up AI with enhanced countermeasures for strategies that users have struggled with; it can also set up AI that adopts similar strategies for strategies that users have won; and it can analyze the strategies that users have used to set up AI with enhanced countermeasures. Thus, by referring to past battle history, countermeasures against specific strategies can be strengthened. Some or all of the above processing in the battle department can be achieved through AI, or it can be done without AI. For example, the battle department can input users' past battle history data into generative AI, which will then execute enhanced countermeasures against specific strategies.

[0135] The battle department can dynamically change the AI's strategy based on the user's playstyle. For example, when the user adopts an aggressive playstyle, the AI ​​adopts a defensive strategy; when the user adopts a defensive playstyle, the AI ​​can also adopt an aggressive strategy; and when the user adopts a balanced playstyle, the AI ​​can flexibly change its strategy. Therefore, by providing corresponding strategies based on the user's playstyle, more effective battles can be achieved. Some or all of the above processing in the battle department can be done by AI, or it can be done without AI. For example, the battle department can input the user's playstyle data into a generative AI, which will then dynamically change the AI's strategy.

[0136] The battle department can infer users' emotions and adjust the pace of battles accordingly. For example, it can slow down the pace when users are tense, maintain a normal pace when users are relaxed, and speed up the pace when users are excited. Thus, by providing battles with an appropriate pace based on user emotions, a suitable battle environment can be provided. Emotion inference can be achieved through emotion inference functions such as emotion engines or generative AI. Generative AI can be text-generating AI (such as LLM) or multimodal generation AI, but is not limited to these. Some or all of the above processing in the battle department can be achieved through AI, or AI can be omitted. For example, the battle department can input users' emotional data into generative AI, which can then adjust the battle pace based on emotions.

[0137] The following is a brief description of the processing flow of Implementation Method 2.

[0138] Step 1: The proposal department proposes the optimal deck based on the user's personality, preferred playstyle, past deck information, and match history. For example, the proposal department uses AI to analyze the user's past deck usage and match results to propose decks that match the user's playstyle. For users who prefer an aggressive playstyle, decks with more high-attack cards are recommended; for users who prefer a defensive playstyle, decks with more high-defense cards are recommended; and for users who prefer a balanced playstyle, decks with both offensive and defensive capabilities are recommended.

[0139] Step 2: The Battle Department uses the decks proposed by the Proposal Department to conduct virtual battles against the AI. For example, users can practice against the AI ​​in an environment close to real battles. The Battle Department uses different strategies to control the opponent through the AI, and users can learn how to counter these strategies. When the AI ​​adopts an aggressive strategy, users can try a defensive strategy; when the AI ​​adopts a defensive strategy, users can try an aggressive strategy. In addition, when the AI ​​adopts a balanced strategy, users can try a variety of strategies.

[0140] Step 3: The Analysis Department performs visual analysis of the battle results conducted by the Battle Department. For example, the Analysis Department has the function of visually analyzing the AI's actions against opponents on a virtual battlefield. Users can intuitively see the AI's actions against the opponent and understand its strategy. The AI ​​can visualize the timing and order of specific card usage, allowing users to learn the strategy. In addition, the AI ​​can also visualize the effects of the strategies adopted in the battle. For example, the AI ​​can display the effects of the cards used through charts or heatmaps.

[0141] Step 4: Based on the results obtained by the Analysis Department, the Improvement Department proposes suggestions for improving the game style. For example, AI analyzes the user's mistakes and areas for improvement during battles, providing specific suggestions. This can include suggesting the timing of specific card usage or strategy changes to enhance the user's game style. Suggestions may include improving the timing of specific card usage, changing strategies, and trying new strategies.

[0142] Step 5: The digitization department digitizes the usage information of the physical card decks. For example, sensors collect information on the actual physical card decks used by users, and AI analyzes the data. Users can check the deck performance in real time and adjust the deck as needed. The types and frequency of cards used by users can be digitized, and AI evaluates the deck performance based on this data.

[0143] The specific processing unit 290 sends 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 voice representing the user's input to the result of the specific processing. The control unit 46A sends the voice data representing the user's 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 voice data.

[0144] Data generation model 58 is what is known as generative AI (Artificial Intelligence). An example of data generation model 58 includes ChatGPT (registered trademark) (Internet search).<URL:https: / / openai.com / blog / chatgpt> Generative AI, such as data generation model 58, is obtained by deep learning through a neural network. The data generation model 58 is input with a prompt containing instructions, and with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data of still images or data of moving images). The data generation model 58 infers the input inference data according to the instructions shown in the prompt, and outputs the inference result in one or more data forms such as speech 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 above-described specific processing while using the data generation model 58. The data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, the data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes multiple data generation models 58, including AI other than generative AI. AI other than generative AI includes, but is not limited to, 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. Furthermore, AI can also act as an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to this example. Moreover, processing performed by AI, including generative AI, can be replaced by rule-based processing, and vice versa.

[0145] Furthermore, the processing performed by the aforementioned data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Additionally, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or external devices, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or external devices.

[0146] Each of the aforementioned elements—the proposal department, battle department, analysis department, improvement department, and digitization department—can be implemented, for example, in at least one of the smart device 14 and the data processing device 12. For instance, the proposal department, implemented by the control unit 46A of the smart device 14, proposes the optimal deck based on the user's personality and playstyle. The battle department, for example, is implemented by the specific processing unit 290 of the data processing device 12, conducting virtual battles against AI. The analysis department, for example, is implemented by the control unit 46A of the smart device 14, visually analyzing the battle results. The improvement department, for example, is implemented by the specific processing unit 290 of the data processing device 12, proposing suggestions for improving the playstyle. The digitization department, for example, is implemented by the control unit 46A of the smart device 14, digitizing the usage information of the physical deck. The correspondence between the various departments and the device or control unit is not limited to the above examples and can be varied.

[0147] [Second Implementation]

[0148] Figure 3 An example of the configuration of the data processing system 210 according to the second embodiment is shown.

[0149] like Figure 3 As shown, the data processing system 210 includes a data processing device 12 and smart glasses 214. One example of the data processing device 12 is a server.

[0150] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0151] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0152] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0154] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0155] Figure 4 An example of the main functions of the data processing device 12 and the smart glasses 214 is shown. Figure 4 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0157] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0158] In the smart glasses 214, specific processing is performed by the processor 46. A specific processing program 60 is stored in the memory 50. The processor 46 reads the specific processing program 60 from the memory 50 and executes the read specific processing program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific processing program 60 executed on the RAM 48. Furthermore, the smart glasses 214 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0159] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 sends 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 voice input representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. 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. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, 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 are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0162] The data processing system 210 of the second embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or external devices, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or external devices.

[0163] Each of the aforementioned elements—the proposal department, battle department, analysis department, improvement department, and digitization department—can be implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For instance, the proposal department, implemented by the control unit 46A of the smart glasses 214, proposes the optimal deck based on the user's personality and playstyle. The battle department, for example, is implemented by the specific processing unit 290 of the data processing device 12, conducting virtual battles against AI. The analysis department, for example, is implemented by the control unit 46A of the smart glasses 214, visually analyzing the battle results. The improvement department, for example, is implemented by the specific processing unit 290 of the data processing device 12, proposing suggestions for improving the playstyle. The digitization department, for example, is implemented by the control unit 46A of the smart glasses 214, digitizing the usage information of the physical deck. The correspondence between each department and the device or control unit is not limited to the above examples and can be varied.

[0164] [Third Implementation Method]

[0165] Figure 5 An example of the configuration of the data processing system 310 according to the third embodiment is shown.

[0166] like Figure 5 As shown, the data processing system 310 includes a data processing device 12 and a head-mounted terminal 314. An example of the data processing device 12 is a server.

[0167] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0168] The head-mounted terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0169] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, used to capture the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0171] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0172] Figure 6 An example of the main functions of the data processing device 12 and the head-mounted terminal 314 is shown. Figure 6 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0174] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0175] In the head-mounted terminal 314, specific processing is performed by the processor 46. A specific program 60 is stored in the memory 50. The processor 46 reads the specific program 60 from the memory 50 and executes the read specific program 60 on the RAM 48. Specific processing is implemented by the processor 46 operating as a control unit 46A based on the specific program 60 executed on the RAM 48. Furthermore, the head-mounted terminal 314 may also have the same data generation model and emotion-specific model as the data generation model 58 and the emotion-specific model 59, and use these models to perform the same processing as the specific processing unit 290.

[0176] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 sends the result of the specific processing to the head-mounted terminal 314. In the head-mounted 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 voice representing the user's input to the specific processing result. The control unit 46A sends the voice data representing the user's input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0178] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. 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. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, 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 are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0179] The data processing system 310 of the third embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the head-mounted terminal 314, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the head-mounted terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the head-mounted terminal 314 or external devices, and the head-mounted terminal 314 acquires or collects information required for processing from the data processing device 12 or external devices.

[0180] Each of the aforementioned elements—the proposal department, battle department, analysis department, improvement department, and digitization department—can be implemented, for example, in at least one of the head-mounted terminal 314 and the data processing device 12. For instance, the proposal department, implemented by the control unit 46A of the head-mounted terminal 314, proposes the optimal deck based on the user's personality and playstyle. The battle department, for example, is implemented by the specific processing unit 290 of the data processing device 12, conducting virtual battles against AI. The analysis department, for example, is implemented by the control unit 46A of the head-mounted terminal 314, visually analyzing the battle results. The improvement department, for example, is implemented by the specific processing unit 290 of the data processing device 12, proposing suggestions for improving the playstyle. The digitization department, for example, is implemented by the control unit 46A of the head-mounted terminal 314, digitizing the usage information of the physical deck. The correspondence between each department and the device or control unit is not limited to the above examples and can be varied.

[0181] [Fourth Implementation Method]

[0182] Figure 7 An example of the configuration of the data processing system 410 according to the fourth embodiment is shown.

[0183] like Figure 7 As shown, 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.

[0184] 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, RAM 30, and memory 32. The processor 28, RAM 30, and memory 32 are connected to a bus 34. Furthermore, the database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. An example of the network 54 includes a WAN and / or LAN, etc.

[0185] Robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control object 443. Computer 36 includes a processor 46, RAM 48, and memory 50. The processor 46, RAM 48, and memory 50 are connected to a bus 52. Furthermore, the microphone 238, speaker 240, camera 42, and control object 443 are also connected to the bus 52.

[0186] Microphone 238 receives user commands by receiving the user's voice. Microphone 238 captures the user's voice, converts the captured sound into speech data, and outputs it to processor 46. Speaker 240 outputs sound according to commands from processor 46.

[0187] The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, used to photograph the user's surroundings (e.g., the shooting range defined by an angle of view equivalent to the field of vision of an average healthy person).

[0188] Communication I / F 44 is connected to network 54. Communication I / F 44 and 26 are responsible for the transmission and reception of various information between processor 46 and processor 28 via network 54. The transmission and reception of various information between processor 46 and processor 28 using communication I / F 44 and 26 is performed in a secure state.

[0189] The controlled object 443 includes a display device, LEDs for the eyes, and motors for driving the arms, hands, and feet. The posture and movements of the robot 414 are controlled by controlling the motors for the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, facial expressions of the robot 414 can also be expressed by controlling the illumination state of the LEDs for the robot 414's eyes.

[0190] Figure 8 An example of the main functions of the data processing device 12 and the robot 414 is shown. For example... Figure 8 As shown, in the data processing device 12, specific processing is performed by the processor 28. The memory 32 stores a specific processing program 56.

[0191] The processor 28 reads a specific processing program 56 from the memory 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is implemented by the processor 28 operating as a specific processing unit 290 based on the specific processing program 56 executed on the RAM 30.

[0192] The memory 32 stores a data generation model 58 and an emotion-specific model 59. The data generation model 58 and the emotion-specific model 59 are used by the specific processing unit 290. The specific processing unit 290 can use the emotion-specific model 59 to infer the user's emotions and perform specific processing based on the user's emotions. The emotion inference function using the emotion-specific model 59 (emotion-specific function) includes inferring and predicting the user's emotions, performing various inferences and predictions related to the user's emotions, but is not limited to this example. Furthermore, emotion inference and prediction also include, for example, emotion analysis (analysis).

[0193] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in memory 50. Processor 46 reads the specific program 60 from memory 50 and executes the read specific program 60 on RAM 48. Specific processing is achieved by processor 46 acting as control unit 46A based on the specific program 60 executed on RAM 48. Furthermore, robot 414 may also have the same data generation model and emotion-specific model as data generation model 58 and emotion-specific model 59, and use these models to perform the same processing as specific processing unit 290.

[0194] 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 the processing results (prediction results, etc.) using the data generation model 58 by communicating with the server device that has the data generation model 58. In addition, the data processing device 12 may be a server device or a user-owned terminal device (e.g., a mobile phone, robot, home appliance, etc.).

[0195] The specific processing unit 290 sends 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 voice representing the user's input regarding the result of the specific processing. The control unit 46A sends the voice data representing the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0196] Data generation model 58 is a so-called generative AI. An example of data generation model 58 includes generative AIs such as ChatGPT. Data generation model 58 is obtained by performing deep learning on a neural network. A prompt containing instructions is input to data generation model 58, along with inference data such as speech data representing speech, text data representing text, and image data representing images (e.g., data from still images or moving images). Data generation model 58 infers the inference data based on the instructions shown in the prompt and outputs the inference result in one or more data forms such as speech data, text data, and image data. 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. Specific processing unit 290 performs the aforementioned specific processing while using data generation model 58. Data generation model 58 can also be a fine-tuned model to output inference results from prompts without instructions; in this case, data generation model 58 can output inference results from prompts without instructions. The data processing device 12, etc., includes various data generation models 58, which include AI other than generative AI. These AIs include, 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 are capable of various processing methods, but are not limited to these examples. Furthermore, AI can also be an AI agent. Additionally, when the processing described above is performed by AI, this processing may be partially or entirely performed by AI, but is not limited to these examples. Moreover, processing performed by AI including generative AI can be replaced by rule-based processing, and vice versa.

[0197] The data processing system 410 of the fourth embodiment performs the same processing as the data processing system 10 of the first embodiment. The processing performed by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it can also be executed jointly by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or external devices, and the robot 414 acquires or collects information required for processing from the data processing device 12 or external devices.

[0198] Each of the aforementioned elements—the proposal department, battle department, analysis department, improvement department, and digitization department—can be implemented, for example, in at least one of the robot 414 and the data processing device 12. For instance, the proposal department, implemented by the control unit 46A of the robot 414, proposes the optimal deck based on the user's personality and play style. The battle department, for example, is implemented by the specific processing unit 290 of the data processing device 12, conducting virtual battles against AI. The analysis department, for example, is implemented by the control unit 46A of the robot 414, visually analyzing the battle results. The improvement department, for example, is implemented by the specific processing unit 290 of the data processing device 12, proposing suggestions for improving the play style. The digitization department, for example, is implemented by the control unit 46A of the robot 414, digitizing the usage information of the physical deck. The correspondence between the various departments and the device or control unit is not limited to the above examples and can be varied.

[0199] Furthermore, the emotion-specific model 59, serving as an emotion engine, can determine the user's emotion based on a specific mapping. Specifically, the emotion-specific model 59 can determine the user's emotion based on an emotion graph that serves as a specific mapping (see...). Figure 9 The robot's emotions can be determined by the emotion-specific model 59. In addition, the emotion-specific model 59 can also determine the robot's emotions in the same way, and the specific processing unit 290 can also perform specific processing using the robot's emotions.

[0200] Figure 9 This is a diagram representing an emotion map 400 that maps various emotions. In the emotion map 400, emotions are arranged radially from the center in concentric circles. The closer to the center of the concentric circles, the more primitive the emotion is. Further out on the concentric circles, emotions are arranged representing states or actions arising from mood. Emotion is a concept that includes both feelings and mental states. To the left of the concentric circles, emotions generated by reactions occurring in the brain are arranged roughly. To the right of the concentric circles, emotions guided by situational judgments are arranged roughly. Above and below the concentric circles, emotions generated by reactions occurring in the brain and guided by situational judgments are arranged roughly. Furthermore, the emotion of "pleasure" is arranged above the concentric circles, and the emotion of "unpleasantness" is arranged below. Thus, in the emotion map 400, various emotions are mapped according to the structure of emotion generation, while easily generated emotions are mapped nearby.

[0201] These emotions are distributed at the 3 o'clock position on the Emotion Chart 400, and usually fluctuate between peace and unease. In the right half of the Emotion Chart 400, because situational awareness is more dominant than internal feelings, it gives a sense of calm.

[0202] The inner side of the emotion diagram 400 represents the mind, and the outer side of the emotion diagram 400 represents actions. Therefore, the further you go to the outer side of the emotion diagram 400, the more the emotion can be seen (manifested in actions).

[0203] Here, human emotions are based on a balance of various factors such as posture and blood sugar levels. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. In robots, cars, and motorcycles, emotions can also be created based on a balance of factors such as posture and remaining battery power. When these balances deviate from the ideal, it indicates unhappiness; when they approach the ideal, it indicates pleasure. Emotion maps can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Speech Emotion Recognition and Brain Physiological Signal Analysis Systems for Emotion, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). In the left half of the emotion map, emotions belonging to the "Reaction" domain, where sensation is dominant, are arranged. Furthermore, in the right half of the emotion map, emotions belonging to the "Situation" domain, where situational cognition is dominant, are arranged.

[0204] The emotion map defines two types of emotions that promote learning. One is a negative emotion located near the middle of "repentance" or "reflection" on the situation side. That is, when the robot experiences negative emotions such as "I never want to feel this way again" or "I never want to be scolded again." The other is a positive emotion located near "desire" on the response side. That is, when the robot experiences positive feelings such as "wanting more" or "wanting to know more."

[0205] The emotion-specific model 59 feeds user input into a pre-learned neural network to obtain emotion values ​​representing each emotion shown in the emotion graph 400, and determines the user's emotion. This neural network is pre-learned based on multiple learning data sets that combine user input with emotion values ​​representing each emotion shown in the emotion graph 400. Furthermore, this neural network is learned to... Figure 10 As shown in sentiment graph 900, sentiment values ​​in nearby configurations are similar to each other. Figure 10 Examples show that multiple emotions such as "peace of mind", "stability", and "reassurance" have similar emotional values.

[0206] In the above embodiments, a specific processing is described by a single computer 22, but the technology disclosed herein is not limited to this, and distributed processing by multiple computers, including computer 22, is also possible.

[0207] In the above embodiments, an example of storing a specific processing program 56 in memory 32 is illustrated, but the technology disclosed herein is not limited thereto. For example, the specific processing program 56 may also be stored in a portable computer-readable non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 performs specific processing according to the specific processing program 56.

[0208] Alternatively, the specific processing program 56 can be stored in a storage device such as a server connected to the data processing device 12 via a network 54, and the specific processing program 56 can be downloaded and installed into the computer 22 upon request from the data processing device 12.

[0209] Furthermore, it is not necessary to store the entire specific process 56 in a storage device such as a server connected to the data processing device 12 via the network 54, nor is it necessary to store the entire specific process 56 in the memory 32; a portion of the specific process 56 may also be stored.

[0210] As a hardware resource for performing specific processing, various processors can be used. For example, a CPU is a general-purpose processor that functions as a hardware resource for performing specific processing by executing software, i.e., programs. Additionally, a dedicated circuit can be listed as a processor; it is a processor with a circuit structure specifically designed for performing specific processing, such as a FPGA (Field-Programmable Gate Array), PLD (Programmable Logic Device), or ASIC (Application Specific Integrated Circuit). Every processor has built-in or connected memory, and every processor executes specific processing by using that memory.

[0211] The hardware resources for performing a specific process can consist of one of these various processors, or a combination of two or more processors of the same or different types (e.g., a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Furthermore, the hardware resources for performing a specific process can also be a single processor.

[0212] As an example of a single processor, the first type consists of a combination of one or more CPUs and software, which functions as a hardware resource to perform specific processing. The second type uses a processor, such as a System-on-a-chip (SoC), which implements the entire system functionality, including multiple hardware resources for performing specific processing, using a single IC chip. In this case, the specific processing is implemented using one or more of the aforementioned processors that serve as hardware resources.

[0213] Furthermore, as the hardware architecture of these various processors, more specifically, circuits combining semiconductor elements and other circuit components can be used. Moreover, the specific process described above is merely an example. Therefore, it goes without saying that, without departing from the main point, unnecessary steps can be removed, new steps can be added, or the processing order can be changed.

[0214] Furthermore, although the above examples have been described in terms of first to fourth embodiments, some or all of these embodiments can be combined. Additionally, the smart device 14, smart glasses 214, head-mounted terminal 314, and robot 414 are just examples and can be combined separately, or other devices may be used. Furthermore, although the above examples have been described in terms of morphological example 1 and morphological example 2, these can also be combined.

[0215] The foregoing descriptions and illustrations are detailed explanations of the parts covered by this disclosure and are merely one example of this disclosure. For instance, the descriptions of the above-described structure, function, role, and effect are just one example of the structure, function, role, and effect of the parts covered by this disclosure. Therefore, it goes without saying that, without departing from the spirit of this disclosure, unnecessary parts can be deleted, new elements can be added, or replacements can be made to the foregoing descriptions and illustrations. Furthermore, to avoid confusion and facilitate understanding of the parts covered by this disclosure, explanations of technical common sense that does not require special explanation for implementing this disclosure have been omitted from the foregoing descriptions and illustrations.

[0216] All documents, patent applications and technical standards described in this specification are incorporated herein by reference as if they were specifically and individually described by reference.

[0217] [Postscript 1]

[0218] A system, characterized in that it comprises:

[0219] The proposal department is responsible for proposing the optimal card deck based on the user's personality, preferred style, past deck information, and battle history.

[0220] The battle division is used to engage in virtual battles against AI using the decks proposed by the proposal division.

[0221] The analysis unit is used to perform visual analysis of the battle results conducted by the battle unit.

[0222] The improvement department is used to propose suggestions for improving the game style based on the results obtained by the analysis department.

[0223] The Digitalization Department is responsible for digitizing the usage information of physical card sets.

[0224] [Postscript 2]

[0225] The system as described in Appendix 1 is characterized in that,

[0226] The proposal department is used to infer the user's emotions and adjust the type of the proposed deck based on the inferred user emotions.

[0227] [Postscript 3]

[0228] The system as described in Appendix 1 is characterized in that,

[0229] The proposal department is used to analyze data on the user's past opponents and propose effective decks for specific opponents.

[0230] [Postscript 4]

[0231] The system as described in Appendix 1 is characterized in that,

[0232] The proposal department is used to adjust the difficulty of the deck based on the user's current in-game level or score.

[0233] [Postscript 5]

[0234] The system as described in Appendix 1 is characterized in that,

[0235] The proposal department is used to infer the user's emotions and adjust the order of cards in the proposed deck based on the inferred user emotions.

[0236] [Postscript 6]

[0237] The system as described in Appendix 1 is characterized in that,

[0238] The proposal department is used to consider the user's geographical location information and propose card decks that reflect regionally specific strategies.

[0239] [Postscript 7]

[0240] The system as described in Appendix 1 is characterized in that,

[0241] The proposal department is used to analyze users' social media activities and propose decks that reflect relevant trends.

[0242] [Postscript 8]

[0243] The system as described in Appendix 1 is characterized in that,

[0244] The battle unit is used to infer the user's emotions and adjust the difficulty of the battle based on the inferred user emotions.

[0245] [Postscript 9]

[0246] The system as described in Appendix 1 is characterized in that,

[0247] The battle section is used to refer to the user's past battle history and strengthen countermeasures against specific strategies.

[0248] [Postscript 10]

[0249] The system as described in Appendix 1 is characterized in that,

[0250] The battle unit dynamically changes the AI's strategy based on the user's gaming style.

[0251] [Postscript 11]

[0252] The system as described in Appendix 1 is characterized in that,

[0253] The battle unit is used to infer the user's emotions and adjust the rhythm of the battle based on the inferred user emotions.

[0254] [Postscript 12]

[0255] The system as described in Appendix 1 is characterized in that,

[0256] The battle unit is used to take into account the user's geographical location information and conduct battles that reflect region-specific strategies.

[0257] [Postscript 13]

[0258] The system as described in Appendix 1 is characterized in that,

[0259] The battle unit is used to analyze users' social media activities and conduct battles that reflect relevant trends.

[0260] [Postscript 14]

[0261] The system as described in Appendix 1 is characterized in that,

[0262] The analysis unit is used to infer the user's emotions and adjust the display method of the analysis results according to the inferred user emotions.

[0263] [Postscript 15]

[0264] The system as described in Appendix 1 is characterized in that,

[0265] The analysis unit is used to analyze the effectiveness of specific strategies in detail by referring to the user's past battle data.

[0266] [Postscript 16]

[0267] The system as described in Appendix 1 is characterized in that,

[0268] The analysis department applies different analysis algorithms based on the user's gaming style.

[0269] [Postscript 17]

[0270] The system as described in Appendix 1 is characterized in that,

[0271] The analysis unit is used to infer the user's emotions and determine the priority of the analysis results based on the inferred user emotions.

[0272] [Postscript 18]

[0273] The system as described in Appendix 1 is characterized in that,

[0274] The analysis unit is used to take into account the user's geographical location information and perform analysis that reflects region-specific strategies.

[0275] [Postscript 19]

[0276] The system as described in Appendix 1 is characterized in that,

[0277] The analysis unit is used to analyze users' social media activities and perform analysis that reflects relevant trends.

[0278] [Postscript 20]

[0279] The system as described in Appendix 1 is characterized in that,

[0280] The improvement unit is used to infer the user's emotions and adjust the content of the improvement suggestions based on the inferred user emotions.

[0281] [Postscript 21]

[0282] The system as described in Appendix 1 is characterized in that,

[0283] The improvement department is used to propose specific improvement measures targeting particular weaknesses by referring to the user's past battle data.

[0284] [Postscript 22]

[0285] The system as described in Appendix 1 is characterized in that,

[0286] The improvement department applies different improvement algorithms based on the user's gaming style.

[0287] [Postscript 23]

[0288] The system as described in Appendix 1 is characterized in that,

[0289] The improvement unit is used to infer the user's emotions and determine the priority of improvement suggestions based on the inferred user emotions.

[0290] [Postscript 24]

[0291] The system as described in Appendix 1 is characterized in that,

[0292] The improvement unit is used to take into account the user's geographical location information and propose improvement measures that reflect region-specific strategies.

[0293] [Postscript 25]

[0294] The system as described in Appendix 1 is characterized in that,

[0295] The improvement department is used to analyze users' social media activities and propose improvement measures that reflect relevant trends.

[0296] [Postscript 26]

[0297] The system as described in Appendix 1 is characterized in that,

[0298] The digitization unit is used to infer the user's emotions and adjust the data type of the digitization based on the inferred user emotions.

[0299] [Postscript 27]

[0300] The system as described in Appendix 1 is characterized in that,

[0301] The digitization department is used to refer to the user's past deck usage data and record the frequency of use of specific cards in detail.

[0302] [Postscript 28]

[0303] The system as described in Appendix 1 is characterized in that,

[0304] The digitization department applies different digitization algorithms based on the user's gaming style.

[0305] [Postscript 29]

[0306] The system as described in Appendix 1 is characterized in that,

[0307] The digitization unit is used to infer the user's emotions and determine the priority of digitized data based on the inferred user emotions.

[0308] [Postscript 30]

[0309] The system as described in Appendix 1 is characterized in that,

[0310] The digitization department is used to take into account the user's geographic location information and prioritize the digitization of region-specific data.

[0311] [Postscript 31]

[0312] The system as described in Appendix 1 is characterized in that,

[0313] The digitization department is used to analyze users' social media activities and digitize data that reflects relevant trends.

[0314] [Postscript 32]

[0315] The system as described in Appendix 1 is characterized in that,

[0316] The digitization department is used to prioritize the digitization of optimal data, taking into account the user's health status.

Claims

1. A system, characterized in that, include: The proposal department is responsible for proposing the optimal card deck based on the user's personality, preferred style, past deck information, and battle history. The battle division is used to engage in virtual battles against AI using the decks proposed by the proposal division. The analysis unit is used to perform visual analysis of the battle results conducted by the battle unit. The improvement department is used to propose suggestions for improving the game style based on the results obtained by the analysis department. The Digitalization Department is responsible for digitizing the usage information of physical card sets.

2. The system as described in claim 1, characterized in that, The proposal department is used to infer the user's emotions and adjust the type of the proposed deck based on the inferred user emotions.

3. The system as described in claim 1, characterized in that, The proposal department is used to analyze data on the user's past opponents and propose effective decks for specific opponents.

4. The system as described in claim 1, characterized in that, The proposal department is used to adjust the difficulty of the deck based on the user's current in-game level or score.

5. The system as described in claim 1, characterized in that, The proposal department is used to infer the user's emotions and adjust the order of cards in the proposed deck based on the inferred user emotions.

6. The system as described in claim 1, characterized in that, The proposal department is used to consider the user's geographical location information and propose card decks that reflect regionally specific strategies.

7. The system as described in claim 1, characterized in that, The proposal department is used to analyze users' social media activities and propose decks that reflect relevant trends.

8. The system as described in claim 1, characterized in that, The battle unit is used to infer the user's emotions and adjust the difficulty of the battle based on the inferred user emotions.

Images