System

Abstract

To provide a more realistic game experience by determining the behavior of a game character on the basis of a player's emotion.SOLUTION: A system according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating a game player's emotion. The estimation unit estimates a player's emotion on the basis of the emotion estimation information acquired by the acquisition unit. The determination unit determines the behavior of a game character on the basis of the player's emotion estimated by the estimation unit. The execution unit executes the behavior of the character determined by the determination unit.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 techniques do not adequately adjust character behavior based on the emotions of the game player, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a more realistic gaming experience by determining the actions of a game character based on the emotions of the player. [Means for solving the problem]

[0006] A system according to an embodiment includes an acquisition unit, an estimation unit, a determination unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating an emotion of a game player. The estimation unit estimates the emotion of the player based on the emotion estimation information acquired by the acquisition unit. The determination unit determines an action of a game character based on the emotion of the player estimated by the estimation unit. The execution unit executes the action of the character determined by the determination unit. Effect of the Invention

[0007] The system according to the embodiment determines the actions of a game character based on the player's emotions, thereby providing a more realistic gaming experience. [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 agent system with an emotion engine according to the embodiment of the present invention is a system that is utilized as an AI character of a game. This system acquires information for estimating the player's emotions, and determines and executes the character's actions based on the emotions. For example, information such as the player's face image, voice, and game operation is acquired, and the player's emotions are estimated based on this information. Based on the estimated emotions, the behavior of the game character is determined, and for example, if the player is happy, the character also takes an action such as showing a happy expression. Furthermore, it is possible to adjust the game scenario and difficulty level based on the estimated player's emotions. For example, if the player is feeling stressed, the game difficulty level is lowered to reduce the player's stress. It is also possible to adjust the character's actions more finely based on the player's emotions and attributes. For example, the character's reaction can be changed depending on whether the player is young or old. This system can provide a game experience that corresponds to the player's emotions, realizing a more immersive game play. As a result, the agent system with an emotion engine can provide a more realistic game experience by determining and executing the character's actions based on the player's emotions.

[0029] The agent system having an emotion engine according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a player. The emotion estimation information includes, for example, an image of the player's face, a voice, game operation data, and the like, but is not limited to such examples. For example, the acquisition unit acquires an image of the player's face using a camera, and acquires the player's voice using a microphone. The acquisition unit can also acquire input data from a game controller or a keyboard. The estimation unit estimates the player's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the facial expression and tone of the voice to estimate the player's emotion. The estimation unit can estimate emotions such as joy, sadness, and anger from the facial expression. The estimation unit can also estimate the player's emotion from the tone and speed of the voice. The determination unit determines the action of a game character based on the player's emotion estimated by the estimation unit. For example, if the player is happy, the determination unit determines an action that causes the character to show a happy expression. Furthermore, the determination unit can also determine an action for the character to comfort the player when the player is sad. The execution unit executes the action of the character determined by the determination unit. The execution unit, for example, controls animation of the character to execute the determined action. The execution unit can also control the voice of the character to utter the determined lines. In this way, the agent system having an emotion engine according to the embodiment can provide a more realistic gaming experience by determining and executing the action of the character based on the emotion of the player.

[0030] The acquisition unit acquires emotion estimation information for estimating the emotion of the player. The emotion estimation information includes, for example, an image of the player's face, voice, game operation data, etc., but is not limited to such examples. For example, the acquisition unit acquires an image of the player's face using a camera, and acquires the voice of the player using a microphone. Specifically, the camera can capture high-resolution video in real time and capture subtle changes in the player's facial expression. The microphone collects voice features such as the tone, volume, and speed of the player's voice with high accuracy. The acquisition unit can also acquire input data from a game controller or a keyboard. This makes it possible to grasp in detail the player's behavioral patterns such as the speed, strength, and frequency of operation. Furthermore, the acquisition unit has a function of centrally managing these data and transmitting them to the analysis unit in real time. For example, the camera video and voice data can be efficiently transmitted using compression technology, minimizing delays. This allows the acquisition unit to quickly and accurately collect a variety of emotion estimation information of the player, and improve the emotion estimation accuracy of the entire system.

[0031] The estimation unit estimates the player's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze facial expressions and voice tones to estimate the player's emotions. Specifically, the AI ​​uses deep learning technology to estimate emotions such as joy, sadness, and anger from facial expressions with high accuracy. For example, it analyzes facial muscle movements, eye opening and closing, and the degree to which the corners of the mouth are turned up to determine the type and intensity of the emotion. It is also possible to estimate the player's emotions from the tone and speed of the voice. In voice analysis, the pitch, intonation, and rhythm of the voice are analyzed to capture changes in emotions. Furthermore, the estimation unit also analyzes game operation data. For example, it is possible to estimate the degree of tension or excitement from the frequency with which the player presses the game controller hard or a sudden increase in operations. As a result, the estimation unit can comprehensively analyze multiple emotion estimation information and estimate the player's emotions from multiple angles. Furthermore, the estimation unit can learn past data and individual characteristics of the player to perform emotion estimation optimized for each individual player. This enables the estimation unit to estimate the player's emotions with high accuracy, improving the responsiveness of the entire system.

[0032] The determination unit determines the behavior of the game character based on the player's emotions estimated by the estimation unit. For example, when the player is happy, the determination unit determines an action that causes the character to show a happy expression. Specifically, the determination unit selects the optimal action by comprehensively considering the character's expressions, actions, lines, etc. For example, when the player is happy, the character can combine actions such as smiling and waving and jumping. In addition, the determination unit can also determine an action that the character will take to comfort the player when the player is sad. For example, the character selects an action that is in line with the player's emotions, such as speaking to the player in a gentle voice or placing a hand on the player's shoulder. Furthermore, the determination unit can adjust the progress and difficulty of the game according to changes in the player's emotions. For example, when the player is feeling stressed, the difficulty of the game can be temporarily lowered to reduce the burden on the player. In this way, the determination unit can flexibly and appropriately determine an action based on the player's emotions, thereby enriching the game experience.

[0033] The execution unit executes the character's action determined by the determination unit. The execution unit, for example, controls the animation of the character and executes the determined action. Specifically, the execution unit uses a skeletal model of the character to generate animation for reproducing realistic movement. For example, when the character smiles, the movement of the facial muscles is finely controlled to create a natural expression. The execution unit can also control the voice of the character and make the character speak the determined lines. Using voice synthesis technology, the tone and intonation of the character's voice are adjusted to realize speech according to the emotion. Furthermore, the execution unit enables more realistic expression by synchronizing the character's movement and voice. For example, when the character waves his / her hand while talking, natural communication is realized by matching the timing of the hand movement and the voice. As a result, the execution unit can execute the action determined by the determination unit with high accuracy and provide the player with a realistic game experience. Furthermore, the execution unit can transmit the result of the executed action to the acquisition unit and the estimation unit as feedback, and continuously improve the accuracy of the entire system. As a result, the execution unit can quickly and accurately execute an action based on the player's emotion, and enrich the game experience.

[0034] The determination unit can select a behavior pattern corresponding to the player's emotion as the character's behavior. For example, when the player is happy, the determination unit selects a behavior pattern in which the character shows a happy expression. In addition, when the player is sad, the determination unit can also select a behavior pattern in which the character comforts the player. Furthermore, when the player is angry, the determination unit can also select a behavior pattern in which the character responds calmly. In this way, by selecting a behavior pattern according to the player's emotion, the character's behavior becomes more appropriate. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's emotion data to the generation AI and cause the generation AI to select a behavior pattern corresponding to the emotion.

[0035] The estimation unit may include a scenario adjustment unit that adjusts the game scenario based on the player's emotions. For example, the scenario adjustment unit adds a positive development to the scenario when the player is happy. The scenario adjustment unit may also add a comforting element to the scenario when the player is sad. Furthermore, the scenario adjustment unit may also add an element to encourage a calm response to the scenario when the player is angry. This allows the game experience to be more personalized by adjusting the scenario based on the player's emotions. A part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the scenario adjustment unit may input the player's emotion data to the generation AI and cause the generation AI to determine how to adjust the scenario based on the emotion.

[0036] The estimation unit may include a difficulty adjustment unit that adjusts the difficulty of the game based on the player's emotions. For example, the difficulty adjustment unit may lower the difficulty of the game when the player is stressed. The difficulty adjustment unit may also maintain the difficulty of the game when the player is relaxed. Furthermore, the difficulty adjustment unit may also increase the difficulty of the game when the player is excited. This allows the stress of the game to be reduced by adjusting the difficulty based on the player's emotions. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the difficulty adjustment unit may input the player's emotion data to the generation AI and cause the generation AI to determine a method of adjusting the difficulty based on the emotion.

[0037] The determination unit can determine the character's behavior based on the player's emotions and the player's attributes. For example, if the player is young, the determination unit can cause the character to take an active action. Also, if the player is older, the determination unit can cause the character to take a calm action. Furthermore, the determination unit can adjust the character's behavior based on the player's gender and game experience. As a result, the character's behavior becomes more appropriate by determining the behavior based on the player's emotions and attributes. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the player's emotion data and attribute data to the generation AI and cause the generation AI to determine a method for determining the behavior based on the emotion and attribute.

[0038] The acquisition unit can acquire at least one of the player's face image, voice, and game operation as emotion estimation information. The acquisition unit can acquire the player's face image using, for example, a camera. The acquisition unit can also acquire the player's voice using a microphone. Furthermore, the acquisition unit can acquire input data from a game controller or a keyboard. This improves the accuracy of emotion estimation by acquiring emotion estimation information from various information sources. A part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the acquisition unit can input face image data acquired by a camera to the generation AI and cause the generation AI to execute analysis as emotion estimation information.

[0039] The acquisition unit can acquire one or more pieces of information among the player's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content as emotion estimation information. The acquisition unit acquires the player's facial expression using, for example, a camera. The acquisition unit can also acquire the player's movement using a movement sensor. Furthermore, the acquisition unit can also acquire the player's heart rate using a heart rate sensor. This improves the accuracy of emotion estimation by acquiring various physiological and behavioral data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input facial expression data acquired by a camera to the generation AI and cause the generation AI to execute analysis as emotion estimation information.

[0040] The acquisition unit can estimate the player's emotion and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the player. For example, when the player is excited, the acquisition unit increases the frequency of acquiring the emotion estimation information and tracks changes in the emotion in real time. In addition, when the player is relaxed, the acquisition unit can reduce the frequency of acquiring the emotion estimation information to save resources. Furthermore, when the player is stressed, the acquisition unit can adjust the timing of acquiring the emotion estimation information and collect data for identifying the cause of the stress. This allows for efficient use of resources by adjusting the acquisition timing according to the player's emotion. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the player's emotion data to the generation AI and cause the generation AI to determine a method of adjusting the acquisition timing based on the emotion.

[0041] The acquisition unit can analyze the player's past emotion data and select the optimal acquisition method. For example, the acquisition unit identifies a time period in which the player's emotions fluctuated greatly in the past, and acquires emotion estimation information focusing on that time period. In addition, if the player's emotions were heightened in a specific game event in the past, the acquisition unit can also intensively acquire emotion estimation information during that event. Furthermore, the acquisition unit can reduce the acquisition frequency during a time period in which emotions fluctuate little based on the player's past emotion data. This improves the accuracy of emotion estimation by selecting the optimal acquisition method based on the past emotion data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the player's past emotion data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0042] The acquisition unit can perform filtering based on the player's current game progress and play style when acquiring the emotion estimation information. For example, when the player is fighting a boss, the acquisition unit increases the frequency of acquiring emotion estimation information and records the degree of tension and excitement in detail. In addition, when the player is in a search mode, the acquisition unit can also reduce the frequency of acquiring emotion estimation information to maintain a relaxed state. Furthermore, when the player is in a battle mode, the acquisition unit can adjust the frequency of acquiring emotion estimation information to measure the degree of competitiveness and stress. As a result, filtering is performed based on the game progress and play style, improving the accuracy of emotion estimation. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the player's game progress and play style data to the generation AI and cause the generation AI to execute filtering.

[0043] The acquisition unit can estimate the player's emotion and determine the priority of emotion estimation information to be acquired based on the estimated player's emotion. For example, when the player is excited, the acquisition unit preferentially acquires physiological data such as heart rate and pupil reaction. In addition, when the player is relaxed, the acquisition unit can also preferentially acquire external data such as facial expressions and voice. Furthermore, when the player is stressed, the acquisition unit can also preferentially acquire stress-related data such as breathing and blood pressure. In this way, important information can be preferentially acquired by determining the priority of information based on emotion. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the player's emotion data to the generation AI and cause the generation AI to determine the priority.

[0044] When acquiring the emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the player. For example, when the player is at home, the acquisition unit acquires emotion estimation information on the assumption that the player is in a relaxed state. In addition, when the player is in a public place, the acquisition unit can also acquire emotion estimation information in consideration of the surrounding environmental sounds and background noise. Furthermore, when the player is at a specific event venue, the acquisition unit can also preferentially acquire emotion estimation information related to the event. In this way, highly relevant information can be preferentially acquired by considering the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI or may be performed without using an AI. For example, the acquisition unit inputs the geographical location information of the player to the generation AI and causes the generation AI to select highly relevant information.

[0045] The acquisition unit can analyze the social media activity of the player when acquiring the emotion estimation information, and acquire related information. For example, when the player posts positively on social media, the acquisition unit acquires emotion estimation information reflecting the emotion. In addition, when the player posts negatively, the acquisition unit can also acquire emotion estimation information reflecting the emotion. Furthermore, when the player posts about a specific game, the acquisition unit can acquire emotion estimation information based on the content of the post. In this way, information related to the player's emotion can be acquired by analyzing the social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the social media activity data of the player to the generation AI and cause the generation AI to acquire related information.

[0046] The estimation unit can estimate the player's emotion and adjust the estimation algorithm based on the estimated player's emotion. For example, when the player is excited, the estimation unit increases the sensitivity of the emotion estimation algorithm to capture subtle changes in emotion. Also, when the player is relaxed, the estimation unit can lower the sensitivity of the emotion estimation algorithm to maintain a stable emotional state. Furthermore, when the player is stressed, the estimation unit can adjust the emotion estimation algorithm and identify the cause of the stress. As a result, the accuracy of emotion estimation is improved by adjusting the estimation algorithm based on the emotion. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI. For example, the estimation unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the estimation algorithm.

[0047] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. For example, when physiological data such as heart rate and pupil reaction are important, the estimation unit performs detailed emotion estimation based on the data. In addition, when external data such as facial expression and voice are important, the estimation unit can also perform detailed emotion estimation based on the data. Furthermore, when stress-related data such as respiration and blood pressure are important, the estimation unit can also perform detailed emotion estimation based on the data. Thereby, by adjusting the level of detail based on the importance, the accuracy of emotion estimation is improved. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the importance data of the emotion estimation information to the generation AI and cause the generation AI to adjust the level of detail.

[0048] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. For example, the estimation unit applies an algorithm based on heart rate or pupil reaction to physiological data. The estimation unit can also apply an algorithm based on facial expression or voice to external data. Furthermore, the estimation unit can apply an algorithm based on respiration or blood pressure to stress-related data. This improves the accuracy of emotion estimation by applying an algorithm depending on the category. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input category data of the emotion estimation information to the generation AI and cause the generation AI to determine the algorithm to be applied.

[0049] The estimation unit can estimate the player's emotions and adjust the display method of the estimated result based on the estimated player's emotions. For example, when the player is nervous, the estimation unit provides a simple and highly visible display method. Furthermore, when the player is relaxed, the estimation unit can also provide a display method including detailed information. Furthermore, when the player is in a hurry, the estimation unit can also provide a display method that focuses on the main points. This allows the display to be easily viewed by the player by adjusting the display method based on the emotions. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0050] The estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit preferentially uses the latest emotion estimation information to perform emotion estimation in real time. The estimation unit can also perform estimation taking into account long-term emotion fluctuations by referring to past emotion estimation information. Furthermore, the estimation unit can preferentially use emotion estimation information acquired during a specific event to estimate an emotion related to the event. This enables emotion estimation in real time by determining the priority based on the acquisition time. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input acquisition time data of the emotion estimation information to the generation AI and have the generation AI determine the priority.

[0051] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation unit preferentially uses data that is most relevant to the player's current emotional state. The estimation unit can also preferentially use highly relevant data by referring to the player's past emotion data. Furthermore, the estimation unit can also preferentially use highly relevant data based on the player's game progress. As a result, the accuracy of emotion estimation is improved by adjusting the order based on the relevance. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the relevance data of the emotion estimation information to the generation AI and cause the generation AI to determine the order of estimation.

[0052] The determination unit can estimate the player's emotions and adjust the character's behavior pattern based on the estimated player's emotions. For example, when the player is happy, the determination unit causes the character to show a happy expression. In addition, when the player is sad, the determination unit can also cause the character to take an action to comfort the player. Furthermore, when the player is angry, the determination unit can also cause the character to take an action to calmly respond. In this way, the behavior pattern is adjusted based on emotions, making the character's behavior more appropriate. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the behavior pattern.

[0053] At the time of determination, the determination unit can select an optimal behavior pattern by analyzing the player's past behavior data. The determination unit, for example, determines the character's behavior based on behavior patterns that the player enjoyed in the past. The determination unit can also determine the character's behavior so as to avoid behavior patterns that the player disliked in the past. Furthermore, the determination unit can analyze the player's past behavior data and select the most effective behavior pattern. As a result, the character's behavior becomes more appropriate by selecting an optimal behavior pattern based on the past behavior data. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's past behavior data to the generation AI and cause the generation AI to select the optimal behavior pattern.

[0054] At the time of the determination, the determination unit can customize the behavior pattern based on the player's current game progress. For example, when the player is fighting a boss, the determination unit can cause the character to take an encouraging action. In addition, when the player is in an exploration mode, the determination unit can also cause the character to take a relaxed action. Furthermore, when the player is in a battle mode, the determination unit can cause the character to take an action that stimulates competitive spirit. This makes the character's behavior more appropriate by customizing the behavior pattern based on the game progress. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the determination unit can input the player's game progress data to the generation AI and cause the generation AI to execute customization of the behavior pattern.

[0055] The determination unit can estimate the player's emotions and determine the priority of the character's actions based on the estimated player's emotions. For example, when the player is happy, the determination unit prioritizes the character's happy actions. In addition, when the player is sad, the determination unit can also prioritize the character's comforting actions. Furthermore, when the player is angry, the determination unit can also prioritize the character's calm response. In this way, by determining the priority of actions based on emotions, the character's actions become more appropriate. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's emotion data to the generation AI and have the generation AI determine the priority of actions.

[0056] The determination unit can select the optimal behavior pattern by considering the geographical location information of the player at the time of determination. For example, when the player is at home, the determination unit selects a relaxed behavior pattern. In addition, when the player is in a public place, the determination unit can also select a behavior pattern that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the determination unit can also select a behavior pattern related to the event. In this way, the optimal behavior pattern can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the determination unit inputs the geographical location information of the player to the generation AI and causes the generation AI to select the optimal behavior pattern.

[0057] At the time of the determination, the determination unit can analyze the social media activity of the player and propose a behavior pattern. For example, when the player posts positively on social media, the determination unit can propose a behavior pattern that reflects the player's emotions. In addition, when the player posts negatively, the determination unit can also propose a behavior pattern that reflects the player's emotions. Furthermore, when the player posts about a specific game, the determination unit can also propose a behavior pattern based on the content of the post. In this way, a behavior pattern related to the player's emotions can be proposed by analyzing the social media activity. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI or may be performed without using an AI. For example, the determination unit can input the player's social media activity data to the generation AI and cause the generation AI to execute the proposal of a behavior pattern.

[0058] The execution unit can estimate the player's emotions and adjust the character's action execution method based on the estimated player's emotions. For example, when the player is happy, the execution unit can make the character speak in a bright voice. Furthermore, when the player is sad, the execution unit can make the character speak in a gentle voice. Furthermore, when the player is angry, the execution unit can make the character speak in a calm voice. In this way, the character's actions become more appropriate by adjusting the execution method based on emotions. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the action execution method.

[0059] The execution unit can select the optimal execution method by referring to the player's past behavior data at the time of execution. The execution unit executes the character's behavior, for example, based on behavior patterns that the player enjoyed in the past. The execution unit can also execute the character's behavior so as to avoid behavior patterns that the player disliked in the past. Furthermore, the execution unit can analyze the player's past behavior data and execute the most effective behavior pattern. As a result, the character's behavior becomes more appropriate by selecting the optimal execution method based on the past behavior data. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit inputs the player's past behavior data to the generation AI and causes the generation AI to select the optimal execution method.

[0060] The execution unit can customize the execution method based on the player's current game progress at the time of execution. For example, when the player is fighting a boss, the execution unit causes the character to perform an encouraging action. In addition, when the player is in an exploration mode, the execution unit can also cause the character to perform a relaxing action. Furthermore, when the player is in a battle mode, the execution unit can also cause the character to perform an action that stimulates competitive spirit. In this way, by customizing the execution method based on the game progress, the character's actions become more appropriate. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the execution unit can input the player's game progress data to the generation AI and cause the generation AI to execute the customization of the execution method.

[0061] The execution unit can estimate the player's emotions and determine the priority of the character's action execution based on the estimated player's emotions. For example, when the player is happy, the execution unit can prioritize the character's action of joy. In addition, when the player is sad, the execution unit can also prioritize the character's action of comfort. Furthermore, when the player is angry, the execution unit can also prioritize the character's calm response. In this way, by determining the priority of execution based on emotions, the character's actions become more appropriate. Part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the player's emotion data to the generation AI and have the generation AI determine the priority of action execution.

[0062] The execution unit can select the optimal execution method in consideration of the geographical location information of the player at the time of execution. For example, when the player is at home, the execution unit executes a relaxed behavior pattern. In addition, when the player is in a public place, the execution unit can also execute a behavior pattern that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the execution unit can also execute a behavior pattern related to the event. In this way, the optimal execution method can be selected by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the execution unit inputs the geographical location information of the player to the generation AI and causes the generation AI to select the optimal execution method.

[0063] The execution unit can analyze the social media activity of the player at the time of execution and suggest an execution method. For example, when the player posts positively on social media, the execution unit executes an action pattern that reflects the player's emotions. In addition, when the player posts negatively, the execution unit can also execute an action pattern that reflects the player's emotions. Furthermore, when the player posts about a specific game, the execution unit can execute an action pattern based on the content of the post. In this way, by analyzing the social media activity, an execution method related to the player's emotions can be suggested. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the player's social media activity data to the generation AI and cause the generation AI to execute the proposed execution method.

[0064] The scenario adjustment unit can estimate the player's emotions and determine how to adjust the scenario based on the estimated player's emotions. For example, if the player is happy, the scenario adjustment unit can add a positive development to the scenario. Also, if the player is sad, the scenario adjustment unit can add an element of comfort to the scenario. Furthermore, if the player is angry, the scenario adjustment unit can add an element that encourages a calm response to the scenario. In this way, by determining how to adjust the scenario based on emotions, it is possible to provide an optimal scenario for the player. Part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the scenario adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine how to adjust the scenario.

[0065] The scenario adjustment unit can select an optimal scenario by referring to the player's past gameplay data when adjusting the scenario. The scenario adjustment unit adjusts the current scenario based on, for example, a scenario development that the player enjoyed in the past. The scenario adjustment unit can also adjust the current scenario so as to avoid a scenario development that the player disliked in the past. Furthermore, the scenario adjustment unit can analyze the player's past gameplay data and select the most effective scenario. In this way, the optimal scenario can be provided to the player by selecting an optimal scenario based on the past gameplay data. A part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the scenario adjustment unit inputs the player's past gameplay data to the generation AI and causes the generation AI to select an optimal scenario.

[0066] The scenario adjustment unit can estimate the player's emotions and determine the priority of scenarios based on the estimated player's emotions. For example, if the player is happy, the scenario adjustment unit prioritizes a positive scenario development. Furthermore, if the player is sad, the scenario adjustment unit can also prioritize a comforting scenario development. Furthermore, if the player is angry, the scenario adjustment unit can also prioritize a scenario development that encourages a calm response. In this way, by determining the priority of scenarios based on emotions, it is possible to provide an optimal scenario for the player. Part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the scenario adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine the priority of scenarios.

[0067] The scenario adjustment unit can select an optimal scenario by considering the geographical location information of the player when adjusting the scenario. For example, when the player is at home, the scenario adjustment unit selects a relaxed scenario development. In addition, when the player is in a public place, the scenario adjustment unit can also select a scenario development that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the scenario adjustment unit can also select a scenario development related to the event. In this way, the optimal scenario can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the scenario adjustment unit can input the geographical location information of the player to the generation AI and cause the generation AI to select the optimal scenario.

[0068] The difficulty adjustment unit can estimate the player's emotions and determine a difficulty adjustment method based on the estimated player's emotions. For example, the difficulty adjustment unit can lower the difficulty of the game when the player is stressed. The difficulty adjustment unit can also maintain the difficulty of the game when the player is relaxed. Furthermore, the difficulty adjustment unit can also increase the difficulty of the game when the player is excited. In this way, by determining the difficulty adjustment method based on emotions, it is possible to provide an optimal difficulty level for the player. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the difficulty adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine the difficulty adjustment method.

[0069] The difficulty adjustment unit can select the optimal difficulty level by referring to the player's past gameplay data when adjusting the difficulty level. The difficulty adjustment unit adjusts the current difficulty level based on, for example, a difficulty level that the player has cleared in the past. The difficulty adjustment unit can also adjust the current difficulty level so as to avoid a difficulty level that the player has had difficulty with in the past. Furthermore, the difficulty adjustment unit can analyze the player's past gameplay data and select the most effective difficulty level. In this way, the optimal difficulty level can be selected based on the past gameplay data, thereby providing the player with an optimal difficulty level. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the difficulty adjustment unit can input the player's past gameplay data to the generation AI and cause the generation AI to select the optimal difficulty level.

[0070] The difficulty adjustment unit can estimate the player's emotions and determine the priority order of the difficulty levels based on the estimated player's emotions. For example, when the player is feeling stressed, the difficulty adjustment unit can prioritize lowering the difficulty level. Furthermore, when the player is relaxed, the difficulty adjustment unit can also prioritize maintaining the difficulty level. Furthermore, when the player is excited, the difficulty adjustment unit can also prioritize increasing the difficulty level. In this way, by determining the priority order of the difficulty levels based on emotions, it is possible to provide an optimal difficulty level for the player. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the difficulty adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine the priority order of the difficulty levels.

[0071] The difficulty adjustment unit can select the optimal difficulty level by considering the geographical location information of the player when adjusting the difficulty level. For example, when the player is at home, the difficulty adjustment unit selects a relaxed difficulty level. In addition, when the player is in a public place, the difficulty adjustment unit can also select a difficulty level that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the difficulty adjustment unit can also select a difficulty level related to the event. In this way, the optimal difficulty level can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the difficulty adjustment unit can input the geographical location information of the player to the generation AI and cause the generation AI to select the optimal difficulty level.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The acquisition unit can monitor the player's physiological data in real time and issue a warning if an abnormality is detected. For example, if the heart rate rises sharply, a warning is displayed urging the player to take a break. Also, if blood pressure is abnormally high, the game can be paused and a recommendation made to contact a medical institution. Furthermore, the acquisition unit can analyze the player's brainwave data and provide a guide for relaxing if excessive stress is detected. This allows the player's health condition to be monitored in real time and appropriate measures to be taken.

[0074] When acquiring the emotion estimation information of the player, the acquisition unit can analyze the player's environmental sound to improve the accuracy of emotion estimation. For example, when the player is in a quiet environment, the accuracy of acquiring the emotion estimation information is improved. Also, when the player is in a noisy environment, the accuracy of the emotion estimation information can be improved by removing the environmental sound using noise canceling technology. Furthermore, when the player is listening to specific music, the emotion can be estimated by taking into account the influence of the music. This makes it possible to estimate emotions taking into account the player's environmental sound.

[0075] When acquiring emotion estimation information of a player, the acquisition unit can improve the accuracy of emotion estimation by referring to the player's past game play data. For example, if the player's emotion was heightened in a particular game event in the past, emotion estimation information is acquired intensively during that event. Also, if the player's emotion fluctuated in a conversation with a particular character in the past, emotion estimation information can be acquired during that conversation. Furthermore, based on the player's past game play data, the acquisition frequency can be reduced during times when emotion fluctuations are small. This makes it possible to estimate emotions based on past game play data.

[0076] When estimating the player's emotion, the estimation unit can improve the accuracy of emotion estimation by taking into account the geographical location information of the player. For example, when the player is at home, the emotion is estimated on the assumption that the player is in a relaxed state. When the player is in a public place, the emotion can be estimated by taking into account the surrounding environmental sounds and background noise. Furthermore, when the player is at a specific event venue, the emotion related to the event can also be estimated. This makes it possible to estimate emotions taking into account geographical location information.

[0077] The process flow of the first embodiment will be briefly described below.

[0078] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a player. The emotion estimation information includes an image of the player's face, voice, game operation data, etc. The acquisition unit acquires an image of the player's face using a camera, and acquires the player's voice using a microphone. It also acquires input data from a game controller and a keyboard. Step 2: The estimation unit estimates the player's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit uses AI to analyze facial expressions and voice tone to estimate the player's emotions. For example, emotions such as joy, sadness, and anger are estimated from facial expressions, and emotions are also estimated from the tone and speed of voice. Step 3: The determination unit determines the behavior of the game character based on the player's emotion estimated by the estimation unit. For example, if the player is happy, the determination unit determines the behavior of the character to show a happy expression, and if the player is sad, the determination unit determines the behavior of the character to comfort the player. Step 4: The execution unit executes the character's action determined by the determination unit. The execution unit controls the character's animation to execute the determined action. The execution unit also controls the character's voice to utter the determined lines.

[0079] (Example 2) The agent system with an emotion engine according to the embodiment of the present invention is a system that is utilized as an AI character of a game. This system acquires information for estimating the player's emotions, and determines and executes the character's actions based on the emotions. For example, information such as the player's face image, voice, and game operation is acquired, and the player's emotions are estimated based on this information. Based on the estimated emotions, the behavior of the game character is determined, and for example, if the player is happy, the character also takes an action such as showing a happy expression. Furthermore, it is possible to adjust the game scenario and difficulty level based on the estimated player's emotions. For example, if the player is feeling stressed, the game difficulty level is lowered to reduce the player's stress. It is also possible to adjust the character's actions more finely based on the player's emotions and attributes. For example, the character's reaction can be changed depending on whether the player is young or old. This system can provide a game experience that corresponds to the player's emotions, realizing a more immersive game play. As a result, the agent system with an emotion engine can provide a more realistic game experience by determining and executing the character's actions based on the player's emotions.

[0080] The agent system having an emotion engine according to the embodiment includes an acquisition unit, an estimation unit, a determination unit, and an execution unit. The acquisition unit acquires emotion estimation information for estimating the emotion of a player. The emotion estimation information includes, for example, an image of the player's face, a voice, game operation data, and the like, but is not limited to such examples. For example, the acquisition unit acquires an image of the player's face using a camera, and acquires the player's voice using a microphone. The acquisition unit can also acquire input data from a game controller or a keyboard. The estimation unit estimates the player's emotion based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze the facial expression and tone of the voice to estimate the player's emotion. The estimation unit can estimate emotions such as joy, sadness, and anger from the facial expression. The estimation unit can also estimate the player's emotion from the tone and speed of the voice. The determination unit determines the action of a game character based on the player's emotion estimated by the estimation unit. For example, if the player is happy, the determination unit determines an action that causes the character to show a happy expression. Furthermore, the determination unit can also determine an action for the character to comfort the player when the player is sad. The execution unit executes the action of the character determined by the determination unit. The execution unit, for example, controls animation of the character to execute the determined action. The execution unit can also control the voice of the character to utter the determined lines. In this way, the agent system having an emotion engine according to the embodiment can provide a more realistic gaming experience by determining and executing the action of the character based on the emotion of the player.

[0081] The acquisition unit acquires emotion estimation information for estimating the emotion of the player. The emotion estimation information includes, for example, an image of the player's face, voice, game operation data, etc., but is not limited to such examples. For example, the acquisition unit acquires an image of the player's face using a camera, and acquires the voice of the player using a microphone. Specifically, the camera can capture high-resolution video in real time and capture subtle changes in the player's facial expression. The microphone collects voice features such as the tone, volume, and speed of the player's voice with high accuracy. The acquisition unit can also acquire input data from a game controller or a keyboard. This makes it possible to grasp in detail the player's behavioral patterns such as the speed, strength, and frequency of operation. Furthermore, the acquisition unit has a function of centrally managing these data and transmitting them to the analysis unit in real time. For example, the camera video and voice data can be efficiently transmitted using compression technology, minimizing delays. This allows the acquisition unit to quickly and accurately collect a variety of emotion estimation information of the player, and improve the emotion estimation accuracy of the entire system.

[0082] The estimation unit estimates the player's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit, for example, uses AI to analyze facial expressions and voice tones to estimate the player's emotions. Specifically, the AI ​​uses deep learning technology to estimate emotions such as joy, sadness, and anger from facial expressions with high accuracy. For example, it analyzes facial muscle movements, eye opening and closing, and the degree to which the corners of the mouth are turned up to determine the type and intensity of the emotion. It is also possible to estimate the player's emotions from the tone and speed of the voice. In voice analysis, the pitch, intonation, and rhythm of the voice are analyzed to capture changes in emotions. Furthermore, the estimation unit also analyzes game operation data. For example, it is possible to estimate the degree of tension or excitement from the frequency with which the player presses the game controller hard or a sudden increase in operations. As a result, the estimation unit can comprehensively analyze multiple emotion estimation information and estimate the player's emotions from multiple angles. Furthermore, the estimation unit can learn past data and individual characteristics of the player to perform emotion estimation optimized for each individual player. This enables the estimation unit to estimate the player's emotions with high accuracy, thereby improving the responsiveness of the entire system.

[0083] The determination unit determines the behavior of the game character based on the player's emotions estimated by the estimation unit. For example, when the player is happy, the determination unit determines an action that causes the character to show a happy expression. Specifically, the determination unit selects the optimal action by comprehensively considering the character's expressions, actions, lines, etc. For example, when the player is happy, the character can combine actions such as smiling and waving and jumping. In addition, the determination unit can also determine an action that the character will take to comfort the player when the player is sad. For example, the character selects an action that is in line with the player's emotions, such as speaking to the player in a gentle voice or placing a hand on the player's shoulder. Furthermore, the determination unit can adjust the progress and difficulty of the game according to changes in the player's emotions. For example, when the player is feeling stressed, the difficulty of the game can be temporarily lowered to reduce the burden on the player. In this way, the determination unit can flexibly and appropriately determine an action based on the player's emotions, thereby enriching the game experience.

[0084] The execution unit executes the character's action determined by the determination unit. The execution unit, for example, controls the animation of the character and executes the determined action. Specifically, the execution unit uses a skeletal model of the character to generate animation for reproducing realistic movement. For example, when the character smiles, the movement of the facial muscles is finely controlled to create a natural expression. The execution unit can also control the voice of the character and make the character speak the determined lines. Using voice synthesis technology, the tone and intonation of the character's voice are adjusted to realize speech according to the emotion. Furthermore, the execution unit enables more realistic expression by synchronizing the character's movement and voice. For example, when the character waves his / her hand while talking, natural communication is realized by matching the timing of the hand movement and the voice. As a result, the execution unit can execute the action determined by the determination unit with high accuracy and provide the player with a realistic game experience. Furthermore, the execution unit can transmit the result of the executed action to the acquisition unit and the estimation unit as feedback, and continuously improve the accuracy of the entire system. As a result, the execution unit can quickly and accurately execute an action based on the player's emotion, and enrich the game experience.

[0085] The determination unit can select a behavior pattern corresponding to the player's emotion as the character's behavior. For example, when the player is happy, the determination unit selects a behavior pattern in which the character shows a happy expression. In addition, when the player is sad, the determination unit can also select a behavior pattern in which the character comforts the player. Furthermore, when the player is angry, the determination unit can also select a behavior pattern in which the character responds calmly. In this way, by selecting a behavior pattern according to the player's emotion, the character's behavior becomes more appropriate. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's emotion data to the generation AI and cause the generation AI to select a behavior pattern corresponding to the emotion.

[0086] The estimation unit may include a scenario adjustment unit that adjusts the game scenario based on the player's emotions. For example, the scenario adjustment unit adds a positive development to the scenario when the player is happy. The scenario adjustment unit may also add a comforting element to the scenario when the player is sad. Furthermore, the scenario adjustment unit may also add an element to encourage a calm response to the scenario when the player is angry. This allows the game experience to be more personalized by adjusting the scenario based on the player's emotions. A part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the scenario adjustment unit may input the player's emotion data to the generation AI and cause the generation AI to determine how to adjust the scenario based on the emotion.

[0087] The estimation unit may include a difficulty adjustment unit that adjusts the difficulty of the game based on the player's emotions. For example, the difficulty adjustment unit may lower the difficulty of the game when the player is stressed. The difficulty adjustment unit may also maintain the difficulty of the game when the player is relaxed. Furthermore, the difficulty adjustment unit may also increase the difficulty of the game when the player is excited. This allows the stress of the game to be reduced by adjusting the difficulty based on the player's emotions. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using AI or may be performed without using AI. For example, the difficulty adjustment unit may input the player's emotion data to the generation AI and cause the generation AI to determine a method of adjusting the difficulty based on the emotion.

[0088] The determination unit can determine the character's behavior based on the player's emotions and the player's attributes. For example, if the player is young, the determination unit can cause the character to take an active action. Also, if the player is older, the determination unit can cause the character to take a calm action. Furthermore, the determination unit can adjust the character's behavior based on the player's gender and game experience. As a result, the character's behavior becomes more appropriate by determining the behavior based on the player's emotions and attributes. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the player's emotion data and attribute data to the generation AI and cause the generation AI to determine a method for determining the behavior based on the emotion and attribute.

[0089] The acquisition unit can acquire at least one of the player's face image, voice, and game operation as emotion estimation information. The acquisition unit can acquire the player's face image using, for example, a camera. The acquisition unit can also acquire the player's voice using a microphone. Furthermore, the acquisition unit can acquire input data from a game controller or a keyboard. This improves the accuracy of emotion estimation by acquiring emotion estimation information from various information sources. A part or all of the above-mentioned processing in the acquisition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the acquisition unit can input face image data acquired by a camera to the generation AI and cause the generation AI to execute analysis as emotion estimation information.

[0090] The acquisition unit can acquire one or more pieces of information among the player's facial expression, movement, voice, heart rate, blood pressure, pupil response, brainwave, body temperature, breathing, and posted content as emotion estimation information. The acquisition unit acquires the player's facial expression using, for example, a camera. The acquisition unit can also acquire the player's movement using a movement sensor. Furthermore, the acquisition unit can also acquire the player's heart rate using a heart rate sensor. This improves the accuracy of emotion estimation by acquiring various physiological and behavioral data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input facial expression data acquired by a camera to the generation AI and cause the generation AI to execute analysis as emotion estimation information.

[0091] The acquisition unit can estimate the player's emotion and adjust the timing of acquiring the emotion estimation information based on the estimated emotion of the player. For example, when the player is excited, the acquisition unit increases the frequency of acquiring the emotion estimation information and tracks changes in the emotion in real time. In addition, when the player is relaxed, the acquisition unit can reduce the frequency of acquiring the emotion estimation information to save resources. Furthermore, when the player is stressed, the acquisition unit can adjust the timing of acquiring the emotion estimation information and collect data for identifying the cause of the stress. This allows for efficient use of resources by adjusting the acquisition timing according to the player's emotion. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using AI or may be performed without using AI. For example, the acquisition unit can input the player's emotion data to the generation AI and cause the generation AI to determine a method of adjusting the acquisition timing based on the emotion.

[0092] The acquisition unit can analyze the player's past emotion data and select the optimal acquisition method. For example, the acquisition unit identifies a time period in which the player's emotions fluctuated greatly in the past, and acquires emotion estimation information focusing on that time period. In addition, if the player's emotions were heightened in a specific game event in the past, the acquisition unit can also intensively acquire emotion estimation information during that event. Furthermore, the acquisition unit can reduce the acquisition frequency during a time period in which emotions fluctuate little based on the player's past emotion data. This improves the accuracy of emotion estimation by selecting the optimal acquisition method based on the past emotion data. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the player's past emotion data to the generation AI and cause the generation AI to select the optimal acquisition method.

[0093] The acquisition unit can perform filtering based on the player's current game progress and play style when acquiring the emotion estimation information. For example, when the player is fighting a boss, the acquisition unit increases the frequency of acquiring emotion estimation information and records the degree of tension and excitement in detail. In addition, when the player is in a search mode, the acquisition unit can also reduce the frequency of acquiring emotion estimation information to maintain a relaxed state. Furthermore, when the player is in a battle mode, the acquisition unit can adjust the frequency of acquiring emotion estimation information to measure the degree of competitiveness and stress. As a result, filtering is performed based on the game progress and play style, improving the accuracy of emotion estimation. Part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the player's game progress and play style data to the generation AI and cause the generation AI to execute filtering.

[0094] The acquisition unit can estimate the player's emotion and determine the priority of emotion estimation information to be acquired based on the estimated player's emotion. For example, when the player is excited, the acquisition unit preferentially acquires physiological data such as heart rate and pupil reaction. In addition, when the player is relaxed, the acquisition unit can also preferentially acquire external data such as facial expressions and voice. Furthermore, when the player is stressed, the acquisition unit can also preferentially acquire stress-related data such as breathing and blood pressure. In this way, important information can be preferentially acquired by determining the priority of information based on emotion. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the player's emotion data to the generation AI and cause the generation AI to determine the priority.

[0095] When acquiring the emotion estimation information, the acquisition unit can preferentially acquire highly relevant information in consideration of the geographical location information of the player. For example, when the player is at home, the acquisition unit acquires emotion estimation information on the assumption that the player is in a relaxed state. In addition, when the player is in a public place, the acquisition unit can also acquire emotion estimation information in consideration of the surrounding environmental sounds and background noise. Furthermore, when the player is at a specific event venue, the acquisition unit can also preferentially acquire emotion estimation information related to the event. In this way, highly relevant information can be preferentially acquired by considering the geographical location information. A part or all of the above-mentioned processing in the acquisition unit may be performed, for example, using an AI or may be performed without using an AI. For example, the acquisition unit inputs the geographical location information of the player to the generation AI and causes the generation AI to select highly relevant information.

[0096] The acquisition unit can analyze the social media activity of the player when acquiring the emotion estimation information, and acquire related information. For example, when the player posts positively on social media, the acquisition unit acquires emotion estimation information reflecting the emotion. In addition, when the player posts negatively, the acquisition unit can also acquire emotion estimation information reflecting the emotion. Furthermore, when the player posts about a specific game, the acquisition unit can acquire emotion estimation information based on the content of the post. In this way, information related to the player's emotion can be acquired by analyzing the social media activity. A part or all of the above-mentioned processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the social media activity data of the player to the generation AI and cause the generation AI to acquire related information.

[0097] The estimation unit can estimate the player's emotion and adjust the estimation algorithm based on the estimated player's emotion. For example, when the player is excited, the estimation unit increases the sensitivity of the emotion estimation algorithm to capture subtle changes in emotion. Also, when the player is relaxed, the estimation unit can lower the sensitivity of the emotion estimation algorithm to maintain a stable emotional state. Furthermore, when the player is stressed, the estimation unit can adjust the emotion estimation algorithm and identify the cause of the stress. As a result, the accuracy of emotion estimation is improved by adjusting the estimation algorithm based on the emotion. A part or all of the above-mentioned processing in the estimation unit may be performed, for example, using AI or may be performed without using AI. For example, the estimation unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the estimation algorithm.

[0098] The estimation unit can adjust the level of detail of the estimation based on the importance of the emotion estimation information at the time of estimation. For example, when physiological data such as heart rate and pupil reaction are important, the estimation unit performs detailed emotion estimation based on the data. In addition, when external data such as facial expression and voice are important, the estimation unit can also perform detailed emotion estimation based on the data. Furthermore, when stress-related data such as respiration and blood pressure are important, the estimation unit can also perform detailed emotion estimation based on the data. Thereby, by adjusting the level of detail based on the importance, the accuracy of emotion estimation is improved. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the importance data of the emotion estimation information to the generation AI and cause the generation AI to adjust the level of detail.

[0099] The estimation unit can apply different estimation algorithms depending on the category of the emotion estimation information at the time of estimation. For example, the estimation unit applies an algorithm based on heart rate or pupil reaction to physiological data. The estimation unit can also apply an algorithm based on facial expression or voice to external data. Furthermore, the estimation unit can apply an algorithm based on respiration or blood pressure to stress-related data. This improves the accuracy of emotion estimation by applying an algorithm depending on the category. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input category data of the emotion estimation information to the generation AI and cause the generation AI to determine the algorithm to be applied.

[0100] The estimation unit can estimate the player's emotions and adjust the display method of the estimated result based on the estimated player's emotions. For example, when the player is nervous, the estimation unit provides a simple and highly visible display method. Furthermore, when the player is relaxed, the estimation unit can also provide a display method including detailed information. Furthermore, when the player is in a hurry, the estimation unit can also provide a display method that focuses on the main points. This allows the display to be easily viewed by the player by adjusting the display method based on the emotions. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0101] The estimation unit can determine the priority of estimation based on the acquisition time of the emotion estimation information at the time of estimation. For example, the estimation unit preferentially uses the latest emotion estimation information to perform emotion estimation in real time. The estimation unit can also perform estimation taking into account long-term emotion fluctuations by referring to past emotion estimation information. Furthermore, the estimation unit can preferentially use emotion estimation information acquired during a specific event to estimate an emotion related to the event. This enables emotion estimation in real time by determining the priority based on the acquisition time. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input acquisition time data of the emotion estimation information to the generation AI and have the generation AI determine the priority.

[0102] The estimation unit can adjust the order of estimation based on the relevance of the emotion estimation information at the time of estimation. For example, the estimation unit preferentially uses data that is most relevant to the player's current emotional state. The estimation unit can also preferentially use highly relevant data by referring to the player's past emotion data. Furthermore, the estimation unit can also preferentially use highly relevant data based on the player's game progress. As a result, the accuracy of emotion estimation is improved by adjusting the order based on the relevance. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the relevance data of the emotion estimation information to the generation AI and cause the generation AI to determine the order of estimation.

[0103] The determination unit can estimate the player's emotions and adjust the character's behavior pattern based on the estimated player's emotions. For example, when the player is happy, the determination unit causes the character to show a happy expression. In addition, when the player is sad, the determination unit can also cause the character to take an action to comfort the player. Furthermore, when the player is angry, the determination unit can also cause the character to take an action to calmly respond. In this way, the behavior pattern is adjusted based on emotions, making the character's behavior more appropriate. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the behavior pattern.

[0104] At the time of determination, the determination unit can select an optimal behavior pattern by analyzing the player's past behavior data. The determination unit, for example, determines the character's behavior based on behavior patterns that the player enjoyed in the past. The determination unit can also determine the character's behavior so as to avoid behavior patterns that the player disliked in the past. Furthermore, the determination unit can analyze the player's past behavior data and select the most effective behavior pattern. As a result, the character's behavior becomes more appropriate by selecting an optimal behavior pattern based on the past behavior data. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's past behavior data to the generation AI and cause the generation AI to select the optimal behavior pattern.

[0105] At the time of the determination, the determination unit can customize the behavior pattern based on the player's current game progress. For example, when the player is fighting a boss, the determination unit can cause the character to take an encouraging action. In addition, when the player is in an exploration mode, the determination unit can also cause the character to take a relaxed action. Furthermore, when the player is in a battle mode, the determination unit can cause the character to take an action that stimulates competitive spirit. This makes the character's behavior more appropriate by customizing the behavior pattern based on the game progress. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the determination unit can input the player's game progress data to the generation AI and cause the generation AI to execute customization of the behavior pattern.

[0106] The determination unit can estimate the player's emotions and determine the priority of the character's actions based on the estimated player's emotions. For example, when the player is happy, the determination unit prioritizes the character's happy actions. In addition, when the player is sad, the determination unit can also prioritize the character's comforting actions. Furthermore, when the player is angry, the determination unit can also prioritize the character's calm response. In this way, by determining the priority of actions based on emotions, the character's actions become more appropriate. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the player's emotion data to the generation AI and have the generation AI determine the priority of actions.

[0107] The determination unit can select the optimal behavior pattern by considering the geographical location information of the player at the time of determination. For example, when the player is at home, the determination unit selects a relaxed behavior pattern. In addition, when the player is in a public place, the determination unit can also select a behavior pattern that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the determination unit can also select a behavior pattern related to the event. In this way, the optimal behavior pattern can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the determination unit inputs the geographical location information of the player to the generation AI and causes the generation AI to select the optimal behavior pattern.

[0108] At the time of the determination, the determination unit can analyze the social media activity of the player and propose a behavior pattern. For example, when the player posts positively on social media, the determination unit can propose a behavior pattern that reflects the player's emotions. In addition, when the player posts negatively, the determination unit can also propose a behavior pattern that reflects the player's emotions. Furthermore, when the player posts about a specific game, the determination unit can also propose a behavior pattern based on the content of the post. In this way, a behavior pattern related to the player's emotions can be proposed by analyzing the social media activity. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using an AI or may be performed without using an AI. For example, the determination unit can input the player's social media activity data to the generation AI and cause the generation AI to execute the proposal of a behavior pattern.

[0109] The execution unit can estimate the player's emotions and adjust the character's action execution method based on the estimated player's emotions. For example, when the player is happy, the execution unit can make the character speak in a bright voice. Furthermore, when the player is sad, the execution unit can make the character speak in a gentle voice. Furthermore, when the player is angry, the execution unit can make the character speak in a calm voice. In this way, the character's actions become more appropriate by adjusting the execution method based on emotions. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit can input the player's emotion data to the generation AI and cause the generation AI to adjust the action execution method.

[0110] The execution unit can select the optimal execution method by referring to the player's past behavior data at the time of execution. The execution unit executes the character's behavior, for example, based on behavior patterns that the player enjoyed in the past. The execution unit can also execute the character's behavior so as to avoid behavior patterns that the player disliked in the past. Furthermore, the execution unit can analyze the player's past behavior data and execute the most effective behavior pattern. As a result, the character's behavior becomes more appropriate by selecting the optimal execution method based on the past behavior data. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI, or may be performed without using AI. For example, the execution unit inputs the player's past behavior data to the generation AI and causes the generation AI to select the optimal execution method.

[0111] The execution unit can customize the execution method based on the player's current game progress at the time of execution. For example, when the player is fighting a boss, the execution unit causes the character to perform an encouraging action. In addition, when the player is in an exploration mode, the execution unit can also cause the character to perform a relaxing action. Furthermore, when the player is in a battle mode, the execution unit can also cause the character to perform an action that stimulates competitive spirit. In this way, by customizing the execution method based on the game progress, the character's actions become more appropriate. Part or all of the above-mentioned processing in the execution unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the execution unit can input the player's game progress data to the generation AI and cause the generation AI to execute the customization of the execution method.

[0112] The execution unit can estimate the player's emotions and determine the priority of the character's action execution based on the estimated player's emotions. For example, when the player is happy, the execution unit can prioritize the character's action of joy. In addition, when the player is sad, the execution unit can also prioritize the character's action of comfort. Furthermore, when the player is angry, the execution unit can also prioritize the character's calm response. In this way, by determining the priority of execution based on emotions, the character's actions become more appropriate. Part or all of the above-mentioned processing in the execution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the execution unit can input the player's emotion data to the generation AI and have the generation AI determine the priority of action execution.

[0113] The execution unit can select the optimal execution method in consideration of the geographical location information of the player at the time of execution. For example, when the player is at home, the execution unit executes a relaxed behavior pattern. In addition, when the player is in a public place, the execution unit can also execute a behavior pattern that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the execution unit can also execute a behavior pattern related to the event. In this way, the optimal execution method can be selected by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the execution unit inputs the geographical location information of the player to the generation AI and causes the generation AI to select the optimal execution method.

[0114] The execution unit can analyze the social media activity of the player at the time of execution and suggest an execution method. For example, when the player posts positively on social media, the execution unit executes an action pattern that reflects the player's emotions. In addition, when the player posts negatively, the execution unit can also execute an action pattern that reflects the player's emotions. Furthermore, when the player posts about a specific game, the execution unit can execute an action pattern based on the content of the post. In this way, by analyzing the social media activity, an execution method related to the player's emotions can be suggested. A part or all of the above-mentioned processing in the execution unit may be performed, for example, using AI or may be performed without using AI. For example, the execution unit can input the player's social media activity data to the generation AI and cause the generation AI to execute the proposed execution method.

[0115] The scenario adjustment unit can estimate the player's emotions and determine how to adjust the scenario based on the estimated player's emotions. For example, if the player is happy, the scenario adjustment unit can add a positive development to the scenario. Also, if the player is sad, the scenario adjustment unit can add an element of comfort to the scenario. Furthermore, if the player is angry, the scenario adjustment unit can add an element that encourages a calm response to the scenario. In this way, by determining how to adjust the scenario based on emotions, it is possible to provide an optimal scenario for the player. Part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the scenario adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine how to adjust the scenario.

[0116] The scenario adjustment unit can select an optimal scenario by referring to the player's past gameplay data when adjusting the scenario. The scenario adjustment unit adjusts the current scenario based on, for example, a scenario development that the player enjoyed in the past. The scenario adjustment unit can also adjust the current scenario so as to avoid a scenario development that the player disliked in the past. Furthermore, the scenario adjustment unit can analyze the player's past gameplay data and select the most effective scenario. In this way, the optimal scenario can be provided to the player by selecting an optimal scenario based on the past gameplay data. A part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the scenario adjustment unit inputs the player's past gameplay data to the generation AI and causes the generation AI to select an optimal scenario.

[0117] The scenario adjustment unit can estimate the player's emotions and determine the priority of scenarios based on the estimated player's emotions. For example, if the player is happy, the scenario adjustment unit prioritizes a positive scenario development. Furthermore, if the player is sad, the scenario adjustment unit can also prioritize a comforting scenario development. Furthermore, if the player is angry, the scenario adjustment unit can also prioritize a scenario development that encourages a calm response. In this way, by determining the priority of scenarios based on emotions, it is possible to provide an optimal scenario for the player. Part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the scenario adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine the priority of scenarios.

[0118] The scenario adjustment unit can select an optimal scenario by considering the geographical location information of the player when adjusting the scenario. For example, when the player is at home, the scenario adjustment unit selects a relaxed scenario development. In addition, when the player is in a public place, the scenario adjustment unit can also select a scenario development that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the scenario adjustment unit can also select a scenario development related to the event. In this way, the optimal scenario can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the scenario adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the scenario adjustment unit can input the geographical location information of the player to the generation AI and cause the generation AI to select the optimal scenario.

[0119] The difficulty adjustment unit can estimate the player's emotions and determine a difficulty adjustment method based on the estimated player's emotions. For example, the difficulty adjustment unit can lower the difficulty of the game when the player is stressed. The difficulty adjustment unit can also maintain the difficulty of the game when the player is relaxed. Furthermore, the difficulty adjustment unit can also increase the difficulty of the game when the player is excited. In this way, by determining the difficulty adjustment method based on emotions, it is possible to provide an optimal difficulty level for the player. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the difficulty adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine the difficulty adjustment method.

[0120] The difficulty adjustment unit can select the optimal difficulty level by referring to the player's past gameplay data when adjusting the difficulty level. The difficulty adjustment unit adjusts the current difficulty level based on, for example, a difficulty level that the player has cleared in the past. The difficulty adjustment unit can also adjust the current difficulty level so as to avoid a difficulty level that the player has had difficulty with in the past. Furthermore, the difficulty adjustment unit can analyze the player's past gameplay data and select the most effective difficulty level. In this way, the optimal difficulty level can be selected based on the past gameplay data, thereby providing the player with an optimal difficulty level. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the difficulty adjustment unit can input the player's past gameplay data to the generation AI and cause the generation AI to select the optimal difficulty level.

[0121] The difficulty adjustment unit can estimate the player's emotions and determine the priority order of the difficulty levels based on the estimated player's emotions. For example, when the player is feeling stressed, the difficulty adjustment unit can prioritize lowering the difficulty level. Furthermore, when the player is relaxed, the difficulty adjustment unit can also prioritize maintaining the difficulty level. Furthermore, when the player is excited, the difficulty adjustment unit can also prioritize increasing the difficulty level. In this way, by determining the priority order of the difficulty levels based on emotions, it is possible to provide an optimal difficulty level for the player. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using AI, or may be performed without using AI. For example, the difficulty adjustment unit can input the player's emotion data to the generation AI and cause the generation AI to determine the priority order of the difficulty levels.

[0122] The difficulty adjustment unit can select the optimal difficulty level by considering the geographical location information of the player when adjusting the difficulty level. For example, when the player is at home, the difficulty adjustment unit selects a relaxed difficulty level. In addition, when the player is in a public place, the difficulty adjustment unit can also select a difficulty level that takes into consideration the surrounding environment. Furthermore, when the player is at a specific event venue, the difficulty adjustment unit can also select a difficulty level related to the event. In this way, the optimal difficulty level can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the difficulty adjustment unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the difficulty adjustment unit can input the geographical location information of the player to the generation AI and cause the generation AI to select the optimal difficulty level.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The acquisition unit can monitor the player's physiological data in real time and issue a warning if an abnormality is detected. For example, if the heart rate rises sharply, a warning is displayed urging the player to take a break. Also, if blood pressure is abnormally high, the game can be paused and a recommendation made to contact a medical institution. Furthermore, the acquisition unit can analyze the player's brainwave data and provide a guide for relaxing if excessive stress is detected. This allows the player's health condition to be monitored in real time and appropriate measures to be taken.

[0125] When estimating the player's emotions, the estimation unit can refer to the player's past emotion data and learn the emotion fluctuation pattern. For example, if the player consistently feels joy in a specific game event, it can predict a similar emotion when that event occurs again. Also, if the player consistently feels anger in a conversation with a specific character, it can predict anger when a conversation with that character occurs. Furthermore, it can adjust the algorithm to improve the emotion prediction accuracy based on the player's emotion fluctuation pattern. This allows the player's emotions to be estimated more accurately.

[0126] The determination unit can take into consideration the play style of the player when determining the character's behavior based on the player's emotions. For example, if the player has an aggressive play style, the character can also take an aggressive action. Also, if the player has a cautious play style, the character can also take a defensive action. Furthermore, if the player has a play style that emphasizes exploration, the character can also take an action that supports exploration. This makes it possible to provide character actions according to the player's play style.

[0127] The execution unit can obtain real-time feedback from the player when performing the character's actions and adjust the actions based on the feedback. For example, if the player has a positive reaction to the character's action, the action can be reinforced. Also, if the player has a negative reaction, the action can be modified. Furthermore, based on the player's feedback, the execution unit can learn the character's behavioral patterns so that it can take more appropriate actions in the future. This makes it possible to provide character actions that reflect the player's feedback.

[0128] When acquiring the emotion estimation information of the player, the acquisition unit can analyze the player's environmental sound to improve the accuracy of emotion estimation. For example, when the player is in a quiet environment, the accuracy of acquiring the emotion estimation information is improved. Also, when the player is in a noisy environment, the accuracy of the emotion estimation information can be improved by removing the environmental sound using noise canceling technology. Furthermore, when the player is listening to specific music, the emotion can be estimated by taking into account the influence of the music. This makes it possible to estimate emotions taking into account the player's environmental sound.

[0129] When estimating the player's emotions, the estimation unit can analyze the player's social media activities to improve the accuracy of emotion estimation. For example, if the player posts positively on social media, an estimation reflecting the player's emotions can be made. Also, if the player posts negatively, an estimation reflecting the player's emotions can be made. Furthermore, if the player posts about a specific game, emotions can be estimated based on the content of the post. This makes it possible to estimate emotions taking social media activities into account.

[0130] The determination unit can take into consideration the player's in-game choices when determining the character's actions based on the player's emotions. For example, the determination unit determines the character's actions based on choices the player has made in the past. Also, when the player selects a particular choice, the determination unit can take an action that corresponds to that choice. Furthermore, the determination unit can learn the player's choice tendencies and be able to take more appropriate actions in the future. This makes it possible to provide character actions that reflect the player's choices.

[0131] When executing the character's actions, the execution unit can obtain the player's real-time physiological data and adjust the actions based on the data. For example, if the player's heart rate increases, the character can take an action to relax. Also, if the player's breathing becomes shallow, the character can take an action to encourage the player to take a deep breath. Furthermore, if the player's body temperature rises, the character can take an action to encourage the player to cool down. In this way, it is possible to provide character actions that reflect the player's physiological data.

[0132] When acquiring emotion estimation information of a player, the acquisition unit can improve the accuracy of emotion estimation by referring to the player's past game play data. For example, if the player's emotion was heightened in a particular game event in the past, emotion estimation information is acquired intensively during that event. Also, if the player's emotion fluctuated in a conversation with a particular character in the past, emotion estimation information can be acquired during that conversation. Furthermore, based on the player's past game play data, the acquisition frequency can be reduced during times when emotion fluctuations are small. This makes it possible to estimate emotions based on past game play data.

[0133] When estimating the player's emotion, the estimation unit can improve the accuracy of emotion estimation by taking into account the geographical location information of the player. For example, when the player is at home, the emotion is estimated on the assumption that the player is in a relaxed state. When the player is in a public place, the emotion can be estimated by taking into account the surrounding environmental sounds and background noise. Furthermore, when the player is at a specific event venue, the emotion related to the event can also be estimated. This makes it possible to estimate emotions taking into account geographical location information.

[0134] The process flow of the second embodiment will be briefly described below.

[0135] Step 1: The acquisition unit acquires emotion estimation information for estimating the emotion of a player. The emotion estimation information includes an image of the player's face, voice, game operation data, etc. The acquisition unit acquires an image of the player's face using a camera, and acquires the player's voice using a microphone. It also acquires input data from a game controller and a keyboard. Step 2: The estimation unit estimates the player's emotions based on the emotion estimation information acquired by the acquisition unit. The estimation unit uses AI to analyze facial expressions and voice tone to estimate the player's emotions. For example, emotions such as joy, sadness, and anger are estimated from facial expressions, and emotions are also estimated from the tone and speed of voice. Step 3: The determination unit determines the behavior of the game character based on the player's emotion estimated by the estimation unit. For example, if the player is happy, the determination unit determines the behavior of the character to show a happy expression, and if the player is sad, the determination unit determines the behavior of the character to comfort the player. Step 4: The execution unit executes the character's action determined by the determination unit. The execution unit controls the character's animation to execute the determined action. The execution unit also controls the character's voice to utter the determined lines.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] For example, the acquisition unit can acquire an image of the face and sound of the player using the camera 42 and microphone 38B of the smart device 14. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the player's emotion by analyzing the emotion estimation information obtained from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the character's action based on the estimated emotion. For example, the execution unit is realized by the control unit 46A of the smart device 14, and executes the determined character's action. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] For example, the acquisition unit can acquire an image of the face and sound of the player using the camera 42 and microphone 238 of the smart glasses 214. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the player's emotion by analyzing the emotion estimation information obtained from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the character's action based on the estimated emotion. For example, the execution unit is realized by the control unit 46A of the smart glasses 214, and executes the determined character's action. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] For example, the acquisition unit can acquire an image of the face and sound of the player using the camera 42 and microphone 238 of the headset type terminal 314. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the player by analyzing the emotion estimation information obtained from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the action of the character based on the estimated emotion. For example, the execution unit is realized by the control unit 46A of the headset type terminal 314, and executes the determined action of the character. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] For example, the acquisition unit can acquire an image of the face and sound of the player using the camera 42 and microphone 238 of the robot 414. For example, the estimation unit is realized by the specific processing unit 290 of the data processing device 12, and estimates the emotion of the player by analyzing the emotion estimation information obtained from the acquisition unit. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12, and determines the behavior of the character based on the estimated emotion. For example, the execution unit is realized by the control unit 46A of the robot 414, and executes the determined behavior of the character. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0189] 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.

[0190] 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.

[0191] These emotions are distributed in the 3 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.

[0192] 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 you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0193] 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.

[0194] 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."

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] (Appendix 1) an acquisition unit that acquires emotion estimation information for estimating an emotion of a game player; an estimation unit that estimates an emotion of the player based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines an action of the game character based on the emotion of the player estimated by the estimation unit; an execution unit that executes the action of the character determined by the determination unit; A system characterized by: (Appendix 2) The determination unit is A behavior pattern corresponding to the player's emotion is selected as the character's behavior. 2. The system of claim 1, (Appendix 3) Based on the emotion of the player estimated by the estimation unit, A scenario adjustment unit adjusts the scenario of the game. 2. The system of claim 1, (Appendix 4) Based on the emotion of the player estimated by the estimation unit, A difficulty level adjustment unit is provided for adjusting the difficulty level of the game. 2. The system of claim 1, (Appendix 5) The determination unit is Deciding the character's actions based on the player's emotions and the player's attributes 2. The system of claim 1, (Appendix 6) The acquisition unit is Acquire, as the emotion estimation information, any one of an image of a player's face, a voice of the player, and a game operation of the player. 2. The system of claim 1, (Appendix 7) The acquisition unit is Any of the player's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, breathing, and posted content is acquired as the emotion estimation information. 2. The system of claim 1, (Appendix 8) The acquisition unit is To estimate a player's emotion and adjust timing of acquiring emotion estimation information based on the estimated player's emotion. 2. The system of claim 1, (Appendix 9) The acquisition unit is Analyze the player's past emotional data and select the optimal acquisition method 2. The system of claim 1, (Appendix 10) The acquisition unit is When acquiring emotion estimation information, filtering is performed based on the player's current game progress and play style. 2. The system of claim 1, (Appendix 11) The acquisition unit is The emotion of the player is estimated, and a priority order of emotion estimation information to be acquired is determined based on the estimated emotion of the player. 2. The system of claim 1, (Appendix 12) The acquisition unit is When obtaining information for emotion estimation, the geographic location of the player is taken into consideration to prioritize obtaining information that is more relevant. 2. The system of claim 1, (Appendix 13) The acquisition unit is When obtaining emotion estimation information, analyze the player's social media activity to obtain related information. 2. The system of claim 1, (Appendix 14) The estimation unit is Estimate player emotions and adjust the estimation algorithm based on the estimated player emotions 2. The system of claim 1, (Appendix 15) The estimation unit is During estimation, the level of detail of the estimation is adjusted based on the importance of the emotion estimation information. 2. The system of claim 1, (Appendix 16) The estimation unit is During estimation, different estimation algorithms are applied depending on the category of emotion estimation information. 2. The system of claim 1, (Appendix 17) The estimation unit is Infer the player's emotions and adjust how the results are displayed based on the inferred player emotions 2. The system of claim 1, (Appendix 18) The estimation unit is During estimation, the priority of estimation is determined based on the time when emotion estimation information was acquired. 2. The system of claim 1, (Appendix 19) The estimation unit is During estimation, the order of estimation is adjusted based on the relevance of emotion estimation information. 2. The system of claim 1, (Appendix 20) The determination unit is Estimate the player's emotions and adjust the character's behavior patterns based on the estimated player's emotions. 2. The system of claim 1, (Appendix 21) The determination unit is When making a decision, the player's past behavior data is analyzed to select the optimal behavior pattern. 2. The system of claim 1, (Appendix 22) The determination unit is When making a decision, customize your behavior based on the player's current game progress 2. The system of claim 1, (Appendix 23) The determination unit is Estimate the player's emotions and prioritize character actions based on the estimated player's emotions 2. The system of claim 1, (Appendix 24) The determination unit is When making a decision, the player's geographic location is taken into account to select the optimal behavior pattern. 2. The system of claim 1, (Appendix 25) The determination unit is Analyze players' social media activity to suggest behavioral patterns when making decisions 2. The system of claim 1, (Appendix 26) The execution unit is Estimate the player's emotions and adjust the way the character performs actions based on the estimated player's emotions. 2. The system of claim 1, (Appendix 27) The execution unit is At the time of execution, the optimal execution method is selected by referring to the player's past behavior data. 2. The system of claim 1, (Appendix 28) The execution unit is At runtime, customize how it runs based on the player's current game progress 2. The system of claim 1, (Appendix 29) The execution unit is Estimate the player's emotions and prioritize the character's actions based on the estimated player's emotions. 2. The system of claim 1, (Appendix 30) The execution unit is At runtime, the optimal execution method is chosen taking into account the player's geographic location. 2. The system of claim 1, (Appendix 31) The execution unit is At run time, analyze players' social media activity to suggest how to execute 2. The system of claim 1, (Appendix 32) The scenario adjustment unit is Estimate player emotions and decide how to adjust the scenario based on the estimated player emotions 4. The system of claim 3. (Appendix 33) The scenario adjustment unit is When adjusting the scenario, the optimal scenario is selected by referring to the player's past gameplay data. 4. The system of claim 3. (Appendix 34) The scenario adjustment unit is Estimate player emotions and prioritize scenarios based on the estimated player emotions 4. The system of claim 3. (Appendix 35) The scenario adjustment unit is When adjusting scenarios, the optimal scenario is selected taking into account the player's geographic location information. 4. The system of claim 3. (Appendix 36) The difficulty level adjustment unit is Estimate the player's emotions and decide how to adjust the difficulty based on the estimated player's emotions 5. The system of claim 4. (Appendix 37) The difficulty level adjustment unit is When adjusting the difficulty level, the optimal difficulty level is selected based on the player's past gameplay data. 5. The system of claim 4. (Appendix 38) The difficulty level adjustment unit is Estimate the player's emotions and prioritize the difficulty based on the estimated player's emotions 5. The system of claim 4. (Appendix 39) The difficulty level adjustment unit is When adjusting the difficulty level, the optimal difficulty level is selected taking into account the player's geographic location information. 5. The system of claim 4. [Explanation of symbols]

[0208] 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. an acquisition unit that acquires emotion estimation information for estimating an emotion of a game player; an estimation unit that estimates an emotion of the player based on the emotion estimation information acquired by the acquisition unit; a determination unit that determines an action of the game character based on the emotion of the player estimated by the estimation unit; an execution unit that executes the action of the character determined by the determination unit; A system characterized by:

2. The determination unit is A behavior pattern corresponding to the emotion of the player is selected as the behavior of the character.

2. The system of claim 1 .

3. A scenario adjustment unit adjusts a scenario of the game based on the emotion of the player estimated by the estimation unit.

2. The system of claim 1 .

4. A difficulty level adjustment unit adjusts the difficulty level of the game based on the emotion of the player estimated by the estimation unit.

2. The system of claim 1 .

5. The determination unit is Deciding the character's actions based on the player's emotions and the player's attributes 2. The system of claim 1 .

6. The acquisition unit is Acquire, as the emotion estimation information, any one of an image of the player's face, the voice of the player, and the game operation of the player.

2. The system of claim 1 .

7. The acquisition unit is Any of the player's facial expression, movement, voice, heart rate, blood pressure, pupil response, brain wave, body temperature, breathing, and posted content is acquired as the emotion estimation information.

2. The system of claim 1 .

8. The acquisition unit is The emotion of the player is estimated, and a timing for acquiring the emotion estimation information is adjusted based on the estimated emotion of the player.

2. The system of claim 1 .

9. The acquisition unit is Analyze the player's past emotion data and select the optimal acquisition method.

2. The system of claim 1 .

10. The acquisition unit is When acquiring the emotion estimation information, filtering is performed based on the player's current game progress and play style.

2. The system of claim 1 .

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