System

Abstract

To provide a conversation which varies in a kinetic state according to a player's action and selection.SOLUTION: A system comprises: a reception part; a provision part; a conversation part; a change part; and a setting part. The reception part receives input from a player. The provision part provides background information of a character when the input received by the reception part is an inquiry to the character. The conversation part creates in a kinetic state, a conversation content with the character on the basis of the player's previous action and previous selection. The change part changes a feeling and an attitude of the character on the basis of a game situation and a relationship between the player and the character. The setting part sets a new quest or a new task on the basis of the conversation result with the player.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that dialogue with in-game characters is fixed and does not allow for dynamic dialogue that responds to the player's actions and choices.

[0005] The system according to the embodiment aims to provide dialogue that changes dynamically in response to the player's actions and choices. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a provision unit, a dialogue unit, a change unit, and a setting unit. The reception unit receives input from a player. The provision unit provides background information of the character when the input received by the reception unit is a question for the character. The dialogue unit dynamically generates dialogue content with the character based on the player's past actions and past selections. The change unit changes the character's emotions and attitudes based on the game situation and the relationship with the player. The setting unit sets a new quest or a new task based on the dialogue result with the player. Effect of the Invention

[0007] In accordance with an embodiment, the system can provide interactions that change dynamically in response to the player's actions and choices. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The in-game interactive character system according to the embodiment of the present invention is a system in which a player can ask a character questions to explore detailed background information about the character, such as past events, family relationships, likes and dislikes, etc. The system provides dynamic dialogue that changes based on the player's actions and past choices. In addition, the character's emotions and attitudes change depending on the game situation and the relationship with the player. Furthermore, new quests and tasks can be provided based on the dialogue with the player. For example, if the player acts friendly toward the character, the character will also show a friendly attitude. This allows the player to build a relationship with the character. Furthermore, new quests and tasks can be set based on the results of the dialogue with the player. This allows the player to always receive new challenges, and the progress of the game does not become monotonous. Furthermore, when the player is in trouble or lost during the progress of the game, the character provides hints and advice. This allows the player to smoothly progress through the game. In addition, through dialogue and actions with the player, the character can learn the player's preferences and strategies and take actions that correspond to them. This allows the player to have a game experience that matches his or her own play style. Furthermore, the game story branches depending on the player's choices and actions, and the character's reactions and actions change accordingly. This allows players to feel that their choices affect the outcome of the game, drawing them in even more. This allows the in-game interactive character system to provide players with a more immersive gaming experience.

[0029] The in-game interactive character system according to the embodiment includes a reception unit, a provision unit, a dialogue unit, a change unit, and a setting unit. The reception unit receives an input from a player. The input from the player includes, for example, text input, voice input, gesture input, and the like, but is not limited to such examples. The provision unit provides background information of the character when the input received by the reception unit is a question for the character. The background information of the character includes, for example, past events, personality, relationships, and the like of the character, but is not limited to such examples. The dialogue unit provides dialogue with the character with dialogue content according to the player's past actions and past selections. The dialogue content is dynamically generated, for example, based on the type of algorithm and the timing of generation, but is not limited to such examples. The change unit changes the emotion and attitude of the character based on the game situation and the relationship with the player. The emotion and attitude include, for example, emotions such as joy, anger, and sadness, and attitudes such as friendly and hostile, but is not limited to such examples. The setting unit sets a new quest or a new task based on the result of the dialogue with the player. The new quest or new task includes, for example, a quest goal and a task type, but is not limited to such examples. This allows the in-game interactive character system according to the embodiment to provide the player with a more immersive gaming experience.

[0030] The reception unit receives input from the player. Examples of input from the player include, but are not limited to, text input, voice input, and gesture input. Specifically, in the case of text input, the player inputs characters using a keyboard or touch screen, and the system analyzes the contents. In the case of voice input, when the player speaks through a microphone, the system converts the contents into text using voice recognition technology and analyzes them. In the case of gesture input, the system detects the player's movements using a camera or motion sensor and recognizes the movements as specific commands. This allows the reception unit to support various input means and improve the convenience of the player. Furthermore, the reception unit also has a function of detecting errors in the input contents and suggesting appropriate corrections. For example, if an erroneous recognition occurs in the voice input, the system can ask the player to confirm and have the player re-enter the correct contents. This allows the reception unit to accurately and quickly grasp the player's intention and proceed to the next process.

[0031] The providing unit provides background information of the character when the input accepted by the accepting unit is a question for the character. The background information of the character includes, for example, the character's past events, personality, relationships, etc., but is not limited to such examples. Specifically, the character's past events include specific events and adventure episodes. The personality is explained as to what personality traits the character has, for example, brave, timid, kind, etc. The relationships are explained as to the relationships with other characters and how past events have influenced the character. This allows the player to deeply understand the character's background and to empathize with the character more easily. Furthermore, the providing unit also has a function of dynamically generating information in response to the player's question. For example, when a player asks about a specific character's past events, the system extracts related information from the character's database and provides it to the player in an appropriate form. This allows the providing unit to quickly provide information according to the player's interests and concerns, enriching the game experience.

[0032] The dialogue unit provides dialogue with the character with dialogue content according to the player's past actions and past selections. The dialogue content is dynamically generated based on, for example, the type of algorithm and the timing of generation, but is not limited to such examples. Specifically, the actions and decisions selected by the player in the past are reflected in the dialogue content. For example, if the player previously helped a specific character, the character will have a dialogue showing gratitude. On the other hand, if the player takes a hostile action, the character will have a dialogue showing caution. In this way, the dialogue unit can provide a realistic dialogue according to the player's actions and enhance the immersiveness of the game. Furthermore, the dialogue unit uses natural language processing technology to analyze the player's input and generate an appropriate response. For example, if the player asks a character a question, the system understands the intention of the question and generates an appropriate answer. In this way, the dialogue unit can realize a natural dialogue with the player and improve the game experience.

[0033] The modification unit modifies the emotion and attitude of the character based on the game situation and the relationship with the player. The emotion and attitude include, but are not limited to, emotions such as joy, anger, and sadness, and attitudes such as friendly and hostile. Specifically, when the player takes a friendly action toward the character, the character shows emotions of joy and gratitude, and the attitude also becomes friendly. On the other hand, when the player takes a hostile action, the character shows anger and caution, and the attitude also becomes hostile. In this way, the modification unit can realize a realistic reaction of the character according to the player's action, and increase the immersiveness of the game. Furthermore, the modification unit also has a function of visually expressing the change in the emotion and attitude of the character. For example, the change in emotion is conveyed to the player by changing the character's facial expression and body language. In this way, the modification unit can provide the player with a more realistic reaction of the character, and improve the game experience.

[0034] The setting unit sets a new quest or a new task based on the result of the dialogue with the player. The new quest or the new task includes, for example, a quest goal or a task type, but is not limited to such examples. Specifically, when the player obtains specific information through dialogue with the character, a new quest is set based on the information. For example, when the character requests the player to go to a specific location, going to the location becomes the goal of the new quest. Also, when the player is requested to collect a specific item, collecting the item becomes the goal of the new task. In this way, the setting unit can provide a new goal according to the player's actions and make the progress of the game smoother. Furthermore, the setting unit also has a function of adjusting the difficulty of the quest or task according to the progress and achievement level of the player. For example, when the player has high skills, a quest or task with a higher difficulty is set. On the other hand, when the player is a beginner, a quest or task with a lower difficulty is set. In this way, the setting unit can provide an appropriate challenge to the player and maintain the fun of the game.

[0035] The advice unit can provide the player with specific hints or advice. For example, the advice unit provides appropriate hints or advice when the player is in trouble or confused. The specific hints or advice include, but are not limited to, hints regarding the progress of the game and advice regarding strategies. This allows the player to smoothly progress through the game. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the player's behavior data into AI to generate appropriate hints or advice.

[0036] The adjustment unit can adjust the character's behavior based on the player's preferences and strategies. The adjustment unit adjusts the character's behavior based on, for example, the player's past choices and play styles. The player's preferences and strategies include, for example, but are not limited to, the player's past choices and play styles. This allows the player to have a game experience that matches his or her play style. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the player's preference data into the AI ​​and adjust the character's behavior.

[0037] The modification unit can branch the game story based on the player's selection and action, and dynamically change the character's reaction and action based on the branched story. The modification unit, for example, branches the game story according to the player's selection and action. Specific methods for branching the game story include, but are not limited to, the number of options and the timing of branching. This allows the player to realize that his or her choice affects the ending of the game, and is further drawn into the game. Some or all of the above-mentioned processing in the modification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the modification unit can input the player's selection data into the generation AI and cause the generation AI to execute the branching of the story.

[0038] The prediction unit can predict the player's behavior based on what the character has learned through dialogue with the player, and suggest the next step. The prediction unit can predict the player's behavior based on what the character has learned through dialogue with the player, for example. Specific methods for suggesting the next step include, for example, the timing of the suggestion and the type of the suggested content, but are not limited to such examples. This allows the player to smoothly progress through the game. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the player's dialogue data into the AI ​​and suggest the next step.

[0039] The health management unit can estimate the health condition and fatigue level from the content of the dialogue of the player and suggest an appropriate break. The health management unit, for example, analyzes the content of the dialogue of the player and estimates the health condition and fatigue level. Specific evaluation criteria for the health condition and fatigue level include, for example, a method of analyzing the content of the dialogue and a method of acquiring health data, but are not limited to such examples. This allows the player to promote healthy gameplay. A part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the dialogue data of the player into AI and estimate the health condition and fatigue level.

[0040] The reception unit can analyze the player's past question history and select the optimal reception method. Specific criteria for the optimal reception method include, for example, a method for analyzing the past question history and a type of reception method, but are not limited to such examples. For example, the reception unit preferentially receives questions that the player has frequently asked in the past. In addition, the reception unit analyzes the player's past question history to determine a tendency to ask many questions in a specific time period, and provides the optimal reception method for that time period. In addition, the reception unit preferentially receives question formats (text, voice, etc.) that the player has used in the past. This provides the optimal reception method based on the player's past question history, and improves the efficiency of the dialogue. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the player's question history data into AI and select the optimal reception method.

[0041] The reception unit can perform filtering based on the player's current game progress when receiving a question. The reception unit, for example, filters the questions based on the player's current game progress. Specific criteria for filtering include, for example, a game progress evaluation method and a filtering algorithm, but are not limited to such examples. For example, when the player is in a specific quest, only questions related to the quest are received. Furthermore, when the player is in a boss battle, questions related to the battle are preferentially received. Furthermore, when the player is exploring, questions related to the exploration are preferentially received. This makes it possible to preferentially receive questions that are highly relevant according to the player's game progress. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the player's game progress data into the AI ​​and filter the questions.

[0042] When receiving a question, the reception unit can preferentially receive a highly relevant question in consideration of the geographical location information of the player. The reception unit, for example, filters questions in consideration of the geographical location information of the player. Specific methods for acquiring geographical location information include, for example, a method for acquiring GPS data and a criterion for evaluating relevance, but are not limited to such examples. For example, when the player is in a specific area, questions related to the area are preferentially accepted. Furthermore, when the player is in a specific building, questions related to the building are preferentially accepted. Furthermore, when the player is in a specific event, questions related to the event are preferentially accepted. This makes it possible to preferentially accept questions that are highly relevant based on the geographical location information of the player. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the player to AI and filter questions.

[0043] The reception unit can analyze the social media activity of the player when receiving a question, and receive related questions. The reception unit, for example, analyzes the social media activity of the player, and filters related questions. Specific methods of acquiring social media activity include, for example, a method of analyzing posted content and a criterion for evaluating relevance, but are not limited to such examples. For example, based on information shared by the player on social media, related questions are preferentially received. Also, based on topics followed by the player on social media, related questions are preferentially received. Furthermore, based on groups in which the player participates on social media, related questions are preferentially received. In this way, it is possible to receive related questions based on the player's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the player's social media data into AI and filter questions.

[0044] The providing unit can adjust the level of detail of the background information provided based on the importance of the character when providing the background information. The providing unit adjusts the level of detail of the background information based on the importance of the character, for example. Specific evaluation criteria for the importance of the character include, for example, the role in the story and the relationship with the player, but are not limited to such examples. For example, in the case of a main character, detailed background information is provided. In addition, in the case of a sub-character, brief background information is provided. Furthermore, in the case of a temporary character, the minimum background information is provided. This allows the level of detail of the background information to be adjusted according to the importance of the character, and more appropriate information provision can be realized. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the character to AI and adjust the level of detail of the background information.

[0045] The providing unit can apply different providing algorithms according to the character category when providing background information. The providing unit, for example, adjusts the providing algorithm of the background information according to the character category. Specific classification criteria of the character category include, for example, enemy character, ally character, neutral character, etc., but are not limited to such examples. For example, in the case of an enemy character, background information related to battle is provided. In addition, in the case of an ally character, background information related to cooperation is provided. Furthermore, in the case of a neutral character, background information related to the progress of the story is provided. In this way, the providing algorithm can be adjusted according to the character category to realize more appropriate information provision. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the character category data into AI and adjust the providing algorithm of the background information.

[0046] The providing unit can determine the priority of providing the background information based on the appearance time of the character when providing the background information. The providing unit determines the priority of providing the background information based on the appearance time of the character, for example. Specific evaluation criteria for the appearance time of the character include, for example, the progress stage of the story and the progress of the player, but are not limited to such examples. For example, when a main character first appears, detailed background information is provided. In addition, when a sub-character appears, brief background information is provided. Furthermore, when a temporary character appears, a minimum amount of background information is provided. This allows the priority of providing to be determined based on the appearance time of the character, and more appropriate information provision can be realized. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the appearance time data of the character to AI and determine the priority of providing the background information.

[0047] The providing unit can adjust the order of providing background information based on the relevance of the characters when providing the background information. The providing unit adjusts the order of providing background information based on the relevance of the characters, for example. Specific evaluation criteria for the relevance of the characters include, for example, story relationships and player selection, but are not limited to such examples. For example, background information of main characters is provided preferentially. In addition, background information of sub-characters is provided next. Furthermore, background information of temporary characters is provided last. This allows the order of providing to be adjusted based on the relevance of the characters, and more appropriate information provision can be realized. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input character relevance data to AI and adjust the order of providing background information.

[0048] The dialogue unit can adjust the level of detail of the dialogue based on the player's past selections during the dialogue. Specific adjustment criteria for the level of detail of the dialogue include, for example, evaluation criteria for past selections and types of level of detail, but are not limited to such examples. For example, detailed dialogue content is provided based on the content selected by the player in the past. Also, concise dialogue content is provided based on the content not selected by the player in the past. Furthermore, related dialogue content is provided based on the content selected by the player in the past. This allows the level of detail of the dialogue to be adjusted based on the player's past selections, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the player's selection data into the AI ​​and adjust the level of detail of the dialogue.

[0049] The dialogue unit can apply different dialogue algorithms according to the player's action history during dialogue. The dialogue unit, for example, adjusts the dialogue algorithm according to the player's action history. Specific types of dialogue algorithms include, for example, rule-based and machine learning-based, but are not limited to such examples. For example, detailed dialogue content is provided based on the player's past actions. Also, concise dialogue content is provided based on the player's past actions. Furthermore, related dialogue content is provided based on the player's past actions. This allows the dialogue algorithm to be adjusted according to the player's action history, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the player's action data into AI and adjust the dialogue algorithm.

[0050] The dialogue unit can determine the priority of dialogue based on the time of selection by the player during dialogue. Specific criteria for determining the priority of dialogue include, for example, evaluation criteria for the time of selection and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, dialogue content related to the quest is provided preferentially. Also, when the player is in a specific event, dialogue content related to the event is provided preferentially. Furthermore, dialogue content is provided preferentially based on the selection made by the player during a specific time period. This allows the priority of dialogue to be determined based on the time of selection by the player, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the selection data of the player to the AI ​​and determine the priority of dialogue.

[0051] The dialogue unit can adjust the order of dialogue based on the relevant actions of the player during dialogue. Specific criteria for adjusting the order of dialogue include, for example, evaluation criteria for the relevant actions and a method for determining the order, but are not limited to such examples. For example, the order of dialogue contents is adjusted based on the actions taken by the player in the past. Also, the order of dialogue contents is adjusted based on actions that the player has not taken in the past. Furthermore, the order of related dialogue contents is adjusted based on the actions taken by the player in the past. This allows the order of dialogue to be adjusted based on the relevant actions of the player, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the action data of the player to the AI ​​and adjust the order of dialogue.

[0052] The modification unit can adjust the level of detail of the change based on the player's past actions at the time of the change. Specific adjustment criteria for the level of detail of the change include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, detailed changes are made based on actions taken by the player in the past. Also, simple changes are made based on actions not taken by the player in the past. Furthermore, related changes are made based on actions taken by the player in the past. This makes it possible to adjust the level of detail of the change based on the player's past actions and realize a more appropriate character reaction. Part or all of the above-mentioned processing in the modification unit may be performed, for example, using AI, or may be performed without using AI. For example, the modification unit can input the player's action data into AI and adjust the level of detail of the change.

[0053] The change unit can apply different change algorithms according to the relationships between players at the time of change. The change unit adjusts the change algorithm according to the relationships between players, for example. Specific types of change algorithms include, for example, rule-based and machine learning-based, but are not limited to such examples. For example, if the players have built a friendly relationship, a friendly change is made. Also, if the players have built a hostile relationship, a hostile change is made. Furthermore, if the players have built a neutral relationship, a neutral change is made. This makes it possible to adjust the change algorithm according to the relationships between players and realize a more appropriate character reaction. Part or all of the above-mentioned processing in the change unit may be performed, for example, using AI, or may be performed without using AI. For example, the change unit can input the relationship data of the players to the AI ​​and adjust the change algorithm.

[0054] The change unit can determine the priority of the change based on the time of the player's action at the time of the change. Specific criteria for determining the priority of the change include, for example, evaluation criteria for the time of the action and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, the change related to the quest is made preferentially. Also, when the player is in a specific event, the change related to the event is made preferentially. Furthermore, the priority of the change is determined based on the action performed by the player in a specific time period. This allows the priority of the change to be determined based on the time of the player's action, and a more appropriate character reaction can be realized. A part or all of the above-mentioned processing in the change unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the change unit can input the player's action time data into the AI ​​and determine the priority of the change.

[0055] The change unit can adjust the order of the changes based on the player's related actions at the time of the change. Specific criteria for adjusting the order of the changes include, for example, evaluation criteria for related actions and a method for determining the order, but are not limited to such examples. For example, the order of the changes is adjusted based on the actions taken by the player in the past. Also, the order of the changes is adjusted based on the actions that the player has not taken in the past. Furthermore, the order of the related changes is adjusted based on the actions taken by the player in the past. This makes it possible to adjust the order of the changes based on the player's related actions and realize a more appropriate character reaction. A part or all of the above-mentioned processing in the change unit may be performed, for example, using AI, or may be performed without using AI. For example, the change unit can input the player's action data into AI and adjust the order of the changes.

[0056] The setting unit can adjust the level of detail of the setting based on the player's past quest history at the time of setting. Specific adjustment criteria for the level of detail of the setting include, for example, evaluation criteria for the past quest history and the type of level of detail, but are not limited to such examples. For example, detailed settings are made based on quests performed by the player in the past. Also, simple settings are made based on quests not performed by the player in the past. Furthermore, related settings are made based on quests performed by the player in the past. In this way, the level of detail of the setting can be adjusted based on the player's past quest history, and more appropriate quests and tasks can be provided. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the setting unit can input the player's quest history data into AI and adjust the level of detail of the setting.

[0057] The setting unit can apply different setting algorithms according to the player's action history at the time of setting. The setting unit adjusts the setting algorithm according to the player's action history, for example. Specific types of setting algorithms include, for example, rule-based and machine learning-based, but are not limited to such examples. For example, detailed settings are made based on the player's past actions. Also, simple settings are made based on the player's past actions. Furthermore, related settings are made based on the player's past actions. This makes it possible to adjust the setting algorithm according to the player's action history and provide more appropriate quests and tasks. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the setting unit can input the player's action data into AI and adjust the setting algorithm.

[0058] The setting unit can determine the priority of the settings based on the quest time of the player at the time of setting. Specific criteria for determining the priority of the settings include, for example, evaluation criteria for the quest time and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, the settings related to the quest are given priority. Also, when the player is in a specific event, the settings related to the event are given priority. Furthermore, the priority of the settings is determined based on the quest performed by the player in a specific time period. In this way, the priority of the settings can be determined based on the quest time of the player, and more appropriate quests and tasks can be provided. Part or all of the above-mentioned processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the quest time data of the player to AI and determine the priority of the settings.

[0059] The setting unit can adjust the order of the settings based on the player's related actions at the time of setting. Specific criteria for adjusting the order of the settings include, for example, evaluation criteria for related actions and a method for determining the order, but are not limited to such examples. For example, the order of the settings is adjusted based on the player's actions in the past. Also, the order of the settings is adjusted based on the player's actions that the player has not performed in the past. Furthermore, the order of related settings is adjusted based on the player's actions in the past. This makes it possible to adjust the order of the settings based on the player's related actions and provide more appropriate quests and tasks. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the setting unit can input the player's action data into AI and adjust the order of the settings.

[0060] The advice unit can adjust the level of detail of advice based on the player's past actions when providing advice. Specific adjustment criteria for the level of detail of advice include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, detailed advice is provided based on actions taken by the player in the past. Also, concise advice is provided based on actions not taken by the player in the past. Furthermore, related advice is provided based on actions taken by the player in the past. This allows the level of detail of advice to be adjusted based on the player's past actions, and more appropriate advice to be provided. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the player's action data into AI and adjust the level of detail of advice.

[0061] The advice unit can determine the priority of advice based on the time of the player's action when providing advice. Specific criteria for determining the priority of advice include, for example, evaluation criteria for the time of action and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, advice related to the quest is provided preferentially. Also, when the player is in a specific event, advice related to the event is provided preferentially. Furthermore, the priority of advice is determined based on the action performed by the player in a specific time period. In this way, the priority of advice can be determined based on the time of the player's action, and more appropriate advice can be provided. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the player's action time data into AI and determine the priority of advice.

[0062] The adjustment unit can adjust the level of detail of the adjustment based on the player's past actions during the adjustment. Specific adjustment criteria for the level of detail of the adjustment include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, detailed adjustment is performed based on the player's past actions. Also, simple adjustment is performed based on the player's past actions. Furthermore, related adjustment is performed based on the player's past actions. This makes it possible to adjust the level of detail of the adjustment based on the player's past actions and realize a more appropriate character reaction. Part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the player's action data into AI and adjust the level of detail of the adjustment.

[0063] The adjustment unit can determine the priority of the adjustment based on the time of the player's action during the adjustment. Specific criteria for determining the priority of the adjustment include, for example, evaluation criteria for the time of the action and a method for determining the priority, but are not limited to such examples. For example, if the player is in a specific quest, the adjustment related to the quest is performed preferentially. Also, if the player is in a specific event, the adjustment related to the event is performed preferentially. Furthermore, the priority of the adjustment is determined based on the action taken by the player during a specific time period. This allows the priority of the adjustment to be determined based on the time of the player's action, and a more appropriate character reaction to be realized. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the player's action time data into the AI ​​and determine the priority of the adjustment.

[0064] The prediction unit can adjust the level of detail of the prediction based on the player's past actions at the time of prediction. Specific adjustment criteria for the level of detail of the prediction include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, a detailed prediction is made based on the player's past actions. Also, a concise prediction is made based on the player's past actions. Furthermore, a related prediction is made based on the player's past actions. This makes it possible to adjust the level of detail of the prediction based on the player's past actions and realize a more appropriate prediction. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the player's action data into AI and adjust the level of detail of the prediction.

[0065] The prediction unit can determine the priority of prediction based on the time of the player's action at the time of prediction. Specific criteria for determining the priority of prediction include, for example, evaluation criteria for the time of action and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, the next step related to the quest is predicted with priority. Also, when the player is in a specific event, the next step related to the event is predicted with priority. Furthermore, the priority of the next step is determined based on the action performed by the player in a specific time period. This allows the priority of prediction to be determined based on the time of the player's action, and more appropriate prediction can be realized. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the player's action time data into AI and determine the priority of prediction.

[0066] The health management unit can adjust the level of detail of the proposal based on the player's past health data when proposing health management. Specific adjustment criteria for the level of detail of the proposal include, for example, evaluation criteria for past health data and types of level of detail, but are not limited to such examples. For example, a detailed proposal is made based on the health management performed by the player in the past. Also, a concise proposal is made based on the health management not performed by the player in the past. Furthermore, a related proposal is made based on the health management performed by the player in the past. In this way, the level of detail of the proposal can be adjusted based on the player's past health data, and more appropriate health management can be realized. Part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the player's health data into AI and adjust the level of detail of the proposal.

[0067] The health management unit can determine the priority of the proposal based on the time of acquisition of the player's health data when making a health management proposal. Specific criteria for determining the priority of the proposal include, for example, evaluation criteria for the time of acquisition of the health data and a method for determining the priority, but are not limited to such examples. For example, the latest proposal is given priority based on the health data recently acquired by the player. Also, related proposals are made based on the health data acquired by the player in the past. Furthermore, the priority of the proposal is determined based on the health data acquired by the player in a specific time period. This allows the priority of the proposal to be determined based on the time of acquisition of the player's health data, thereby realizing more appropriate health management. Part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the player's health data into AI and determine the priority of the proposal.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] When accepting an input from a player, the reception unit can adjust the input method in consideration of the player's current health condition. For example, if the player is tired, voice input is preferentially accepted. Also, if the player is relaxed, text input is preferentially accepted. Furthermore, if the player is stressed, gesture input is preferentially accepted. This allows the player to be provided with an optimal input method according to the player's health condition, and to achieve a more comfortable gaming experience. The health condition is estimated, for example, using a method for acquiring health data or an evaluation criterion. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the player's health data into AI and adjust the input method.

[0070] The advice unit can infer the learning style of the player from the content of the player's dialogue and provide appropriate hints and advice. For example, if the player has a visual learning style, visual hints are provided. Also, if the player has an auditory learning style, audio advice is provided. Furthermore, if the player has an experiential learning style, advice through actual game play is provided. In this way, optimal advice according to the player's learning style can be provided, and the game can progress smoothly. The learning style is inferred, for example, using an analysis method or evaluation criteria of the dialogue content. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the dialogue data of the player into AI and infer the learning style.

[0071] When adjusting the character's behavior based on the player's preferences and strategies, the adjustment unit can analyze the player's past gameplay data and select the behavior that best suits the player's play style. For example, if the player has taken an aggressive play style in the past, the character will also take an aggressive action. Furthermore, if the player has taken a defensive play style, the character will also take a defensive action. Furthermore, if the player has taken a balanced play style, the character will also take a balanced action. This provides the optimal character behavior according to the player's play style, and achieves a greater sense of immersion. The analysis of the play style is performed, for example, using evaluation criteria and analysis methods for past gameplay data. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the player's gameplay data into AI and adjust the character's behavior.

[0072] When the change unit branches the story of the game based on the player's selection and action, the change unit can analyze the player's past selection data, predict the story development that the player prefers, and provide it. For example, if the story development selected by the player in the past was action-oriented, the change unit provides an action-oriented story development. Furthermore, if the story development selected by the player in the past was drama-oriented, the change unit provides a drama-oriented story development. Furthermore, if the story development selected by the player in the past was mystery-oriented, the change unit provides a mystery-oriented story development. This allows the player to provide an optimal story development according to their preferences, and achieve a greater sense of immersion. The analysis of the selection data is performed, for example, using evaluation criteria and analysis methods for past selection data. A part or all of the above-mentioned processing in the change unit may be performed, for example, using AI, or may be performed without using AI. For example, the change unit can input the player's selection data into AI and adjust the branching of the story.

[0073] When predicting the player's behavior based on the content that the character has learned through the dialogue with the player, the prediction unit can analyze the player's past dialogue data and predict the next action that the player is likely to take. For example, if the player has asked a specific question in the past, the prediction unit predicts an action related to the question. Also, if the player has made a specific choice in the past, the prediction unit predicts an action related to the choice. Furthermore, if the player has taken a specific action in the past, the prediction unit predicts the next step related to the action. This makes it possible to predict the optimal next step based on the player's past dialogue data and smoothly progress through the game. The analysis of the dialogue data is performed, for example, using evaluation criteria and analysis methods for past dialogue data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the player's dialogue data into AI and predict the next step.

[0074] When the health management unit estimates the health state and fatigue level from the content of the player's dialogue, the health management unit can analyze the player's past health data and estimate the player's health state more accurately. For example, if the player felt fatigue in the past, the health management unit estimates the current health state based on the fatigue level. Also, if the player was in a healthy state in the past, the health management unit estimates the current health state based on the health state. Furthermore, if the player had a specific health problem in the past, the health management unit estimates the current health state based on the health problem. This makes it possible to estimate the player's health state more accurately based on the player's past health data and suggest appropriate rest. The analysis of the health data is performed, for example, using evaluation criteria and analysis methods for past health data. Part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the player's health data into AI and estimate the health state.

[0075] The process flow of the first embodiment will be briefly described below.

[0076] Step 1: The reception unit receives input from a player, including, but not limited to, text input, voice input, and gesture input. Step 2: If the input received by the reception unit is a question for the character, the providing unit provides background information of the character. The background information of the character includes, but is not limited to, the character's past events, personality, relationships, etc. Step 3: The dialogue unit dynamically generates dialogue content with the character based on the player's past actions and past choices. The dialogue content is dynamically generated based on, but not limited to, the type of algorithm and the timing of generation. Step 4: The modification unit modifies the character's emotions and attitudes based on the game situation and the relationship with the player. The emotions and attitudes include, but are not limited to, emotions such as joy, anger, sadness, and attitudes such as friendly and hostile. Step 5: The setting unit sets a new quest or a new task based on the result of the dialogue with the player. The new quest or the new task includes, but is not limited to, a quest goal and a task type.

[0077] (Example 2) The in-game interactive character system according to the embodiment of the present invention is a system in which a player can ask a character questions to explore detailed background information about the character, such as past events, family relationships, likes and dislikes, etc. The system provides dynamic dialogue that changes based on the player's actions and past choices. In addition, the character's emotions and attitudes change depending on the game situation and the relationship with the player. Furthermore, new quests and tasks can be provided based on the dialogue with the player. For example, if the player acts friendly toward the character, the character will also show a friendly attitude. This allows the player to build a relationship with the character. Furthermore, new quests and tasks can be set based on the results of the dialogue with the player. This allows the player to always receive new challenges, and the progress of the game does not become monotonous. Furthermore, when the player is in trouble or lost during the progress of the game, the character provides hints and advice. This allows the player to smoothly progress through the game. In addition, through dialogue and actions with the player, the character can learn the player's preferences and strategies and take actions that correspond to them. This allows the player to have a game experience that matches his or her own play style. Furthermore, the game story branches depending on the player's choices and actions, and the character's reactions and actions change accordingly. This allows players to feel that their choices affect the outcome of the game, drawing them in even more. This allows the in-game interactive character system to provide players with a more immersive gaming experience.

[0078] The in-game interactive character system according to the embodiment includes a reception unit, a provision unit, a dialogue unit, a change unit, and a setting unit. The reception unit receives an input from a player. The input from the player includes, for example, text input, voice input, gesture input, and the like, but is not limited to such examples. The provision unit provides background information of the character when the input received by the reception unit is a question for the character. The background information of the character includes, for example, past events, personality, relationships, and the like of the character, but is not limited to such examples. The dialogue unit provides dialogue with the character with dialogue content according to the player's past actions and past selections. The dialogue content is dynamically generated, for example, based on the type of algorithm and the timing of generation, but is not limited to such examples. The change unit changes the emotion and attitude of the character based on the game situation and the relationship with the player. The emotion and attitude include, for example, emotions such as joy, anger, and sadness, and attitudes such as friendly and hostile, but is not limited to such examples. The setting unit sets a new quest or a new task based on the result of the dialogue with the player. The new quest or new task includes, for example, a quest goal and a task type, but is not limited to such examples. This allows the in-game interactive character system according to the embodiment to provide the player with a more immersive gaming experience.

[0079] The reception unit receives input from the player. Examples of input from the player include, but are not limited to, text input, voice input, and gesture input. Specifically, in the case of text input, the player inputs characters using a keyboard or touch screen, and the system analyzes the contents. In the case of voice input, when the player speaks through a microphone, the system converts the contents into text using voice recognition technology and analyzes them. In the case of gesture input, the system detects the player's movements using a camera or motion sensor and recognizes the movements as specific commands. This allows the reception unit to support various input means and improve the convenience of the player. Furthermore, the reception unit also has a function of detecting errors in the input contents and suggesting appropriate corrections. For example, if an erroneous recognition occurs in the voice input, the system can ask the player to confirm and have the player re-enter the correct contents. This allows the reception unit to accurately and quickly grasp the player's intention and proceed to the next process.

[0080] The providing unit provides background information of the character when the input accepted by the accepting unit is a question for the character. The background information of the character includes, for example, the character's past events, personality, relationships, etc., but is not limited to such examples. Specifically, the character's past events include specific events and adventure episodes. The personality is explained as to what personality traits the character has, for example, brave, timid, kind, etc. The relationships are explained as to the relationships with other characters and how past events have influenced the character. This allows the player to deeply understand the character's background and to empathize with the character more easily. Furthermore, the providing unit also has a function of dynamically generating information in response to the player's question. For example, when a player asks about a specific character's past events, the system extracts related information from the character's database and provides it to the player in an appropriate form. This allows the providing unit to quickly provide information according to the player's interests and concerns, enriching the game experience.

[0081] The dialogue unit provides dialogue with the character with dialogue content according to the player's past actions and past selections. The dialogue content is dynamically generated based on, for example, the type of algorithm and the timing of generation, but is not limited to such examples. Specifically, the actions and decisions selected by the player in the past are reflected in the dialogue content. For example, if the player previously helped a specific character, the character will have a dialogue showing gratitude. On the other hand, if the player takes a hostile action, the character will have a dialogue showing caution. In this way, the dialogue unit can provide a realistic dialogue according to the player's actions and enhance the immersiveness of the game. Furthermore, the dialogue unit uses natural language processing technology to analyze the player's input and generate an appropriate response. For example, if the player asks a character a question, the system understands the intention of the question and generates an appropriate answer. In this way, the dialogue unit can realize a natural dialogue with the player and improve the game experience.

[0082] The modification unit modifies the emotion and attitude of the character based on the game situation and the relationship with the player. The emotion and attitude include, but are not limited to, emotions such as joy, anger, and sadness, and attitudes such as friendly and hostile. Specifically, when the player takes a friendly action toward the character, the character shows emotions of joy and gratitude, and the attitude also becomes friendly. On the other hand, when the player takes a hostile action, the character shows anger and caution, and the attitude also becomes hostile. In this way, the modification unit can realize a realistic reaction of the character according to the player's action, and increase the immersiveness of the game. Furthermore, the modification unit also has a function of visually expressing the change in the emotion and attitude of the character. For example, the change in emotion is conveyed to the player by changing the character's facial expression and body language. In this way, the modification unit can provide the player with a more realistic reaction of the character, and improve the game experience.

[0083] The setting unit sets a new quest or a new task based on the result of the dialogue with the player. The new quest or the new task includes, for example, a quest goal or a task type, but is not limited to such examples. Specifically, when the player obtains specific information through dialogue with the character, a new quest is set based on the information. For example, when the character requests the player to go to a specific location, going to the location becomes the goal of the new quest. Also, when the player is requested to collect a specific item, collecting the item becomes the goal of the new task. In this way, the setting unit can provide a new goal according to the player's actions and make the progress of the game smoother. Furthermore, the setting unit also has a function of adjusting the difficulty of the quest or task according to the progress and achievement level of the player. For example, when the player has high skills, a quest or task with a higher difficulty is set. On the other hand, when the player is a beginner, a quest or task with a lower difficulty is set. In this way, the setting unit can provide an appropriate challenge to the player and maintain the fun of the game.

[0084] The advice unit can provide the player with specific hints or advice. For example, the advice unit provides appropriate hints or advice when the player is in trouble or confused. The specific hints or advice include, but are not limited to, hints regarding the progress of the game and advice regarding strategies. This allows the player to smoothly progress through the game. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the player's behavior data into AI to generate appropriate hints or advice.

[0085] The adjustment unit can adjust the character's behavior based on the player's preferences and strategies. The adjustment unit adjusts the character's behavior based on, for example, the player's past choices and play styles. The player's preferences and strategies include, for example, but are not limited to, the player's past choices and play styles. This allows the player to have a game experience that matches his or her play style. Some or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the player's preference data into the AI ​​and adjust the character's behavior.

[0086] The modification unit can branch the game story based on the player's selection and action, and dynamically change the character's reaction and action based on the branched story. The modification unit, for example, branches the game story according to the player's selection and action. Specific methods for branching the game story include, but are not limited to, the number of options and the timing of branching. This allows the player to realize that his or her choice affects the ending of the game, and is further drawn into the game. Some or all of the above-mentioned processing in the modification unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the modification unit can input the player's selection data into the generation AI and cause the generation AI to execute the branching of the story.

[0087] The prediction unit can predict the player's behavior based on what the character has learned through dialogue with the player, and suggest the next step. The prediction unit can predict the player's behavior based on what the character has learned through dialogue with the player, for example. Specific methods for suggesting the next step include, for example, the timing of the suggestion and the type of the suggested content, but are not limited to such examples. This allows the player to smoothly progress through the game. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the player's dialogue data into the AI ​​and suggest the next step.

[0088] The health management unit can estimate the health condition and fatigue level from the content of the dialogue of the player and suggest an appropriate break. The health management unit, for example, analyzes the content of the dialogue of the player and estimates the health condition and fatigue level. Specific evaluation criteria for the health condition and fatigue level include, for example, a method of analyzing the content of the dialogue and a method of acquiring health data, but are not limited to such examples. This allows the player to promote healthy gameplay. A part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the dialogue data of the player into AI and estimate the health condition and fatigue level.

[0089] The reception unit can estimate the player's emotion and adjust the timing of accepting questions based on the estimated player's emotion. Specific types of the player's emotion include, but are not limited to, excitement, relaxation, stress, and the like. For example, when the player is excited, the reception of questions is temporarily delayed and the player waits until the player calms down. Also, when the player is relaxed, the question is immediately accepted to promote smooth dialogue. Furthermore, when the player is stressed, the reception of questions is simplified so that the player does not feel burdened. This makes it possible to realize smooth dialogue according to the player's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the player's emotion data to the generation AI and adjust the timing of accepting questions.

[0090] The reception unit can analyze the player's past question history and select the optimal reception method. Specific criteria for the optimal reception method include, for example, a method for analyzing the past question history and a type of reception method, but are not limited to such examples. For example, the reception unit preferentially receives questions that the player has frequently asked in the past. In addition, the reception unit analyzes the player's past question history to determine a tendency to ask many questions in a specific time period, and provides the optimal reception method for that time period. In addition, the reception unit preferentially receives question formats (text, voice, etc.) that the player has used in the past. This provides the optimal reception method based on the player's past question history, and improves the efficiency of the dialogue. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the player's question history data into AI and select the optimal reception method.

[0091] The reception unit can perform filtering based on the player's current game progress when receiving a question. The reception unit, for example, filters the questions based on the player's current game progress. Specific criteria for filtering include, for example, a game progress evaluation method and a filtering algorithm, but are not limited to such examples. For example, when the player is in a specific quest, only questions related to the quest are received. Furthermore, when the player is in a boss battle, questions related to the battle are preferentially received. Furthermore, when the player is exploring, questions related to the exploration are preferentially received. This makes it possible to preferentially receive questions that are highly relevant according to the player's game progress. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the player's game progress data into the AI ​​and filter the questions.

[0092] The reception unit can estimate the player's emotion and determine the priority of the questions to be received based on the estimated player's emotion. Specific criteria for determining the priority of the questions include, for example, emotion evaluation criteria and a method for determining the priority, but are not limited to such examples. For example, if the player is excited, questions with high urgency are preferentially received. Also, if the player is relaxed, detailed questions are preferentially received. Furthermore, if the player is stressed, simple questions are preferentially received. In this way, the priority of the questions can be determined according to the player's emotion, and more appropriate questions can be received. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the player's emotion data to the generation AI and determine the priority of the questions.

[0093] When receiving a question, the reception unit can preferentially receive a highly relevant question in consideration of the geographical location information of the player. The reception unit, for example, filters questions in consideration of the geographical location information of the player. Specific methods for acquiring geographical location information include, for example, a method for acquiring GPS data and a criterion for evaluating relevance, but are not limited to such examples. For example, when the player is in a specific area, questions related to the area are preferentially accepted. Furthermore, when the player is in a specific building, questions related to the building are preferentially accepted. Furthermore, when the player is in a specific event, questions related to the event are preferentially accepted. This makes it possible to preferentially accept questions that are highly relevant based on the geographical location information of the player. Part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the geographical location data of the player to AI and filter questions.

[0094] The reception unit can analyze the social media activity of the player when receiving a question, and receive related questions. The reception unit, for example, analyzes the social media activity of the player, and filters related questions. Specific methods of acquiring social media activity include, for example, a method of analyzing posted content and a criterion for evaluating relevance, but are not limited to such examples. For example, based on information shared by the player on social media, related questions are preferentially received. Also, based on topics followed by the player on social media, related questions are preferentially received. Furthermore, based on groups in which the player participates on social media, related questions are preferentially received. In this way, it is possible to receive related questions based on the player's social media activity. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the player's social media data into AI and filter questions.

[0095] The providing unit can estimate the player's emotion and adjust the method of providing background information based on the estimated player's emotion. Specific adjustment criteria for the method of providing background information include, for example, emotion evaluation criteria and types of providing methods, but are not limited to such examples. For example, when the player is excited, concise and to the point background information is provided. When the player is relaxed, detailed background information is provided. Furthermore, when the player is stressed, background information is provided in a format that visually relaxes the player. This allows the method of providing background information to be adjusted according to the player's emotion, and more appropriate information to be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the player's emotion data into the generation AI and adjust the method of providing background information.

[0096] The providing unit can adjust the level of detail of the background information provided based on the importance of the character when providing the background information. The providing unit adjusts the level of detail of the background information based on the importance of the character, for example. Specific evaluation criteria for the importance of the character include, for example, the role in the story and the relationship with the player, but are not limited to such examples. For example, in the case of a main character, detailed background information is provided. In addition, in the case of a sub-character, brief background information is provided. Furthermore, in the case of a temporary character, the minimum background information is provided. This allows the level of detail of the background information to be adjusted according to the importance of the character, and more appropriate information provision can be realized. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the importance data of the character to AI and adjust the level of detail of the background information.

[0097] The providing unit can apply different providing algorithms according to the character category when providing background information. The providing unit, for example, adjusts the providing algorithm of the background information according to the character category. Specific classification criteria of the character category include, for example, enemy character, ally character, neutral character, etc., but are not limited to such examples. For example, in the case of an enemy character, background information related to battle is provided. In addition, in the case of an ally character, background information related to cooperation is provided. Furthermore, in the case of a neutral character, background information related to the progress of the story is provided. In this way, the providing algorithm can be adjusted according to the character category to realize more appropriate information provision. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the character category data into AI and adjust the providing algorithm of the background information.

[0098] The providing unit can estimate the player's emotion and adjust the length of the background information based on the estimated player's emotion. Specific adjustment criteria for the length of the background information include, for example, emotion evaluation criteria and the importance of information, but are not limited to such examples. For example, if the player is in a hurry, short and to the point background information is provided. Also, if the player is relaxed, detailed background information is provided. Furthermore, if the player is excited, visually stimulating background information is provided. This allows the length of the background information to be adjusted according to the player's emotion, and more appropriate information to be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the providing unit can input the player's emotion data into the generation AI and adjust the length of the background information.

[0099] The providing unit can determine the priority of providing the background information based on the appearance time of the character when providing the background information. The providing unit determines the priority of providing the background information based on the appearance time of the character, for example. Specific evaluation criteria for the appearance time of the character include, for example, the progress stage of the story and the progress of the player, but are not limited to such examples. For example, when a main character first appears, detailed background information is provided. In addition, when a sub-character appears, brief background information is provided. Furthermore, when a temporary character appears, a minimum amount of background information is provided. This allows the priority of providing to be determined based on the appearance time of the character, and more appropriate information provision can be realized. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the appearance time data of the character to AI and determine the priority of providing the background information.

[0100] The providing unit can adjust the order of providing background information based on the relevance of the characters when providing the background information. The providing unit adjusts the order of providing background information based on the relevance of the characters, for example. Specific evaluation criteria for the relevance of the characters include, for example, story relationships and player selection, but are not limited to such examples. For example, background information of main characters is provided preferentially. In addition, background information of sub-characters is provided next. Furthermore, background information of temporary characters is provided last. This allows the order of providing to be adjusted based on the relevance of the characters, and more appropriate information provision can be realized. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input character relevance data to AI and adjust the order of providing background information.

[0101] The dialogue unit can estimate the player's emotion and adjust the dialogue content based on the estimated player's emotion. Specific adjustment criteria for the dialogue content include, for example, emotion evaluation criteria and types of dialogue, but are not limited to such examples. For example, when the player is excited, a concise and to the point dialogue content is provided. Also, when the player is relaxed, detailed dialogue content is provided. Furthermore, when the player is stressed, a dialogue content that visually relaxes the player is provided. This allows the dialogue content to be adjusted according to the player's emotion and realizes a more appropriate dialogue. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input the player's emotion data to the generation AI and adjust the dialogue content.

[0102] The dialogue unit can adjust the level of detail of the dialogue based on the player's past selections during the dialogue. Specific adjustment criteria for the level of detail of the dialogue include, for example, evaluation criteria for past selections and types of level of detail, but are not limited to such examples. For example, detailed dialogue content is provided based on the content selected by the player in the past. Also, concise dialogue content is provided based on the content not selected by the player in the past. Furthermore, related dialogue content is provided based on the content selected by the player in the past. This allows the level of detail of the dialogue to be adjusted based on the player's past selections, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the player's selection data into the AI ​​and adjust the level of detail of the dialogue.

[0103] The dialogue unit can apply different dialogue algorithms according to the player's action history during dialogue. The dialogue unit, for example, adjusts the dialogue algorithm according to the player's action history. Specific types of dialogue algorithms include, for example, rule-based and machine learning-based, but are not limited to such examples. For example, detailed dialogue content is provided based on the player's past actions. Also, concise dialogue content is provided based on the player's past actions. Furthermore, related dialogue content is provided based on the player's past actions. This allows the dialogue algorithm to be adjusted according to the player's action history, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the player's action data into AI and adjust the dialogue algorithm.

[0104] The dialogue unit can estimate the player's emotion and adjust the length of the dialogue based on the estimated player's emotion. Specific adjustment criteria for the length of the dialogue include, for example, emotion evaluation criteria and the importance of the dialogue, but are not limited to such examples. For example, if the player is in a hurry, a short and to the point dialogue content is provided. Also, if the player is relaxed, detailed dialogue content is provided. Furthermore, if the player is excited, visually stimulating dialogue content is provided. This allows the dialogue length to be adjusted according to the player's emotion and a more appropriate dialogue to be realized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the dialogue unit can input the player's emotion data to the generation AI and adjust the length of the dialogue.

[0105] The dialogue unit can determine the priority of dialogue based on the time of selection by the player during dialogue. Specific criteria for determining the priority of dialogue include, for example, evaluation criteria for the time of selection and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, dialogue content related to the quest is provided preferentially. Also, when the player is in a specific event, dialogue content related to the event is provided preferentially. Furthermore, dialogue content is provided preferentially based on the selection made by the player during a specific time period. This allows the priority of dialogue to be determined based on the time of selection by the player, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the selection data of the player to the AI ​​and determine the priority of dialogue.

[0106] The dialogue unit can adjust the order of dialogue based on the relevant actions of the player during dialogue. Specific criteria for adjusting the order of dialogue include, for example, evaluation criteria for the relevant actions and a method for determining the order, but are not limited to such examples. For example, the order of dialogue contents is adjusted based on the actions taken by the player in the past. Also, the order of dialogue contents is adjusted based on actions that the player has not taken in the past. Furthermore, the order of related dialogue contents is adjusted based on the actions taken by the player in the past. This allows the order of dialogue to be adjusted based on the relevant actions of the player, and more appropriate dialogue can be realized. Part or all of the above-mentioned processing in the dialogue unit may be performed, for example, using AI, or may be performed without using AI. For example, the dialogue unit can input the action data of the player to the AI ​​and adjust the order of dialogue.

[0107] The modification unit can estimate the player's emotion and modify the character's emotion and attitude based on the estimated player's emotion. Specific types of the character's emotion and attitude include, but are not limited to, emotions such as joy, anger, and sadness, and attitudes such as friendly and hostile. For example, when the player shows a friendly emotion, the character also shows a friendly attitude. Furthermore, when the player shows a hostile emotion, the character also shows a hostile attitude. Furthermore, when the player shows a neutral emotion, the character also shows a neutral attitude. This allows the character's emotion and attitude to be changed according to the player's emotion, and a more appropriate character reaction to be realized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the modification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the modification unit can input the player's emotion data into the generation AI and modify the character's emotion and attitude.

[0108] The modification unit can adjust the level of detail of the change based on the player's past actions at the time of the change. Specific adjustment criteria for the level of detail of the change include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, detailed changes are made based on actions taken by the player in the past. Also, simple changes are made based on actions not taken by the player in the past. Furthermore, related changes are made based on actions taken by the player in the past. This makes it possible to adjust the level of detail of the change based on the player's past actions and realize a more appropriate character reaction. Part or all of the above-mentioned processing in the modification unit may be performed, for example, using AI, or may be performed without using AI. For example, the modification unit can input the player's action data into AI and adjust the level of detail of the change.

[0109] The change unit can apply different change algorithms according to the relationships between players at the time of change. The change unit adjusts the change algorithm according to the relationships between players, for example. Specific types of change algorithms include, for example, rule-based and machine learning-based, but are not limited to such examples. For example, if the players have built a friendly relationship, a friendly change is made. Also, if the players have built a hostile relationship, a hostile change is made. Furthermore, if the players have built a neutral relationship, a neutral change is made. This makes it possible to adjust the change algorithm according to the relationships between players and realize a more appropriate character reaction. Part or all of the above-mentioned processing in the change unit may be performed, for example, using AI, or may be performed without using AI. For example, the change unit can input the relationship data of the players to the AI ​​and adjust the change algorithm.

[0110] The modification unit can estimate the player's emotion and adjust the change frequency of the character's emotion and attitude based on the estimated player's emotion. Specific adjustment criteria for the change frequency include, for example, emotion evaluation criteria and frequency types, but are not limited to such examples. For example, if the player frequently changes emotions, the change frequency of the character's emotion and attitude is increased. Also, if the player does not change emotions much, the change frequency of the character's emotion and attitude is decreased. Furthermore, if the player shows a specific emotion, the change frequency of the character's emotion and attitude is adjusted according to the emotion. In this way, the change frequency of the character's emotion and attitude can be adjusted according to the player's emotion, and a more appropriate character reaction can be realized. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the modification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the modification unit can input the player's emotion data into the generation AI and adjust the change frequency of the character's emotion and attitude.

[0111] The change unit can determine the priority of the change based on the time of the player's action at the time of the change. Specific criteria for determining the priority of the change include, for example, evaluation criteria for the time of the action and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, the change related to the quest is made preferentially. Also, when the player is in a specific event, the change related to the event is made preferentially. Furthermore, the priority of the change is determined based on the action performed by the player in a specific time period. This allows the priority of the change to be determined based on the time of the player's action, and a more appropriate character reaction can be realized. A part or all of the above-mentioned processing in the change unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the change unit can input the player's action time data into the AI ​​and determine the priority of the change.

[0112] The change unit can adjust the order of the changes based on the player's related actions at the time of the change. Specific criteria for adjusting the order of the changes include, for example, evaluation criteria for related actions and a method for determining the order, but are not limited to such examples. For example, the order of the changes is adjusted based on the actions taken by the player in the past. Also, the order of the changes is adjusted based on the actions that the player has not taken in the past. Furthermore, the order of the related changes is adjusted based on the actions taken by the player in the past. This makes it possible to adjust the order of the changes based on the player's related actions and realize a more appropriate character reaction. A part or all of the above-mentioned processing in the change unit may be performed, for example, using AI, or may be performed without using AI. For example, the change unit can input the player's action data into AI and adjust the order of the changes.

[0113] The setting unit can estimate the player's emotion and adjust the setting method of the new quest or task based on the estimated player's emotion. Specific adjustment criteria for the setting method of the new quest or task include, for example, emotion evaluation criteria and the type of setting method, but are not limited to such examples. For example, if the player is excited, a challenging quest is set. Also, if the player is relaxed, a relaxing task is set. Furthermore, if the player is stressed, an easy task is set. This makes it possible to adjust the setting method of the new quest or task according to the player's emotion and provide a more appropriate quest or task. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the setting unit can input the player's emotion data into the generation AI and adjust the setting method of the new quest or task.

[0114] The setting unit can adjust the level of detail of the setting based on the player's past quest history at the time of setting. Specific adjustment criteria for the level of detail of the setting include, for example, evaluation criteria for the past quest history and the type of level of detail, but are not limited to such examples. For example, detailed settings are made based on quests performed by the player in the past. Also, simple settings are made based on quests not performed by the player in the past. Furthermore, related settings are made based on quests performed by the player in the past. In this way, the level of detail of the setting can be adjusted based on the player's past quest history, and more appropriate quests and tasks can be provided. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the setting unit can input the player's quest history data into AI and adjust the level of detail of the setting.

[0115] The setting unit can apply different setting algorithms according to the player's action history at the time of setting. The setting unit adjusts the setting algorithm according to the player's action history, for example. Specific types of setting algorithms include, for example, rule-based and machine learning-based, but are not limited to such examples. For example, detailed settings are made based on the player's past actions. Also, simple settings are made based on the player's past actions. Furthermore, related settings are made based on the player's past actions. This makes it possible to adjust the setting algorithm according to the player's action history and provide more appropriate quests and tasks. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the setting unit can input the player's action data into AI and adjust the setting algorithm.

[0116] The setting unit can estimate the player's emotion and determine the priority of a new quest or task based on the estimated player's emotion. Specific criteria for determining the priority of a new quest or task include, for example, emotion evaluation criteria and a priority determination method, but are not limited to such examples. For example, if the player is excited, a challenging quest is set as a priority. Also, if the player is relaxed, a relaxing task is set as a priority. Furthermore, if the player is stressed, an easy task is set as a priority. In this way, the priority of a new quest or task can be determined according to the player's emotion, and more appropriate quests and tasks can be provided. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the setting unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the setting unit can input the player's emotion data into the generation AI and determine the priority of a new quest or task.

[0117] The setting unit can determine the priority of the settings based on the quest time of the player at the time of setting. Specific criteria for determining the priority of the settings include, for example, evaluation criteria for the quest time and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, the settings related to the quest are given priority. Also, when the player is in a specific event, the settings related to the event are given priority. Furthermore, the priority of the settings is determined based on the quest performed by the player in a specific time period. In this way, the priority of the settings can be determined based on the quest time of the player, and more appropriate quests and tasks can be provided. Part or all of the above-mentioned processing in the setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the setting unit can input the quest time data of the player to AI and determine the priority of the settings.

[0118] The setting unit can adjust the order of the settings based on the player's related actions at the time of setting. Specific criteria for adjusting the order of the settings include, for example, evaluation criteria for related actions and a method for determining the order, but are not limited to such examples. For example, the order of the settings is adjusted based on the player's actions in the past. Also, the order of the settings is adjusted based on the player's actions that the player has not performed in the past. Furthermore, the order of related settings is adjusted based on the player's actions in the past. This makes it possible to adjust the order of the settings based on the player's related actions and provide more appropriate quests and tasks. Part or all of the above-mentioned processing in the setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the setting unit can input the player's action data into AI and adjust the order of the settings.

[0119] The advice unit can estimate the player's emotion and adjust the advice providing method based on the estimated player's emotion. Specific adjustment criteria for the advice providing method include, for example, emotion evaluation criteria and types of providing methods, but are not limited to such examples. For example, if the player is excited, concise and to the point advice is provided. Also, if the player is relaxed, detailed advice is provided. Furthermore, if the player is stressed, advice is provided in a format that visually relaxes the player. This allows the advice providing method to be adjusted according to the player's emotion and more appropriate advice to be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the player's emotion data into the generation AI and adjust the advice providing method.

[0120] The advice unit can adjust the level of detail of advice based on the player's past actions when providing advice. Specific adjustment criteria for the level of detail of advice include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, detailed advice is provided based on actions taken by the player in the past. Also, concise advice is provided based on actions not taken by the player in the past. Furthermore, related advice is provided based on actions taken by the player in the past. This allows the level of detail of advice to be adjusted based on the player's past actions, and more appropriate advice to be provided. Part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the player's action data into AI and adjust the level of detail of advice.

[0121] The advice unit can estimate the player's emotion and determine the priority of advice based on the estimated player's emotion. Specific criteria for determining the priority of advice include, for example, emotion evaluation criteria and a priority determination method, but are not limited to such examples. For example, if the player is excited, concise and to the point advice is provided preferentially. Furthermore, if the player is relaxed, detailed advice is provided preferentially. Furthermore, if the player is stressed, advice is provided preferentially in a visually relaxing format. In this way, the priority of advice can be determined according to the player's emotion and more appropriate advice can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the advice unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the advice unit can input the player's emotion data into the generation AI and determine the priority of advice.

[0122] The advice unit can determine the priority of advice based on the time of the player's action when providing advice. Specific criteria for determining the priority of advice include, for example, evaluation criteria for the time of action and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, advice related to the quest is provided preferentially. Also, when the player is in a specific event, advice related to the event is provided preferentially. Furthermore, the priority of advice is determined based on the action performed by the player in a specific time period. In this way, the priority of advice can be determined based on the time of the player's action, and more appropriate advice can be provided. Part or all of the above-mentioned processing in the advice unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice unit can input the player's action time data into AI and determine the priority of advice.

[0123] The adjustment unit can estimate the player's emotion and adjust the character's behavior based on the estimated player's emotion. Specific adjustment criteria for the character's behavior include, for example, emotion evaluation criteria and types of behavior, but are not limited to such examples. For example, if the player shows friendly emotion, the character also takes friendly behavior. In addition, if the player shows hostile emotion, the character also takes hostile behavior. In addition, if the player shows neutral emotion, the character also takes neutral behavior. This makes it possible to adjust the character's behavior according to the player's emotion and realize a more appropriate character reaction. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the player's emotion data into the generation AI and adjust the character's behavior.

[0124] The adjustment unit can adjust the level of detail of the adjustment based on the player's past actions during the adjustment. Specific adjustment criteria for the level of detail of the adjustment include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, detailed adjustment is performed based on the player's past actions. Also, simple adjustment is performed based on the player's past actions. Furthermore, related adjustment is performed based on the player's past actions. This makes it possible to adjust the level of detail of the adjustment based on the player's past actions and realize a more appropriate character reaction. Part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, AI, or may be performed without using AI. For example, the adjustment unit can input the player's action data into AI and adjust the level of detail of the adjustment.

[0125] The adjustment unit can estimate the player's emotion and determine the priority of the character's actions based on the estimated player's emotion. Specific criteria for determining the priority of the character's actions include, for example, emotion evaluation criteria and a method for determining the priority, but are not limited to such examples. For example, if the player shows friendly emotion, friendly actions are taken preferentially. Also, if the player shows hostile emotion, hostile actions are taken preferentially. Furthermore, if the player shows neutral emotion, neutral actions are taken preferentially. This allows the character's action priority to be determined according to the player's emotion, and a more appropriate character response to be realized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the player's emotion data into the generation AI and determine the priority of the character's actions.

[0126] The adjustment unit can determine the priority of the adjustment based on the time of the player's action during the adjustment. Specific criteria for determining the priority of the adjustment include, for example, evaluation criteria for the time of the action and a method for determining the priority, but are not limited to such examples. For example, if the player is in a specific quest, the adjustment related to the quest is performed preferentially. Also, if the player is in a specific event, the adjustment related to the event is performed preferentially. Furthermore, the priority of the adjustment is determined based on the action taken by the player during a specific time period. This allows the priority of the adjustment to be determined based on the time of the player's action, and a more appropriate character reaction to be realized. A part or all of the above-mentioned processing in the adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the adjustment unit can input the player's action time data into the AI ​​and determine the priority of the adjustment.

[0127] The prediction unit can estimate the player's emotion and adjust the next step prediction method based on the estimated player's emotion. Specific adjustment criteria for the next step prediction method include, for example, emotion evaluation criteria and types of prediction methods, but are not limited to such examples. For example, if the player is excited, the next step is predicted briefly. Furthermore, if the player is relaxed, the next step is predicted in detail. Furthermore, if the player is stressed, the next step is predicted in a visually relaxing format. This allows the next step prediction method to be adjusted according to the player's emotion and more appropriate prediction to be realized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the player's emotion data into the generation AI and adjust the next step prediction method.

[0128] The prediction unit can adjust the level of detail of the prediction based on the player's past actions at the time of prediction. Specific adjustment criteria for the level of detail of the prediction include, for example, evaluation criteria for past actions and types of level of detail, but are not limited to such examples. For example, a detailed prediction is made based on the player's past actions. Also, a concise prediction is made based on the player's past actions. Furthermore, a related prediction is made based on the player's past actions. This makes it possible to adjust the level of detail of the prediction based on the player's past actions and realize a more appropriate prediction. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the player's action data into AI and adjust the level of detail of the prediction.

[0129] The prediction unit can estimate the player's emotion and determine the priority of the next step based on the estimated player's emotion. Specific criteria for determining the priority of the next step include, for example, emotion evaluation criteria and a priority determination method, but are not limited to such examples. For example, if the player is excited, a concise and to-the-point next step is predicted preferentially. Also, if the player is relaxed, a detailed next step is predicted preferentially. Furthermore, if the player is stressed, a next step is predicted preferentially in a visually relaxing format. This allows the priority of the next step to be determined according to the player's emotion, and more appropriate prediction can be realized. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prediction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prediction unit can input the player's emotion data into the generation AI and determine the priority of the next step.

[0130] The prediction unit can determine the priority of prediction based on the time of the player's action at the time of prediction. Specific criteria for determining the priority of prediction include, for example, evaluation criteria for the time of action and a method for determining the priority, but are not limited to such examples. For example, when the player is in a specific quest, the next step related to the quest is predicted with priority. Also, when the player is in a specific event, the next step related to the event is predicted with priority. Furthermore, the priority of the next step is determined based on the action performed by the player in a specific time period. This allows the priority of prediction to be determined based on the time of the player's action, and more appropriate prediction can be realized. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the player's action time data into AI and determine the priority of prediction.

[0131] The health management unit can estimate the player's emotions and adjust the health management suggestion method based on the estimated player's emotions. Specific adjustment criteria for the health management suggestion method include, for example, emotion evaluation criteria and types of suggestion methods, but are not limited to such examples. For example, if the player is excited, a relaxing health management is suggested. Also, if the player is relaxed, healthy activities are suggested. Furthermore, if the player is stressed, a suggestion for stress reduction is made. In this way, the health management suggestion method can be adjusted according to the player's emotions, and more appropriate health management can be realized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the health management unit can input the player's emotion data into the generation AI and adjust the health management suggestion method.

[0132] The health management unit can adjust the level of detail of the proposal based on the player's past health data when proposing health management. Specific adjustment criteria for the level of detail of the proposal include, for example, evaluation criteria for past health data and types of level of detail, but are not limited to such examples. For example, a detailed proposal is made based on the health management performed by the player in the past. Also, a concise proposal is made based on the health management not performed by the player in the past. Furthermore, a related proposal is made based on the health management performed by the player in the past. In this way, the level of detail of the proposal can be adjusted based on the player's past health data, and more appropriate health management can be realized. Part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the player's health data into AI and adjust the level of detail of the proposal.

[0133] The health management unit can estimate the player's emotions and determine the priority of health management based on the estimated player's emotions. Specific criteria for determining the priority of health management include, for example, emotion evaluation criteria and a method for determining the priority, but are not limited to such examples. For example, if the player is excited, a suggestion for health management that allows the player to relax is given priority. Also, if the player is relaxed, a suggestion for healthy activities is given priority. Furthermore, if the player is stressed, a suggestion for stress reduction is given priority. In this way, the priority of health management can be determined according to the player's emotions, and more appropriate health management can be realized. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the health management unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the health management unit can input the player's emotion data into the generation AI and determine the priority of health management.

[0134] The health management unit can determine the priority of the proposal based on the time of acquisition of the player's health data when making a health management proposal. Specific criteria for determining the priority of the proposal include, for example, evaluation criteria for the time of acquisition of the health data and a method for determining the priority, but are not limited to such examples. For example, the latest proposal is given priority based on the health data recently acquired by the player. Also, related proposals are made based on the health data acquired by the player in the past. Furthermore, the priority of the proposal is determined based on the health data acquired by the player in a specific time period. This allows the priority of the proposal to be determined based on the time of acquisition of the player's health data, thereby realizing more appropriate health management. Part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the player's health data into AI and determine the priority of the proposal.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] When accepting an input from a player, the reception unit can adjust the input method in consideration of the player's current health condition. For example, if the player is tired, voice input is preferentially accepted. Also, if the player is relaxed, text input is preferentially accepted. Furthermore, if the player is stressed, gesture input is preferentially accepted. This allows the player to be provided with an optimal input method according to the player's health condition, and to achieve a more comfortable gaming experience. The health condition is estimated, for example, using a method for acquiring health data or an evaluation criterion. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the player's health data into AI and adjust the input method.

[0137] The advice unit can infer the learning style of the player from the content of the player's dialogue and provide appropriate hints and advice. For example, if the player has a visual learning style, visual hints are provided. Also, if the player has an auditory learning style, audio advice is provided. Furthermore, if the player has an experiential learning style, advice through actual game play is provided. In this way, optimal advice according to the player's learning style can be provided, and the game can progress smoothly. The learning style is inferred, for example, using an analysis method or evaluation criteria of the dialogue content. A part or all of the above-mentioned processing in the advice unit may be performed, for example, using AI, or may be performed without using AI. For example, the advice unit can input the dialogue data of the player into AI and infer the learning style.

[0138] When adjusting the character's behavior based on the player's preferences and strategies, the adjustment unit can analyze the player's past gameplay data and select the behavior that best suits the player's play style. For example, if the player has taken an aggressive play style in the past, the character will also take an aggressive action. Furthermore, if the player has taken a defensive play style, the character will also take a defensive action. Furthermore, if the player has taken a balanced play style, the character will also take a balanced action. This provides the optimal character behavior according to the player's play style, and achieves a greater sense of immersion. The analysis of the play style is performed, for example, using evaluation criteria and analysis methods for past gameplay data. Some or all of the above-mentioned processing in the adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the adjustment unit can input the player's gameplay data into AI and adjust the character's behavior.

[0139] When the change unit branches the story of the game based on the player's selection and action, the change unit can analyze the player's past selection data, predict the story development that the player prefers, and provide it. For example, if the story development selected by the player in the past was action-oriented, the change unit provides an action-oriented story development. Furthermore, if the story development selected by the player in the past was drama-oriented, the change unit provides a drama-oriented story development. Furthermore, if the story development selected by the player in the past was mystery-oriented, the change unit provides a mystery-oriented story development. This allows the player to provide an optimal story development according to their preferences, and achieve a greater sense of immersion. The analysis of the selection data is performed, for example, using evaluation criteria and analysis methods for past selection data. A part or all of the above-mentioned processing in the change unit may be performed, for example, using AI, or may be performed without using AI. For example, the change unit can input the player's selection data into AI and adjust the branching of the story.

[0140] When predicting the player's behavior based on the content that the character has learned through the dialogue with the player, the prediction unit can analyze the player's past dialogue data and predict the next action that the player is likely to take. For example, if the player has asked a specific question in the past, the prediction unit predicts an action related to the question. Also, if the player has made a specific choice in the past, the prediction unit predicts an action related to the choice. Furthermore, if the player has taken a specific action in the past, the prediction unit predicts the next step related to the action. This makes it possible to predict the optimal next step based on the player's past dialogue data and smoothly progress through the game. The analysis of the dialogue data is performed, for example, using evaluation criteria and analysis methods for past dialogue data. Part or all of the above-mentioned processing in the prediction unit may be performed, for example, using AI, or may be performed without using AI. For example, the prediction unit can input the player's dialogue data into AI and predict the next step.

[0141] When the health management unit estimates the health state and fatigue level from the content of the player's dialogue, the health management unit can analyze the player's past health data and estimate the player's health state more accurately. For example, if the player felt fatigue in the past, the health management unit estimates the current health state based on the fatigue level. Also, if the player was in a healthy state in the past, the health management unit estimates the current health state based on the health state. Furthermore, if the player had a specific health problem in the past, the health management unit estimates the current health state based on the health problem. This makes it possible to estimate the player's health state more accurately based on the player's past health data and suggest appropriate rest. The analysis of the health data is performed, for example, using evaluation criteria and analysis methods for past health data. Part or all of the above-mentioned processing in the health management unit may be performed, for example, using AI, or may be performed without using AI. For example, the health management unit can input the player's health data into AI and estimate the health state.

[0142] The reception unit can estimate the player's emotion and adjust the timing of accepting questions based on the estimated player's emotion. For example, if the player is excited, the reception of questions is temporarily delayed until the player calms down. Also, if the player is relaxed, the question is immediately accepted to promote smooth dialogue. Furthermore, if the player is stressed, the reception of questions is simplified so that the player does not feel burdened. This makes it possible to realize smooth dialogue according to the player's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the reception unit can input the player's emotion data to the generation AI and adjust the timing of accepting questions.

[0143] The providing unit can estimate the player's emotion and adjust the method of providing background information based on the estimated player's emotion. For example, if the player is excited, concise and to the point background information is provided. If the player is relaxed, detailed background information is provided. If the player is stressed, background information is provided in a visually relaxing format. This allows the method of providing background information to be adjusted according to the player's emotion, and more appropriate information to be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the player's emotion data into the generation AI and adjust the method of providing background information.

[0144] The dialogue unit can estimate the player's emotions and adjust the dialogue content based on the estimated player's emotions. For example, if the player is excited, the dialogue unit provides concise and to the point dialogue content. If the player is relaxed, the dialogue unit provides detailed dialogue content. If the player is stressed, the dialogue unit provides visually relaxing dialogue content. This allows the dialogue content to be adjusted according to the player's emotions and realizes more appropriate dialogue. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input the player's emotion data to the generation AI and adjust the dialogue content.

[0145] The modification unit can estimate the player's emotion and change the character's emotion and attitude based on the estimated player's emotion. For example, if the player shows a friendly emotion, the character also shows a friendly attitude. Moreover, if the player shows a hostile emotion, the character also shows a hostile attitude. Furthermore, if the player shows a neutral emotion, the character also shows a neutral attitude. This allows the character's emotion and attitude to be changed according to the player's emotion, and a more appropriate character reaction to be realized. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the modification unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the modification unit can input the player's emotion data into the generation AI and change the character's emotion and attitude.

[0146] The process flow of the second embodiment will be briefly described below.

[0147] Step 1: The reception unit receives input from a player, including, but not limited to, text input, voice input, and gesture input. Step 2: If the input received by the reception unit is a question for the character, the providing unit provides background information of the character. The background information of the character includes, but is not limited to, the character's past events, personality, relationships, etc. Step 3: The dialogue unit dynamically generates dialogue content with the character based on the player's past actions and past choices. The dialogue content is dynamically generated based on, but not limited to, the type of algorithm and the timing of generation. Step 4: The modification unit modifies the character's emotions and attitudes based on the game situation and the relationship with the player. The emotions and attitudes include, but are not limited to, emotions such as joy, anger, sadness, and attitudes such as friendly and hostile. Step 5: The setting unit sets a new quest or a new task based on the result of the dialogue with the player. The new quest or the new task includes, but is not limited to, a quest goal and a task type.

[0148] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0150] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0151] For example, the reception unit is realized by the reception device 38 of the smart device 14, and receives input from the player using the touch panel 38A or the microphone 38B. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides background information of the character. The dialogue unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates dialogue content based on the player's past actions and selections. The change unit is realized by, for example, the control unit 46A of the smart device 14, and changes the character's emotions and attitudes. The setting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and sets new quests and tasks. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0154] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

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

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0159] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0162] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0164] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0165] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0166] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0167] For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the player. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and provides background information of the character. The dialogue unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates dialogue content based on the player's past actions and selections. The change unit is realized by, for example, the control unit 46A of the smart glasses 214 and changes the character's emotions and attitudes. The setting unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and sets new quests and tasks. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0170] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0175] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0176] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0177] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0178] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0179] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0180] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0181] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0182] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0183] For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and receives voice input from the player. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and provides background information of the character. The dialogue unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and generates dialogue content based on the player's past actions and selections. The change unit is realized by, for example, the control unit 46A of the headset type terminal 314, and changes the character's emotions and attitudes. The setting unit is realized by, for example, the specific processing unit 290 of the data processing device 12, and sets new quests and tasks. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 7, a 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.

[0186] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0187] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0188] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0189] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0190] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0191] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0192] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0193] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0194] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0195] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0196] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0197] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0198] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0199] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0200] For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the player. The provision unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and provides background information of the character. The dialogue unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and generates dialogue content based on the player's past actions and selections. The change unit is realized by, for example, the control unit 46A of the robot 414 and changes the character's emotions and attitudes. The setting unit is realized by, for example, the specific processing unit 290 of the data processing device 12 and sets new quests and tasks. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0201] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0202] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0203] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0204] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0205] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0206] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0207] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0208] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0209] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0210] In addition, 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 upon request from the data processing device 12.

[0211] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0212] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0213] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0214] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0215] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0216] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0217] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0218] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0219] (Appendix 1) A reception unit that receives input from a player; a providing unit that provides background information of the character when the input received by the receiving unit is a question for the character; A dialogue unit that dynamically generates dialogue content with the character based on the player's past actions and past choices; A modification unit that modifies the character's emotions and attitudes based on the game situation and the relationship with the player; A setting unit that sets a new quest or a new task based on a dialogue result with the player. A system characterized by: (Appendix 2) Equipping the player with an advice section that provides specific hints or advice 2. The system of claim 1, (Appendix 3) Equipped with an adjustment section that adjusts character behavior based on player preferences and strategies 2. The system of claim 1, (Appendix 4) The change unit is Branching game storylines based on player choices and actions, and dynamically changing character reactions and behaviors based on the branching storylines 2. The system of claim 1, (Appendix 5) Equipped with a prediction section that predicts the player's actions and suggests the next step based on what the character has learned through dialogue with the player. 2. The system of claim 1, (Appendix 6) Equipped with a health management section that predicts the player's health condition and fatigue level from the content of their dialogue and suggests appropriate breaks 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the player's emotions and adjust the timing of accepting questions based on the estimated player's emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the player's past question history and choose the most appropriate method of accepting the question 2. The system of claim 1, (Appendix 9) The reception unit is When questions are asked, they are filtered based on the player's current game progress. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the player's emotions and prioritize questions based on the estimated player's emotions 2. The system of claim 1, (Appendix 11) The reception unit is When answering questions, we take into account the player's geographic location to prioritize questions that are more relevant to them. 2. The system of claim 1, (Appendix 12) The reception unit is When a question is submitted, analyze the player's social media activity and submit relevant questions to them. 2. The system of claim 1, (Appendix 13) The providing unit is Infer the player's emotions and adjust how contextual information is presented based on the inferred player emotions. 2. The system of claim 1, (Appendix 14) The providing unit is When providing background information, adjust the level of detail provided based on the importance of the character. 2. The system of claim 1, (Appendix 15) The providing unit is When providing background information, different algorithms are applied depending on the character category. 2. The system of claim 1, (Appendix 16) The providing unit is Infer the player's emotions and adjust the length of the background information based on the estimated player's emotions 2. The system of claim 1, (Appendix 17) The providing unit is When providing background information, prioritize it based on when the character first appeared. 2. The system of claim 1, (Appendix 18) The providing unit is When providing background information, adjust the order of information provided based on the relevance of the character 2. The system of claim 1, (Appendix 19) The dialogue unit includes: Estimate the player's emotions and adjust the dialogue based on the estimated player's emotions 2. The system of claim 1, (Appendix 20) The dialogue unit includes: During interactions, adjust the level of detail of the interaction based on the player's previous choices 2. The system of claim 1, (Appendix 21) The dialogue unit includes: During dialogue, different dialogue algorithms are applied depending on the player's behavior history. 2. The system of claim 1, (Appendix 22) The dialogue unit includes: Estimate the player's emotions and adjust the length of the dialogue based on the estimated player's emotions 2. The system of claim 1, (Appendix 23) The dialogue unit includes: During interactions, prioritize interactions based on when the player makes a choice 2. The system of claim 1, (Appendix 24) The dialogue unit includes: During interactions, adjust the order of interactions based on the player's relevant actions 2. The system of claim 1, (Appendix 25) The change unit is Inferring the player's emotions and changing the character's emotions and attitude based on the inferred player's emotions 2. The system of claim 1, (Appendix 26) The change unit is When making changes, adjust the granularity of the changes based on the player's past behavior 2. The system of claim 1, (Appendix 27) The change unit is When changing, apply different change algorithms depending on the player's relationship 2. The system of claim 1, (Appendix 28) The change unit is Inferring a player's emotions and adjusting the frequency of changes in the character's emotions and attitudes based on the inferred player's emotions. 2. The system of claim 1, (Appendix 29) The change unit is When changes are made, prioritize them based on when players behave 2. The system of claim 1, (Appendix 30) The change unit is When changes occur, adjust the order of changes based on the relevant player actions. 2. The system of claim 1, (Appendix 31) The setting unit is Estimate player emotions and adjust how new quests or tasks are set up based on the estimated player emotions 2. The system of claim 1, (Appendix 32) The setting unit is During setup, adjust the granularity of the setup based on the player's past quest history 2. The system of claim 1, (Appendix 33) The setting unit is During configuration, different configuration algorithms are applied depending on the player's behavior history. 2. The system of claim 1, (Appendix 34) The setting unit is Estimate player emotions and prioritize new quests or tasks based on the estimated player emotions 2. The system of claim 1, (Appendix 35) The setting unit is During setup, settings are prioritized based on when the player is on a quest. 2. The system of claim 1, (Appendix 36) The setting unit is During configuration, adjust the order of configurations based on the player's relevant behavior 2. The system of claim 1, (Appendix 37) The advice unit is Inferring a player's emotions and tailoring advice delivery based on the inferred player emotions 3. The system of claim 2, (Appendix 38) The advice unit is When giving advice, adjust the detail of the advice based on the player's past actions 3. The system of claim 2, (Appendix 39) The advice unit is Estimate the player's emotions and prioritize advice based on the estimated player's emotions 3. The system of claim 2, (Appendix 40) The advice unit is When giving advice, prioritize advice based on when the player acts 3. The system of claim 2, (Appendix 41) The adjustment unit is Estimate the player's emotions and adjust the character's behavior based on the estimated player's emotions 4. The system of claim 3. (Appendix 42) The adjustment unit is When tuning, adjust the granularity of tuning based on the player's past behavior 4. The system of claim 3. (Appendix 43) The adjustment unit is Estimate the player's emotions and prioritize character actions based on the estimated player's emotions 4. The system of claim 3. (Appendix 44) The adjustment unit is During tuning, prioritize tuning based on when players behave 4. The system of claim 3. (Appendix 45) The prediction unit is Estimate player sentiment and adjust how you predict next steps based on the estimated player sentiment 6. The system of claim 5, (Appendix 46) The prediction unit is When making predictions, adjust the granularity of your predictions based on the player's past actions. 6. The system of claim 5, (Appendix 47) The prediction unit is Estimate player sentiment and prioritize next steps based on estimated player sentiment 6. The system of claim 5, (Appendix 48) The prediction unit is When making predictions, prioritize predictions based on when players will act. 6. The system of claim 5, (Appendix 49) The health management unit includes: Estimate the player's emotions and adjust the health care suggestions based on the estimated player's emotions. 7. The system of claim 6, (Appendix 50) The health management unit includes: When making health management suggestions, the level of detail of the suggestions will be adjusted based on the player's past health data. 7. The system of claim 6, (Appendix 51) The health management unit includes: Estimate the player's emotions and prioritize health care based on the estimated player's emotions. 7. The system of claim 6, (Appendix 52) The health management unit includes: When making health suggestions, we will prioritize them based on when the player's health data was obtained. 7. The system of claim 6, [Explanation of symbols]

[0220] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that receives input from a player; a providing unit that provides background information of the character when the input received by the receiving unit is a question for the character; A dialogue unit that dynamically generates dialogue content with the character based on the player's past actions and past choices; A modification unit that modifies the character's emotions and attitudes based on the game situation and the relationship with the player; A setting unit that sets a new quest or a new task based on a dialogue result with the player. A system characterized by:

2. Equipping the player with an advice section that provides specific hints or advice 2. The system of claim 1 .

3. Equipped with an adjustment section that adjusts character behavior based on player preferences and strategies 2. The system of claim 1 .

4. The change unit is Branching game storylines based on player choices and actions, and dynamically changing character reactions and behaviors based on the branching storylines 2. The system of claim 1 .

5. Equipped with a prediction section that predicts the player's actions and suggests the next step based on what the character has learned through dialogue with the player.

2. The system of claim 1 .

6. Equipped with a health management section that predicts the player's health condition and fatigue level from the content of their dialogue and suggests appropriate breaks 2. The system of claim 1 .

7. The reception unit is Estimate the player's emotions and adjust the timing of accepting questions based on the estimated player's emotions 2. The system of claim 1 .

8. The reception unit is Analyze the player's past question history and choose the most appropriate method of accepting the question 2. The system of claim 1 .

9. The reception unit is When questions are asked, they are filtered based on the player's current game progress.

2. The system of claim 1 .

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