system

Abstract

The system according to this embodiment aims to dynamically adjust the difficulty and progression of the game in response to the player's actions and emotions. [Solution] The system according to the embodiment comprises a first sensing unit, a generation unit, a providing unit, a second sensing unit, and an adjustment unit. The first sensing unit senses the player's actions and choices. The generation unit generates dialogue and responses based on the information sensed by the first sensing unit. The providing unit provides the dialogue and responses generated by the generation unit to the player. The second sensing unit senses the player's emotions. The adjustment unit adjusts the difficulty and progression of the game based on the information sensed by the second sensing unit.

Description

Technical Field

[0006]

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

Background Art

[0007] The system according to this embodiment can dynamically adjust the difficulty and progression of the game in response to the player's actions and emotions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​assistant system for in-game dialogue characters according to an embodiment of the present invention is a system that exhibits different dialogues and responses depending on the player's actions and choices. This system senses the player's actions and choices and generates corresponding dialogues and responses. Next, it provides hints for the progression of the in-game story and quests. Furthermore, it senses the player's emotions and level of excitement and dynamically changes the difficulty and development of the game. For example, it senses actions such as the player selecting a specific item or moving to a specific location. This information is input to the AI ​​assistant. Next, the AI ​​assistant generates dialogues and responses according to the player's actions and choices. For example, if the player selects a specific item, it generates dialogue that provides information and hints about that item. Also, if the player moves to a specific location, it generates dialogue that provides information about that location and hints about the next action. Furthermore, the AI ​​assistant provides hints for the progression of the in-game story and quests. For example, if the player is progressing through a specific quest, it generates dialogue that provides hints about that quest and information about the next step. This allows the player to progress through the game smoothly. In addition, the AI ​​assistant senses the player's emotions and level of excitement and dynamically changes the difficulty and development of the game. For example, if a player is excited, the game's difficulty can be increased or the pace of the game can be sped up to maintain that excitement. Conversely, if a player is calm, the game's difficulty can be lowered or the pace can be slowed down to encourage relaxation. This mechanism allows players to receive dialogue and responses that correspond to their actions and choices, and to obtain hints for game progression and quests. Furthermore, because the game's difficulty and pace change according to the player's emotions and level of excitement, players can always have a fresh experience. In this way, the in-game AI assistant character system can provide dialogue and responses that correspond to the player's actions and choices, and adjust the game's difficulty and pace based on emotions and level of excitement, thereby providing players with a fresh experience.

[0029] The in-game dialogue character AI assistant system according to this embodiment comprises a first sensing unit, a generation unit, a providing unit, a second sensing unit, and an adjustment unit. The first sensing unit senses the player's actions and choices. Player actions and choices include, but are not limited to, in-game movement, item use, and dialogue choices. The first sensing unit senses the player's actions in real time, for example, using a sensor. The first sensing unit can also record the player's choices and store them in a database. The generation unit generates dialogue and responses based on the information sensed by the first sensing unit. The generation unit generates dialogue using, for example, a text generation AI (e.g., LLM). The generation unit can also generate voice dialogue using a voice generation AI. Furthermore, the generation unit can also generate visual responses using an animation generation AI. The providing unit provides the dialogue and responses generated by the generation unit to the player. The providing unit displays text dialogue using, for example, a display. The providing unit can also play voice dialogue using a speaker. Furthermore, the providing unit can also display animations using a display. The second sensing unit senses the player's emotions. For example, the second sensing unit analyzes the player's facial expressions using facial recognition technology to sense their emotions. The second sensing unit can also analyze the tone and speed of the player's voice using voice analysis technology to sense their emotions. Furthermore, the second sensing unit can measure the player's heart rate and skin electrical activity using biosensors to sense their emotions. The adjustment unit adjusts the difficulty and progression of the game based on the information sensed by the second sensing unit. For example, the adjustment unit increases the difficulty of the game if the player is excited. The adjustment unit can also decrease the difficulty of the game if the player is relaxed. Furthermore, the adjustment unit can slow down the progression of the game if the player is stressed. As a result, the in-game dialogue character AI assistant system according to this embodiment can provide the player with a fresh experience by offering dialogue and responses in response to the player's actions and choices, and by adjusting the difficulty and progression of the game based on their emotions and level of excitement.

[0030] The first sensing unit senses the player's actions and choices. These actions and choices include, but are not limited to, movement within the game, use of items, and dialogue options. The first sensing unit senses the player's actions in real time, for example, using sensors. Specifically, it analyzes signals from input devices such as game controllers, keyboards, and mice to accurately understand the player's actions. The first sensing unit can also record the player's choices and store them in a database. This provides foundational data for referencing the player's past behavior history and generating more personalized dialogue and responses. Furthermore, the first sensing unit has the ability to learn and predict the player's behavior patterns. For example, by analyzing what choices the player tends to make in certain situations and predicting their next actions, it enables more natural dialogue. This allows the first sensing unit to understand the player's actions and choices in detail, improving the overall accuracy and responsiveness of the system.

[0031] The generation unit generates dialogue and responses based on information sensed by the first sensing unit. For example, the generation unit uses text generation AI (e.g., LLM) to generate dialogue. Specifically, it utilizes natural language processing techniques to understand the context and generate appropriate responses in order to produce dialogue content that corresponds to the player's actions and choices. The generation unit can also generate voice dialogue using voice generation AI. The voice generation AI converts text data into voice data, generating speech with natural pronunciation and intonation. Furthermore, the generation unit can generate visual responses using animation generation AI. The animation generation AI generates character expressions and movements in real time, making the interaction with the player more immersive. This allows the generation unit to combine text, voice, and animation elements to provide the player with a diverse and rich dialogue experience. Additionally, the generation unit can consider the player's past behavior history and preferences to generate more personalized dialogue. This allows the generation unit to provide dialogue and responses that meet the player's expectations, improving the game experience.

[0032] The delivery unit provides the player with the dialogue and responses generated by the generation unit. For example, the delivery unit displays text dialogue using a display. Specifically, it displays a dialogue window on the game screen, allowing the player to easily check the content of the dialogue. The delivery unit can also play voice dialogue using a speaker. Voice dialogue is played as a character's voice to enhance the player's immersion. Furthermore, the delivery unit can display animation using a display. Animation displays the character's facial expressions and movements in real time, enhancing the realism of the dialogue. In this way, the delivery unit can provide the player with a visually and aurally rich dialogue experience. In addition, the delivery unit can receive player feedback in real time and adjust the dialogue content and responses as needed. For example, if the player reacts to a particular dialogue, the next dialogue content can be adjusted based on that reaction, resulting in a more natural dialogue. In this way, the delivery unit can provide the player with a consistent, high-quality dialogue experience, improving the appeal of the game.

[0033] The second sensing unit senses the player's emotions. For example, it analyzes the player's facial expressions using facial recognition technology to sense their emotions. Specifically, it uses a camera to capture the player's face and a facial recognition algorithm to identify emotions such as smiles, anger, and surprise. The second sensing unit can also sense emotions by analyzing the tone and speed of the player's voice using voice analysis technology. Voice analysis technology analyzes the pitch, intensity, and rhythm of the player's voice to detect changes in emotion. Furthermore, the second sensing unit can also sense emotions by measuring the player's heart rate and skin electrical activity using biosensors. These biosensors monitor the player's physical reactions in real time and measure the degree of stress and excitement. This allows the second sensing unit to comprehensively understand the player's emotional state and improve the overall system responsiveness. Moreover, by accumulating emotional data and learning the player's emotional patterns, the second sensing unit achieves more accurate emotion recognition. This allows the second sensing unit to provide appropriate dialogue and responses tailored to the player's emotions, improving the gaming experience.

[0034] The adjustment unit adjusts the game's difficulty and progression based on information sensed by the second sensing unit. For example, if the player is excited, the adjustment unit increases the game's difficulty. Specifically, it might increase the strength of enemy characters or increase the complexity of puzzles. Conversely, if the player is relaxed, the adjustment unit can decrease the game's difficulty. For example, it might reduce the number of enemy characters or increase the number of puzzle hints. Furthermore, if the player is stressed, the adjustment unit can slow down the game's progression. For example, it might extend the time limit or reduce the number of tense scenes. In this way, the adjustment unit can flexibly adjust the game's difficulty and progression according to the player's emotional state, providing the optimal gaming experience for the player. Moreover, the adjustment unit can also adjust the long-term game balance based on the player's past emotional data. For example, if the player frequently experiences stress in a particular scene, adjusting the difficulty of that scene can improve the player's satisfaction. In this way, the adjustment unit can flexibly adjust the game according to the player's emotions, maximizing the game's appeal.

[0035] The generation unit can generate dialogues and responses in response to the player's actions and choices. For example, if the player selects a specific item, the generation unit can generate dialogue that provides information and hints about that item. It can also generate dialogues that provide information about a specific location and hints about the next action if the player moves to that location. Furthermore, if the player is progressing through a specific quest, the generation unit can generate dialogues that provide hints about that quest and information about the next steps. This improves player interaction by generating dialogues and responses that respond to the player's actions and choices. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate dialogues and responses using a generative AI model that takes the player's actions and choices as input and outputs dialogues and responses.

[0036] The service provider can provide hints for story progression and quests within the game. For example, if the player is progressing through a particular quest, the service provider can generate dialogue that provides hints about that quest and information about the next steps. The service provider can also generate dialogue that provides information about a location and hints about the next action if the player moves to a specific location. Furthermore, if the player selects a specific item, the service provider can generate dialogue that provides information about that item and hints about it. This makes the game progress smoother for the player by providing hints for story progression and quests within the game. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can generate dialogues and responses using a generative AI model that takes the player's actions and choices as input and outputs hints for story progression and quests.

[0037] The sensing unit can sense the player's emotions. For example, the sensing unit analyzes the player's facial expressions using facial recognition technology to sense emotions. For example, the sensing unit calculates an emotion score based on changes in facial expressions. The sensing unit can also analyze the tone and speed of the player's voice using voice analysis technology to sense emotions. For example, the sensing unit analyzes the tone and speed of the voice to calculate an emotion score. The sensing unit can also measure the player's heart rate and skin electrical activity using biosensors to sense emotions. For example, the sensing unit calculates an emotion score based on fluctuations in heart rate. This allows the game's difficulty and progression to be appropriately adjusted by sensing the player's emotions and level of excitement. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input image data of the player captured by the camera into a generating AI, which can then perform the estimation of the player's emotions.

[0038] The adjustment unit can adjust the game's difficulty and progression based on the player's emotions. For example, if the player is excited, the adjustment unit can increase the game's difficulty. Conversely, if the player is relaxed, the adjustment unit can decrease the game's difficulty. Furthermore, if the player is stressed, the adjustment unit can slow down the game's progression. In this way, by adjusting the game's difficulty and progression based on the player's emotions and level of excitement, the adjustment unit provides the player with the optimal gaming experience. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can adjust the game's difficulty and progression using an AI model that takes player emotion data as input and outputs the game's difficulty and progression.

[0039] The sensing unit can analyze the player's past behavior history and select the optimal sensing method. For example, the sensing unit prioritizes sensing actions that the player has frequently performed in the past. The sensing unit can also analyze the player's past behavior patterns and suggest the optimal sensing method. Furthermore, the sensing unit can adjust the sensing method based on the player's past successful actions. This allows the system to select the optimal sensing method by analyzing the player's past behavior history and provide the player with useful information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the player's past behavior data into a generating AI and have the generating AI execute the optimal sensing method.

[0040] The sensing unit can filter information based on the player's current game progress and areas of interest upon detection. For example, the sensing unit prioritizes detecting information related to the quest the player is currently in progress on. The sensing unit can also detect relevant items and locations based on the player's areas of interest. Furthermore, the sensing unit can detect information necessary for the next step depending on the player's current progress. This allows the system to provide highly relevant information by filtering based on the player's current game progress and areas of interest. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input data on the player's game progress and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The sensing unit can prioritize detecting highly relevant actions and choices by considering the player's geographical location information when sensing a player. For example, if the player is in a specific location, the sensing unit will prioritize detecting information related to that location. The sensing unit can also detect nearby quests and items based on the player's current location. Furthermore, the sensing unit can detect optimal actions and choices based on the player's location information. This allows the system to prioritize providing highly relevant information by considering the player's geographical location information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the player's geographical location information into a generating AI and have the generating AI execute highly relevant actions and choices.

[0042] The sensing unit can analyze the player's social media activity and detect relevant actions and choices when sensing activity. For example, the sensing unit can detect relevant actions and choices based on information the player shares on social media. The sensing unit can also detect quests and items of interest from the player's social media activity. Furthermore, the sensing unit can detect relevant information based on the actions of the player's social media friends. In this way, by analyzing the player's social media activity, it can detect relevant actions and choices and provide the player with useful information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the player's social media activity data into a generating AI and have the generating AI execute relevant actions and choices.

[0043] The generation unit can adjust the level of detail in dialogues and responses based on the importance of the player's actions during generation. For example, the generation unit generates detailed dialogues and responses for important actions. It can also generate concise dialogues and responses for general actions. Furthermore, the generation unit can adjust the level of detail in dialogues and responses according to the importance of the player's actions. This allows the system to provide the player with optimal information by adjusting the level of detail in dialogues and responses based on the importance of the player's actions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI perform the level of detail in dialogues and responses based on the importance of the actions.

[0044] The generation unit can apply different generation algorithms depending on the category of the player's action during generation. For example, the generation unit can apply a combat-specific generation algorithm to combat actions. It can also apply an exploration-specific generation algorithm to exploration actions. Furthermore, it can apply a quest-specific generation algorithm to quest actions. By applying different generation algorithms depending on the category of the player's action, the generation unit can provide the player with the most optimal dialogue and responses. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI execute a generation algorithm corresponding to the category of the action.

[0045] The generation unit can determine the priority of dialogues and responses based on the timing of the player's actions during generation. For example, the generation unit can prioritize generating dialogues and responses for the player's most recent actions. It can also generate dialogues and responses at the appropriate time for the player's past actions. Furthermore, the generation unit can adjust the priority of dialogues and responses based on the timing of the player's actions. This allows information to be provided to the player at the optimal time by determining the priority of dialogues and responses based on the timing of the player's actions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI prioritize dialogues and responses based on the timing of the actions.

[0046] The generation unit can adjust the order of dialogues and responses based on the relevance of the player's actions during generation. For example, if the player's actions are related, the generation unit will generate a sequence of dialogues and responses. It can also generate individual dialogues and responses if the player's actions are unrelated. Furthermore, the generation unit can adjust the order of dialogues and responses based on the relevance of the player's actions. This allows the generation unit to provide the player with optimal information by adjusting the order of dialogues and responses based on the relevance of the player's actions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI execute the order of dialogues and responses based on the relevance of the actions.

[0047] The service provider can select the optimal display method by referring to the player's past dialogue history when providing the service. For example, the service provider may prioritize using display methods that the player has preferred in the past. The service provider can also analyze the player's past dialogue history and suggest the optimal display method. Furthermore, the service provider can adjust the display method based on display methods that the player has used in the past. This allows the service provider to provide the optimal display method by referring to the player's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's dialogue history data into a generating AI and have the generating AI execute the optimal display method.

[0048] The service provider can customize the content of dialogues and responses based on the player's current game progress at the time of delivery. For example, the service provider can provide dialogues and responses related to the quest the player is currently in. The service provider can also provide dialogues and responses related to the next step, depending on the player's current progress. Furthermore, the service provider can customize the content of dialogues and responses based on the player's progress. This allows the service provider to provide the player with the most relevant information by customizing the content of dialogues and responses based on the player's current game progress. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's game progress data into a generating AI and have the generating AI execute the customized dialogues and responses.

[0049] The service provider can provide optimal dialogue and responses by considering the player's geographical location information at the time of delivery. For example, if the player is in a specific location, the service provider can provide dialogue and responses related to that location. The service provider can also provide information about nearby quests and items based on the player's current location. Furthermore, the service provider can provide optimal dialogue and responses based on the player's location information. In this way, by considering the player's geographical location information, optimal dialogue and responses can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's geographical location information into a generating AI and have the generating AI execute the optimal dialogue and responses.

[0050] The service provider can analyze the player's social media activity and adjust the content of conversations and responses at the time of delivery. For example, the service provider can provide relevant conversations and responses based on information the player has shared on social media. The service provider can also provide information about quests and items of interest based on the player's social media activity. Furthermore, the service provider can provide relevant conversations and responses based on the actions of the player's social media friends. In this way, relevant conversations and responses can be provided by analyzing the player's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the player's social media activity data into a generating AI and have the generating AI execute the content of conversations and responses.

[0051] The adjustment unit can analyze the player's past gameplay history to select the optimal adjustment method during the adjustment process. For example, the adjustment unit can adjust the game's difficulty based on difficulty settings that the player has previously succeeded with. The adjustment unit can also analyze the player's past gameplay history and suggest the optimal progression. Furthermore, the adjustment unit can change the adjustment method to avoid difficulty settings that the player has previously failed with. In this way, by analyzing the player's past gameplay history, the optimal adjustment method can be selected, providing the player with the best possible gaming experience. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the player's gameplay history data into a generating AI and have the generating AI execute the optimal adjustment method.

[0052] The adjustment unit can adjust the difficulty and progression of the game based on the player's current game progress. For example, the adjustment unit can adjust the difficulty of the quest the player is currently in. The adjustment unit can also adjust the progression of the game according to the player's current progress. Furthermore, the adjustment unit can adjust the difficulty of the next step based on the player's progress. This allows the adjustment unit to provide the player with the best possible game experience by adjusting the difficulty and progression based on the player's current game progress. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the player's game progress data into a generating AI and have the generating AI perform the difficulty and progression adjustments.

[0053] The adjustment unit can adjust the difficulty and progression to the optimal level, taking into account the player's geographical location during the adjustment process. For example, if the player is in a specific location, the adjustment unit will adjust the difficulty and progression to be relevant to that location. The adjustment unit can also provide the optimal difficulty and progression based on the player's current location. Furthermore, the adjustment unit can adjust the difficulty and progression based on the player's location information. This allows the adjustment unit to provide the optimal difficulty and progression by considering the player's geographical location. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the player's geographical location information into a generating AI and have the generating AI execute the optimal difficulty and progression.

[0054] The adjustment unit can analyze the player's social media activity during adjustments to adjust the difficulty and progression of the game. For example, the adjustment unit can adjust the difficulty and progression based on information shared by the player on social media. The adjustment unit can also provide information about quests and items of interest based on the player's social media activity. Furthermore, the adjustment unit can adjust the difficulty and progression based on the actions of the player's social media friends. In this way, by analyzing the player's social media activity, it can provide relevant difficulty and progression. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the player's social media activity data into a generating AI and have the generating AI perform the adjustment of difficulty and progression.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The in-game AI assistant system for dialogue characters can learn the player's preferences and play style based on their actions and choices, providing individually customized dialogue and responses. For example, if the player prefers combat, it can generate dialogue offering detailed strategies and hints about combat. If the player prefers exploration, it can generate dialogue providing information about exploration and hints about hidden items. Furthermore, if the player has a story-driven play style, it can generate dialogue providing detailed background information about the story progression and character relationships. This allows for a more personalized gaming experience by providing dialogue and responses tailored to the player's preferences and play style.

[0057] The generation unit can estimate the player's skill level based on their actions and choices, and generate dialogue and responses of appropriate difficulty. For example, if the player is a beginner, it can generate dialogue that provides basic controls and simple hints. If the player is an intermediate player, it can generate dialogue about more advanced strategies and techniques. Furthermore, if the player is an advanced player, it can generate dialogue about challenging quests and high-difficulty missions. In this way, by providing dialogue and responses that match the player's skill level, it can provide a game experience of appropriate difficulty.

[0058] The offering team can suggest side quests and mini-games that will interest the player based on their actions and choices. For example, if a player frequently explores a particular area, the team can suggest a side quest related to that area. Similarly, if a player enjoys collecting specific items, the team can suggest a mini-game related to those items. Furthermore, if a player enjoys interacting with a particular character, the team can suggest a story or event related to that character. This expands the ways in which players can enjoy the game by suggesting engaging side quests and mini-games.

[0059] The sensing unit can monitor the player's health status and provide appropriate advice based on the player's actions and choices. For example, if a player is playing the game for an extended period, it can advise them to take a break. It can also warn the player if they are repeating certain actions, as these actions may have adverse effects on their health. Furthermore, it can provide hints and advice to help the player maintain healthy lifestyle habits. In this way, by monitoring the player's health status and providing appropriate advice, healthy gameplay can be promoted.

[0060] The sensing unit can estimate the player's learning style based on their actions and choices, and provide appropriate learning content. For example, if the player prefers visual information, visual learning content can be provided. If the player prefers auditory information, audio learning content can be provided. Furthermore, if the player prefers hands-on learning, interactive learning content can be provided. In this way, by providing learning content tailored to the player's learning style, effective learning can be supported.

[0061] The sensing unit can provide a reward system to motivate players based on their actions and choices. For example, it can offer rewards when players achieve specific goals. It can also offer bonuses for consecutive logins. Furthermore, it can offer special rewards when players cooperate with other players to complete quests. By providing a reward system that increases player motivation, it can encourage continued gameplay.

[0062] The service provider can offer a customizable interface tailored to the player's preferences based on their actions and choices. For example, if a player prefers a particular color or theme, the service provider can offer an interface based on that color or theme. Similarly, if a player prefers a specific layout, the service provider can offer an interface based on that layout. Furthermore, if a player frequently uses a particular function, the service provider can offer an interface that prioritizes displaying that function. This allows for a more comfortable gaming experience by providing a customizable interface tailored to the player's preferences.

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

[0064] Step 1: The first sensing unit detects the player's actions and choices. These include movement within the game, use of items, and dialogue options. The first sensing unit can use sensors to detect the player's actions in real time and record choices, which can then be stored in a database. Step 2: The generation unit generates dialogue and responses based on the information sensed by the first sensing unit. The generation unit can generate dialogue using text generation AI (e.g., LLM), generate voice dialogue using speech generation AI, and generate visual responses using animation generation AI. Step 3: The provider unit provides the player with the dialogue and responses generated by the generator unit. The provider unit can display text dialogue using a display, play voice dialogue using a speaker, and display animations using a display. Step 4: The second sensing unit senses the player's emotions. The second sensing unit can sense emotions by analyzing the player's facial expressions using facial recognition technology, analyzing the tone and speed of the player's voice using voice analysis technology, and measuring the player's heart rate and skin electrical activity using biosensors. Step 5: The adjustment unit adjusts the game difficulty and pace based on the information sensed by the second sensing unit. The adjustment unit can increase the game difficulty if the player is excited, decrease the game difficulty if they are relaxed, and slow down the pace of the game if they are stressed.

[0065] (Example of form 2) The AI ​​assistant system for in-game dialogue characters according to an embodiment of the present invention is a system that exhibits different dialogues and responses depending on the player's actions and choices. This system senses the player's actions and choices and generates corresponding dialogues and responses. Next, it provides hints for the progression of the in-game story and quests. Furthermore, it senses the player's emotions and level of excitement and dynamically changes the difficulty and development of the game. For example, it senses actions such as the player selecting a specific item or moving to a specific location. This information is input to the AI ​​assistant. Next, the AI ​​assistant generates dialogues and responses according to the player's actions and choices. For example, if the player selects a specific item, it generates dialogue that provides information and hints about that item. Also, if the player moves to a specific location, it generates dialogue that provides information about that location and hints about the next action. Furthermore, the AI ​​assistant provides hints for the progression of the in-game story and quests. For example, if the player is progressing through a specific quest, it generates dialogue that provides hints about that quest and information about the next step. This allows the player to progress through the game smoothly. In addition, the AI ​​assistant senses the player's emotions and level of excitement and dynamically changes the difficulty and development of the game. For example, if a player is excited, the game's difficulty can be increased or the pace of the game can be sped up to maintain that excitement. Conversely, if a player is calm, the game's difficulty can be lowered or the pace can be slowed down to encourage relaxation. This mechanism allows players to receive dialogue and responses that correspond to their actions and choices, and to obtain hints for game progression and quests. Furthermore, because the game's difficulty and pace change according to the player's emotions and level of excitement, players can always have a fresh experience. In this way, the in-game AI assistant character system can provide dialogue and responses that correspond to the player's actions and choices, and adjust the game's difficulty and pace based on emotions and level of excitement, thereby providing players with a fresh experience.

[0066] The in-game dialogue character AI assistant system according to this embodiment comprises a first sensing unit, a generation unit, a providing unit, a second sensing unit, and an adjustment unit. The first sensing unit senses the player's actions and choices. Player actions and choices include, but are not limited to, in-game movement, item use, and dialogue choices. The first sensing unit senses the player's actions in real time, for example, using a sensor. The first sensing unit can also record the player's choices and store them in a database. The generation unit generates dialogue and responses based on the information sensed by the first sensing unit. The generation unit generates dialogue using, for example, a text generation AI (e.g., LLM). The generation unit can also generate voice dialogue using a voice generation AI. Furthermore, the generation unit can also generate visual responses using an animation generation AI. The providing unit provides the dialogue and responses generated by the generation unit to the player. The providing unit displays text dialogue using, for example, a display. The providing unit can also play voice dialogue using a speaker. Furthermore, the providing unit can also display animations using a display. The second sensing unit senses the player's emotions. For example, the second sensing unit analyzes the player's facial expressions using facial recognition technology to sense their emotions. The second sensing unit can also analyze the tone and speed of the player's voice using voice analysis technology to sense their emotions. Furthermore, the second sensing unit can measure the player's heart rate and skin electrical activity using biosensors to sense their emotions. The adjustment unit adjusts the difficulty and progression of the game based on the information sensed by the second sensing unit. For example, the adjustment unit increases the difficulty of the game if the player is excited. The adjustment unit can also decrease the difficulty of the game if the player is relaxed. Furthermore, the adjustment unit can slow down the progression of the game if the player is stressed. As a result, the in-game dialogue character AI assistant system according to this embodiment can provide the player with a fresh experience by offering dialogue and responses in response to the player's actions and choices, and by adjusting the difficulty and progression of the game based on their emotions and level of excitement.

[0067] The first sensing unit senses the player's actions and choices. These actions and choices include, but are not limited to, movement within the game, use of items, and dialogue options. The first sensing unit senses the player's actions in real time, for example, using sensors. Specifically, it analyzes signals from input devices such as game controllers, keyboards, and mice to accurately understand the player's actions. The first sensing unit can also record the player's choices and store them in a database. This provides foundational data for referencing the player's past behavior history and generating more personalized dialogue and responses. Furthermore, the first sensing unit has the ability to learn and predict the player's behavior patterns. For example, by analyzing what choices the player tends to make in certain situations and predicting their next actions, it enables more natural dialogue. This allows the first sensing unit to understand the player's actions and choices in detail, improving the overall accuracy and responsiveness of the system.

[0068] The generation unit generates dialogue and responses based on information sensed by the first sensing unit. For example, the generation unit uses text generation AI (e.g., LLM) to generate dialogue. Specifically, it utilizes natural language processing techniques to understand the context and generate appropriate responses in order to produce dialogue content that corresponds to the player's actions and choices. The generation unit can also generate voice dialogue using voice generation AI. The voice generation AI converts text data into voice data, generating speech with natural pronunciation and intonation. Furthermore, the generation unit can generate visual responses using animation generation AI. The animation generation AI generates character expressions and movements in real time, making the interaction with the player more immersive. This allows the generation unit to combine text, voice, and animation elements to provide the player with a diverse and rich dialogue experience. Additionally, the generation unit can consider the player's past behavior history and preferences to generate more personalized dialogue. This allows the generation unit to provide dialogue and responses that meet the player's expectations, improving the game experience.

[0069] The delivery unit provides the player with the dialogue and responses generated by the generation unit. For example, the delivery unit displays text dialogue using a display. Specifically, it displays a dialogue window on the game screen, allowing the player to easily check the content of the dialogue. The delivery unit can also play voice dialogue using a speaker. Voice dialogue is played as a character's voice to enhance the player's immersion. Furthermore, the delivery unit can display animation using a display. Animation displays the character's facial expressions and movements in real time, enhancing the realism of the dialogue. In this way, the delivery unit can provide the player with a visually and aurally rich dialogue experience. In addition, the delivery unit can receive player feedback in real time and adjust the dialogue content and responses as needed. For example, if the player reacts to a particular dialogue, the next dialogue content can be adjusted based on that reaction, resulting in a more natural dialogue. In this way, the delivery unit can provide the player with a consistent, high-quality dialogue experience, improving the appeal of the game.

[0070] The second sensing unit senses the player's emotions. For example, it analyzes the player's facial expressions using facial recognition technology to sense their emotions. Specifically, it uses a camera to capture the player's face and a facial recognition algorithm to identify emotions such as smiles, anger, and surprise. The second sensing unit can also sense emotions by analyzing the tone and speed of the player's voice using voice analysis technology. Voice analysis technology analyzes the pitch, intensity, and rhythm of the player's voice to detect changes in emotion. Furthermore, the second sensing unit can also sense emotions by measuring the player's heart rate and skin electrical activity using biosensors. These biosensors monitor the player's physical reactions in real time and measure the degree of stress and excitement. This allows the second sensing unit to comprehensively understand the player's emotional state and improve the overall system responsiveness. Moreover, by accumulating emotional data and learning the player's emotional patterns, the second sensing unit achieves more accurate emotion recognition. This allows the second sensing unit to provide appropriate dialogue and responses tailored to the player's emotions, improving the gaming experience.

[0071] The adjustment unit adjusts the game's difficulty and progression based on information sensed by the second sensing unit. For example, if the player is excited, the adjustment unit increases the game's difficulty. Specifically, it might increase the strength of enemy characters or increase the complexity of puzzles. Conversely, if the player is relaxed, the adjustment unit can decrease the game's difficulty. For example, it might reduce the number of enemy characters or increase the number of puzzle hints. Furthermore, if the player is stressed, the adjustment unit can slow down the game's progression. For example, it might extend the time limit or reduce the number of tense scenes. In this way, the adjustment unit can flexibly adjust the game's difficulty and progression according to the player's emotional state, providing the optimal gaming experience for the player. Moreover, the adjustment unit can also adjust the long-term game balance based on the player's past emotional data. For example, if the player frequently experiences stress in a particular scene, adjusting the difficulty of that scene can improve the player's satisfaction. In this way, the adjustment unit can flexibly adjust the game according to the player's emotions, maximizing the game's appeal.

[0072] The generation unit can generate dialogues and responses in response to the player's actions and choices. For example, if the player selects a specific item, the generation unit can generate dialogue that provides information and hints about that item. It can also generate dialogues that provide information about a specific location and hints about the next action if the player moves to that location. Furthermore, if the player is progressing through a specific quest, the generation unit can generate dialogues that provide hints about that quest and information about the next steps. This improves player interaction by generating dialogues and responses that respond to the player's actions and choices. Some or all of the above processing in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate dialogues and responses using a generative AI model that takes the player's actions and choices as input and outputs dialogues and responses.

[0073] The service provider can provide hints for story progression and quests within the game. For example, if the player is progressing through a particular quest, the service provider can generate dialogue that provides hints about that quest and information about the next steps. The service provider can also generate dialogue that provides information about a location and hints about the next action if the player moves to a specific location. Furthermore, if the player selects a specific item, the service provider can generate dialogue that provides information about that item and hints about it. This makes the game progress smoother for the player by providing hints for story progression and quests within the game. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can generate dialogues and responses using a generative AI model that takes the player's actions and choices as input and outputs hints for story progression and quests.

[0074] The sensing unit can sense the player's emotions. For example, the sensing unit analyzes the player's facial expressions using facial recognition technology to sense emotions. For example, the sensing unit calculates an emotion score based on changes in facial expressions. The sensing unit can also analyze the tone and speed of the player's voice using voice analysis technology to sense emotions. For example, the sensing unit analyzes the tone and speed of the voice to calculate an emotion score. The sensing unit can also measure the player's heart rate and skin electrical activity using biosensors to sense emotions. For example, the sensing unit calculates an emotion score based on fluctuations in heart rate. This allows the game's difficulty and progression to be appropriately adjusted by sensing the player's emotions and level of excitement. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input image data of the player captured by the camera into a generating AI, which can then perform the estimation of the player's emotions.

[0075] The adjustment unit can adjust the game's difficulty and progression based on the player's emotions. For example, if the player is excited, the adjustment unit can increase the game's difficulty. Conversely, if the player is relaxed, the adjustment unit can decrease the game's difficulty. Furthermore, if the player is stressed, the adjustment unit can slow down the game's progression. In this way, by adjusting the game's difficulty and progression based on the player's emotions and level of excitement, the adjustment unit provides the player with the optimal gaming experience. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can adjust the game's difficulty and progression using an AI model that takes player emotion data as input and outputs the game's difficulty and progression.

[0076] The sensing unit can estimate the player's emotions and determine the priority of actions and choices to sense based on the estimated emotions. For example, if the player is excited, the sensing unit will prioritize sensing important quests or items. It can also prioritize exploration and collection activities if the player is relaxed. Furthermore, if the player is stressed, the sensing unit can prioritize simple tasks or relaxing activities. This allows the system to provide the player with optimal information by prioritizing actions and choices based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the sensing unit may be performed using AI or not. For example, the sensing unit can input the player's emotion data into a generative AI and have the generative AI execute emotion-based priority actions and choices.

[0077] The sensing unit can analyze the player's past behavior history and select the optimal sensing method. For example, the sensing unit prioritizes sensing actions that the player has frequently performed in the past. The sensing unit can also analyze the player's past behavior patterns and suggest the optimal sensing method. Furthermore, the sensing unit can adjust the sensing method based on the player's past successful actions. This allows the system to select the optimal sensing method by analyzing the player's past behavior history and provide the player with useful information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the player's past behavior data into a generating AI and have the generating AI execute the optimal sensing method.

[0078] The sensing unit can filter information based on the player's current game progress and areas of interest upon detection. For example, the sensing unit prioritizes detecting information related to the quest the player is currently in progress on. The sensing unit can also detect relevant items and locations based on the player's areas of interest. Furthermore, the sensing unit can detect information necessary for the next step depending on the player's current progress. This allows the system to provide highly relevant information by filtering based on the player's current game progress and areas of interest. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input data on the player's game progress and areas of interest into a generating AI and have the generating AI perform the filtering.

[0079] The sensing unit can estimate the player's emotions and adjust the timing of its actions and choices based on the estimated emotions. For example, if the player is excited, the sensing unit can immediately sense important information. It can also sense information slowly if the player is relaxed. Furthermore, if the player is stressed, the sensing unit can sense information at an appropriate time. This allows the system to provide information at the optimal time for the player by adjusting the timing of actions and choices based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the sensing unit may be performed using AI, or not. For example, the sensing unit can input the player's emotion data into a generative AI and have the generative AI execute emotion-based actions and choices at the appropriate times.

[0080] The sensing unit can prioritize detecting highly relevant actions and choices by considering the player's geographical location information when sensing a player. For example, if the player is in a specific location, the sensing unit will prioritize detecting information related to that location. The sensing unit can also detect nearby quests and items based on the player's current location. Furthermore, the sensing unit can detect optimal actions and choices based on the player's location information. This allows the system to prioritize providing highly relevant information by considering the player's geographical location information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the player's geographical location information into a generating AI and have the generating AI execute highly relevant actions and choices.

[0081] The sensing unit can analyze the player's social media activity and detect relevant actions and choices when sensing activity. For example, the sensing unit can detect relevant actions and choices based on information the player shares on social media. The sensing unit can also detect quests and items of interest from the player's social media activity. Furthermore, the sensing unit can detect relevant information based on the actions of the player's social media friends. In this way, by analyzing the player's social media activity, it can detect relevant actions and choices and provide the player with useful information. Some or all of the above processing in the sensing unit may be performed using AI, for example, or without AI. For example, the sensing unit can input the player's social media activity data into a generating AI and have the generating AI execute relevant actions and choices.

[0082] The generation unit can estimate the player's emotions and adjust the way dialogue and responses are expressed based on the estimated emotions. For example, if the player is excited, the generation unit will use an energetic expression. If the player is relaxed, the generation unit can also use a calm expression. Furthermore, if the player is stressed, the generation unit can use a soothing expression. This allows the system to provide the optimal interaction for the player by adjusting the way dialogue and responses are expressed based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input the player's emotion data into the generative AI and have the generative AI execute emotion-based dialogue and response expressions.

[0083] The generation unit can adjust the level of detail in dialogues and responses based on the importance of the player's actions during generation. For example, the generation unit generates detailed dialogues and responses for important actions. It can also generate concise dialogues and responses for general actions. Furthermore, the generation unit can adjust the level of detail in dialogues and responses according to the importance of the player's actions. This allows the system to provide the player with optimal information by adjusting the level of detail in dialogues and responses based on the importance of the player's actions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI perform the level of detail in dialogues and responses based on the importance of the actions.

[0084] The generation unit can apply different generation algorithms depending on the category of the player's action during generation. For example, the generation unit can apply a combat-specific generation algorithm to combat actions. It can also apply an exploration-specific generation algorithm to exploration actions. Furthermore, it can apply a quest-specific generation algorithm to quest actions. By applying different generation algorithms depending on the category of the player's action, the generation unit can provide the player with the most optimal dialogue and responses. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI execute a generation algorithm corresponding to the category of the action.

[0085] The generation unit can estimate the player's emotions and adjust the length of dialogue and responses based on the estimated emotions. For example, if the player is excited, the generation unit can generate short, concise dialogue. If the player is relaxed, the generation unit can also generate longer dialogue with detailed explanations. Furthermore, if the player is stressed, the generation unit can generate concise and easy-to-understand dialogue. By adjusting the length of dialogue and responses based on the player's emotions, the system can provide the player with the most relevant information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the player's emotion data into the generation AI and have the generation AI execute the length of dialogue and responses based on the emotion.

[0086] The generation unit can determine the priority of dialogues and responses based on the timing of the player's actions during generation. For example, the generation unit can prioritize generating dialogues and responses for the player's most recent actions. It can also generate dialogues and responses at the appropriate time for the player's past actions. Furthermore, the generation unit can adjust the priority of dialogues and responses based on the timing of the player's actions. This allows information to be provided to the player at the optimal time by determining the priority of dialogues and responses based on the timing of the player's actions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI prioritize dialogues and responses based on the timing of the actions.

[0087] The generation unit can adjust the order of dialogues and responses based on the relevance of the player's actions during generation. For example, if the player's actions are related, the generation unit will generate a sequence of dialogues and responses. It can also generate individual dialogues and responses if the player's actions are unrelated. Furthermore, the generation unit can adjust the order of dialogues and responses based on the relevance of the player's actions. This allows the generation unit to provide the player with optimal information by adjusting the order of dialogues and responses based on the relevance of the player's actions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input player action data into a generation AI and have the generation AI execute the order of dialogues and responses based on the relevance of the actions.

[0088] The service provider can estimate the player's emotions and adjust the way dialogue and responses are displayed based on the estimated emotions. For example, if the player is excited, the service provider may use a visually stimulating display method. If the player is relaxed, the service provider may use a calm display method. Furthermore, if the player is stressed, the service provider may use a simple and highly visible display method. This allows the service provider to provide the optimal display method for the player by adjusting the way dialogue and responses are displayed based on the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI execute an emotion-based display method.

[0089] The service provider can select the optimal display method by referring to the player's past dialogue history when providing the service. For example, the service provider may prioritize using display methods that the player has preferred in the past. The service provider can also analyze the player's past dialogue history and suggest the optimal display method. Furthermore, the service provider can adjust the display method based on display methods that the player has used in the past. This allows the service provider to provide the optimal display method by referring to the player's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's dialogue history data into a generating AI and have the generating AI execute the optimal display method.

[0090] The service provider can customize the content of dialogues and responses based on the player's current game progress at the time of delivery. For example, the service provider can provide dialogues and responses related to the quest the player is currently in. The service provider can also provide dialogues and responses related to the next step, depending on the player's current progress. Furthermore, the service provider can customize the content of dialogues and responses based on the player's progress. This allows the service provider to provide the player with the most relevant information by customizing the content of dialogues and responses based on the player's current game progress. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's game progress data into a generating AI and have the generating AI execute the customized dialogues and responses.

[0091] The service provider can estimate the player's emotions and determine the priority of the dialogues and responses to provide based on the estimated emotions. For example, if the player is excited, the service provider will prioritize providing important information. If the player is relaxed, the service provider can also provide detailed information. Furthermore, if the player is stressed, the service provider can provide concise and to-the-point information. This allows the service provider to provide the player with the most relevant information by prioritizing dialogues and responses based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the player's emotion data into a generative AI and have the generative AI prioritize dialogues and responses based on those emotions.

[0092] The service provider can provide optimal dialogue and responses by considering the player's geographical location information at the time of delivery. For example, if the player is in a specific location, the service provider can provide dialogue and responses related to that location. The service provider can also provide information about nearby quests and items based on the player's current location. Furthermore, the service provider can provide optimal dialogue and responses based on the player's location information. In this way, by considering the player's geographical location information, optimal dialogue and responses can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the player's geographical location information into a generating AI and have the generating AI execute the optimal dialogue and responses.

[0093] The service provider can analyze the player's social media activity and adjust the content of conversations and responses at the time of delivery. For example, the service provider can provide relevant conversations and responses based on information the player has shared on social media. The service provider can also provide information about quests and items of interest based on the player's social media activity. Furthermore, the service provider can provide relevant conversations and responses based on the actions of the player's social media friends. In this way, relevant conversations and responses can be provided by analyzing the player's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the player's social media activity data into a generating AI and have the generating AI execute the content of conversations and responses.

[0094] The adjustment unit can estimate the player's emotions and determine how to adjust the game's difficulty and progression based on the estimated emotions. For example, if the player is excited, the adjustment unit can increase the game's difficulty. Conversely, if the player is relaxed, the adjustment unit can also decrease the game's difficulty. Furthermore, if the player is stressed, the adjustment unit can slow down the game's progression. This allows the game to provide the player with an optimal gaming experience by determining how to adjust the game's difficulty and progression based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the player's emotion data into a generative AI and have the generative AI execute methods for adjusting the game's difficulty and progression based on those emotions.

[0095] The adjustment unit can analyze the player's past gameplay history to select the optimal adjustment method during the adjustment process. For example, the adjustment unit can adjust the game's difficulty based on difficulty settings that the player has previously succeeded with. The adjustment unit can also analyze the player's past gameplay history and suggest the optimal progression. Furthermore, the adjustment unit can change the adjustment method to avoid difficulty settings that the player has previously failed with. In this way, by analyzing the player's past gameplay history, the optimal adjustment method can be selected, providing the player with the best possible gaming experience. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the player's gameplay history data into a generating AI and have the generating AI execute the optimal adjustment method.

[0096] The adjustment unit can adjust the difficulty and progression of the game based on the player's current game progress. For example, the adjustment unit can adjust the difficulty of the quest the player is currently in. The adjustment unit can also adjust the progression of the game according to the player's current progress. Furthermore, the adjustment unit can adjust the difficulty of the next step based on the player's progress. This allows the adjustment unit to provide the player with the best possible game experience by adjusting the difficulty and progression based on the player's current game progress. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the player's game progress data into a generating AI and have the generating AI perform the difficulty and progression adjustments.

[0097] The adjustment unit can estimate the player's emotions and determine the difficulty level and priority of the game's progression based on those estimated emotions. For example, if the player is excited, the adjustment unit may prioritize providing high-difficulty quests. It can also prioritize providing low-difficulty quests if the player is relaxed. Furthermore, if the player is stressed, it may prioritize providing relaxing progression. This allows the system to provide the player with the optimal gaming experience by determining the difficulty level and priority of the game's progression based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can input the player's emotion data into a generative AI and have the generative AI prioritize the game's difficulty level and progression based on those emotions.

[0098] The adjustment unit can adjust the difficulty and progression to the optimal level, taking into account the player's geographical location during the adjustment process. For example, if the player is in a specific location, the adjustment unit will adjust the difficulty and progression to be relevant to that location. The adjustment unit can also provide the optimal difficulty and progression based on the player's current location. Furthermore, the adjustment unit can adjust the difficulty and progression based on the player's location information. This allows the adjustment unit to provide the optimal difficulty and progression by considering the player's geographical location. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the player's geographical location information into a generating AI and have the generating AI execute the optimal difficulty and progression.

[0099] The adjustment unit can analyze the player's social media activity during adjustments to adjust the difficulty and progression of the game. For example, the adjustment unit can adjust the difficulty and progression based on information shared by the player on social media. The adjustment unit can also provide information about quests and items of interest based on the player's social media activity. Furthermore, the adjustment unit can adjust the difficulty and progression based on the actions of the player's social media friends. In this way, by analyzing the player's social media activity, it can provide relevant difficulty and progression. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the player's social media activity data into a generating AI and have the generating AI perform the adjustment of difficulty and progression.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The in-game AI assistant system for dialogue characters can learn the player's preferences and play style based on their actions and choices, providing individually customized dialogue and responses. For example, if the player prefers combat, it can generate dialogue offering detailed strategies and hints about combat. If the player prefers exploration, it can generate dialogue providing information about exploration and hints about hidden items. Furthermore, if the player has a story-driven play style, it can generate dialogue providing detailed background information about the story progression and character relationships. This allows for a more personalized gaming experience by providing dialogue and responses tailored to the player's preferences and play style.

[0102] The generation unit can estimate the player's skill level based on their actions and choices, and generate dialogue and responses of appropriate difficulty. For example, if the player is a beginner, it can generate dialogue that provides basic controls and simple hints. If the player is an intermediate player, it can generate dialogue about more advanced strategies and techniques. Furthermore, if the player is an advanced player, it can generate dialogue about challenging quests and high-difficulty missions. In this way, by providing dialogue and responses that match the player's skill level, it can provide a game experience of appropriate difficulty.

[0103] The offering team can suggest side quests and mini-games that will interest the player based on their actions and choices. For example, if a player frequently explores a particular area, the team can suggest a side quest related to that area. Similarly, if a player enjoys collecting specific items, the team can suggest a mini-game related to those items. Furthermore, if a player enjoys interacting with a particular character, the team can suggest a story or event related to that character. This expands the ways in which players can enjoy the game by suggesting engaging side quests and mini-games.

[0104] The sensing unit can estimate the player's emotions and, based on the estimated emotions, provide a relaxation mode to reduce the player's stress level. For example, if the player is stressed, it can suggest a relaxation mode and display calming music or scenery. If the player is relaxed, it can maintain the relaxation mode and provide relaxing content. Furthermore, if the player is excited, it can temporarily deactivate the relaxation mode and provide content to maintain the excitement. In this way, by providing a relaxation mode based on the player's emotions, it is possible to reduce the player's stress level and provide a relaxed gaming experience.

[0105] The adjustment unit can estimate the player's emotions and adjust the in-game music and sound effects based on those estimates. For example, if the player is excited, it can play fast-paced music and intense sound effects. If the player is relaxed, it can play calm music and quiet sound effects. Furthermore, if the player is stressed, it can play relaxing music and sound effects. By adjusting the music and sound effects based on the player's emotions, it can provide the player with the optimal sound environment.

[0106] The sensing unit can monitor the player's health status and provide appropriate advice based on the player's actions and choices. For example, if a player is playing the game for an extended period, it can advise them to take a break. It can also warn the player if they are repeating certain actions, as these actions may have adverse effects on their health. Furthermore, it can provide hints and advice to help the player maintain healthy lifestyle habits. In this way, by monitoring the player's health status and providing appropriate advice, healthy gameplay can be promoted.

[0107] The sensing unit can estimate the player's learning style based on their actions and choices, and provide appropriate learning content. For example, if the player prefers visual information, visual learning content can be provided. If the player prefers auditory information, audio learning content can be provided. Furthermore, if the player prefers hands-on learning, interactive learning content can be provided. In this way, by providing learning content tailored to the player's learning style, effective learning can be supported.

[0108] The sensing unit can provide a reward system to motivate players based on their actions and choices. For example, it can offer rewards when players achieve specific goals. It can also offer bonuses for consecutive logins. Furthermore, it can offer special rewards when players cooperate with other players to complete quests. By providing a reward system that increases player motivation, it can encourage continued gameplay.

[0109] The sensing unit can estimate the player's emotions and, based on those emotions, provide social interactions to enhance the player's sociability. For example, if a player is feeling lonely, it can suggest matching with other players. If a player is excited, it can suggest cooperative or competitive play. Furthermore, if a player is relaxed, it can suggest chatting or interacting in forums. In this way, by providing social interactions based on the player's emotions, it can enhance the player's sociability.

[0110] The service provider can offer a customizable interface tailored to the player's preferences based on their actions and choices. For example, if a player prefers a particular color or theme, the service provider can offer an interface based on that color or theme. Similarly, if a player prefers a specific layout, the service provider can offer an interface based on that layout. Furthermore, if a player frequently uses a particular function, the service provider can offer an interface that prioritizes displaying that function. This allows for a more comfortable gaming experience by providing a customizable interface tailored to the player's preferences.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The first sensing unit detects the player's actions and choices. These include movement within the game, use of items, and dialogue options. The first sensing unit can use sensors to detect the player's actions in real time and record choices, which can then be stored in a database. Step 2: The generation unit generates dialogue and responses based on the information sensed by the first sensing unit. The generation unit can generate dialogue using text generation AI (e.g., LLM), generate voice dialogue using speech generation AI, and generate visual responses using animation generation AI. Step 3: The provider unit provides the player with the dialogue and responses generated by the generator unit. The provider unit can display text dialogue using a display, play voice dialogue using a speaker, and display animations using a display. Step 4: The second sensing unit senses the player's emotions. The second sensing unit can sense emotions by analyzing the player's facial expressions using facial recognition technology, analyzing the tone and speed of the player's voice using voice analysis technology, and measuring the player's heart rate and skin electrical activity using biosensors. Step 5: The adjustment unit adjusts the game difficulty and pace based on the information sensed by the second sensing unit. The adjustment unit can increase the game difficulty if the player is excited, decrease the game difficulty if they are relaxed, and slow down the pace of the game if they are stressed.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] For example, the first sensing unit can sense the player's actions and choices using the camera 42 and microphone 38B of the smart device 14. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates dialogue and responses using text generation AI and voice generation AI. The providing unit provides the generated dialogue and responses to the player using the display 40A and speaker 40B of the smart device 14. The second sensing unit can sense the player's emotions using the camera 42 and microphone 38B of the smart device 14. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty and progression of the game based on the player's emotions. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] For example, the first sensing unit can sense the player's actions and choices using the camera 42 and microphone 238 of the smart glasses 214. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates dialogue and responses using text generation AI and voice generation AI. The providing unit provides the generated dialogue and responses to the player using the display and speaker 240 of the smart glasses 214. The second sensing unit can sense the player's emotions using the camera 42 and microphone 238 of the smart glasses 214. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty and progression of the game based on the player's emotions. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] For example, the first sensing unit can sense the player's actions and choices using the camera 42 and microphone 238 of the headset terminal 314. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates dialogue and responses using text generation AI and voice generation AI. The providing unit provides the generated dialogue and responses to the player using the display 343 and speaker 240 of the headset terminal 314. The second sensing unit can sense the player's emotions using the camera 42 and microphone 238 of the headset terminal 314. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the game difficulty and progression based on the player's emotions. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] For example, the first sensing unit can sense the player's actions and choices using the camera 42 and microphone 238 of the robot 414. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates dialogue and responses using text generation AI and voice generation AI. The providing unit provides the generated dialogue and responses to the player using the display and speaker 240 of the robot 414. The second sensing unit can sense the player's emotions using the camera 42 and microphone 238 of the robot 414. The adjustment unit is implemented by the specific processing unit 290 of the data processing device 12 and adjusts the difficulty and progression of the game based on the player's emotions. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The first sensing unit detects the player's actions and choices, A generation unit that generates dialogue and responses based on information sensed by the first sensing unit, A providing unit that provides the player with the dialogue and responses generated by the generation unit, and a second sensing unit that senses the player's emotions, The game includes an adjustment unit that adjusts the difficulty and progression of the game based on the information detected by the second sensing unit. A system characterized by the following features. (Note 2) The system according to Appendix 1, characterized in that the generation unit generates dialogue and responses in response to the player's actions and choices. (Note 3) The system described in Appendix 1 is characterized in that the aforementioned provisioning unit provides hints for story progression and quests within the game. (Note 4) The system according to Appendix 1, characterized in that the sensing unit senses the player's emotions. (Note 5) The adjustment unit is characterized by adjusting the difficulty and progression of the game based on the player's emotions, as described in Appendix 1. (Note 6) The system according to Appendix 1, characterized in that the sensing unit estimates the player's emotions and determines the priority of actions and choices to sense based on the estimated player's emotions. (Note 7) The sensing unit is Analyze the player's past behavior history and select the optimal detection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The sensing unit is When detected, filtering is performed based on the player's current game progress and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The system according to Appendix 1, characterized in that the sensing unit estimates the player's emotions and adjusts the timing of sensing actions and choices based on the estimated player's emotions. (Note 10) The sensing unit is When sensing an action, the system prioritizes detecting highly relevant actions and choices by considering the player's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The sensing unit is Upon detection, the system analyzes the player's social media activity to identify related actions and choices. The system described in Appendix 1, characterized by the features described herein. (Note 12) The system according to Appendix 1, characterized in that the generation unit estimates the player's emotions and adjusts the method of expressing dialogue and reactions based on the estimated player's emotions. (Note 13) The generating unit is During generation, the level of detail in dialogue and responses is adjusted based on the importance of the player's actions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the category of the player's action. The system described in Appendix 1, characterized by the features described herein. (Note 15) The system according to Appendix 1, characterized in that the generation unit estimates the player's emotions and adjusts the length of dialogue and responses based on the estimated player's emotions. (Note 16) The generating unit is During generation, the priority of dialogue and responses is determined based on when the player's actions occur. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the order of dialogues and responses is adjusted based on the relevance of the player's actions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The system described in Appendix 1 is characterized in that the providing unit estimates the player's emotions and adjusts the method of displaying the dialogue and responses provided based on the estimated player's emotions. (Note 19) The aforementioned supply unit is, When providing the content, the system will refer to the player's past dialogue history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When delivered, the content of dialogue and responses will be customized based on the player's current game progress. The system described in Appendix 1, characterized by the features described herein. (Note 21) The system described in Appendix 1 is characterized in that the providing unit estimates the player's emotions and determines the priority of the dialogues and responses to be provided based on the estimated player's emotions. (Note 22) The aforementioned supply unit is, When providing the service, it takes the player's geographical location into account to provide optimal dialogue and responses. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, we analyze players' social media activity to adjust the content of conversations and responses. The system described in Appendix 1, characterized by the features described herein. (Note 24) The system according to Appendix 1, characterized in that the adjustment unit estimates the player's emotions and determines how to adjust the difficulty and progression of the game based on the estimated player's emotions. (Note 25) The adjustment unit is, During adjustments, the system analyzes the player's past gameplay history to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During adjustments, the difficulty and progression of the game are adjusted based on the player's current game progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is characterized by estimating the player's emotions and determining the difficulty level and priority of the game's progression based on the estimated player emotions, as described in Appendix 1. (Note 28) The adjustment unit is, During adjustments, the difficulty and progression are optimized by taking into account the player's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The adjustment unit is, During adjustments, the difficulty and progression of the game are adjusted based on an analysis of players' social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The first sensing unit detects the player's actions and choices, A generation unit that generates dialogue and responses based on information sensed by the first sensing unit, A providing unit that provides the player with the dialogue and responses generated by the generation unit, A second sensing unit that detects the player's emotions, The game includes an adjustment unit that adjusts the difficulty and progression of the game based on the information detected by the second sensing unit. This is a key feature of the system.

2. The generating unit is Generates dialogue and responses based on the player's actions and choices. The system according to feature 1.

3. The aforementioned supply unit is, Provides hints for progressing through the game's story and quests. The system according to feature 1.

4. The sensing unit is Sensing the player's emotions The system according to feature 1.

5. The adjustment unit is, The game's difficulty and progression are adjusted based on the player's emotions. The system according to feature 1.

6. The sensing unit is The system estimates the player's emotions and determines the priority of perceived actions and choices based on those estimated emotions. The system according to feature 1.

7. The sensing unit is Analyze the player's past behavior history and select the optimal detection method. The system according to feature 1.

8. The sensing unit is When detected, filtering is performed based on the player's current game progress and areas of interest. The system according to feature 1.

Images