System

Abstract

An object of the system according to the embodiment is to enable an individual to easily create and distribute a game and obtain a profit.SOLUTION: A system according to an embodiment includes a game creation unit, a distribution unit, and a monetization unit. The game creation unit creates a game using the generated AI. The distribution unit distributes the game created by the game creation unit. The monetization unit obtains a profit from the game distributed by the distribution 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has made it difficult for individuals to easily create, distribute, and monetize games.

[0005] The system according to the embodiment aims to enable individuals to easily create and distribute games and earn revenue. [Means for solving the problem]

[0006] The system according to the embodiment includes a game creation unit, a distribution unit, and a monetization unit. The game creation unit creates a game using a generation AI. The distribution unit distributes the game created by the game creation unit. The monetization unit earns revenue from the game distributed by the distribution unit. [Effects of the Invention]

[0007] The system according to the embodiment allows individuals to easily create and distribute games and earn revenue. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 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 device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A game creation platform according to an embodiment of the present invention is a system that uses generative AI to allow individuals to easily create and distribute games and earn revenue. This allows individuals to create and distribute games and earn revenue.

[0029] A game creation platform according to an embodiment includes a generation AI, a game creation unit, a distribution unit, and a monetization unit. The generation AI analyzes game requirements input by a user and automatically creates a game based on those requirements. For example, if a user inputs a requirement such as "an action game with a scene in which the main character fights a dragon," the generation AI analyzes the requirements and generates an appropriate game scenario, character design, background, music, and so on. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI creates a game based on those prompts. The game creation unit uses the generation AI to create a game. For example, the generation AI can learn the user's past game creation history and automatically suggest game elements tailored to the user's preferences. The generation AI can also analyze the user's voice input and create a game based on the voice instructions. Furthermore, the generation AI can use an emotion estimation function to analyze the user's emotions in real time while creating a game and suggest game elements that elicit positive emotions. The distribution unit distributes the game created by the game creation unit. For example, games created by the generation AI can be easily distributed on the platform. Users can publish the games they have created to other users and allow them to play them. The monetization unit earns revenue from the games distributed by the distribution unit. For example, revenue can be earned through game distribution by introducing advertising revenue or a billing system, similar to the YouTube business model. As a result, the game creation platform according to the embodiment allows individuals to easily create and distribute games and earn revenue.

[0030] The game creation unit can learn the user's past game creation history and automatically suggest game elements that match the user's preferences. The generation AI, for example, analyzes the user's past game creation history to identify the user's preferred game genres and design elements. For example, it automatically suggests characters and storylines that the user prefers based on data from games created in the past. The generation AI also learns the user's past game creation history and prioritizes suggesting game elements that the user frequently uses. For example, it automatically selects specific game mechanics and art styles. Furthermore, the generation AI suggests new game elements that match the user's preferences based on the user's past game creation history. For example, it generates new characters and scenarios that reflect the user's preferred themes and settings. This improves the efficiency of game creation by automatically suggesting game elements that match the user's preferences.

[0031] The game creation unit can analyze the user's voice input and create a game based on the voice instructions. For example, if the user verbally instructs, "I want to create a game where people fight dragons in a fantasy world," the generation AI analyzes the voice and automatically generates an appropriate game scenario and character design. The generation AI also analyzes the voice input and generates game backgrounds and music based on the user's instructions. For example, based on the voice instruction, "I want to create a battle scene in a dark forest," the generation AI selects appropriate background images and music. Furthermore, when the user verbally instructs the user on detailed game requirements, the generation AI analyzes the requirements and automatically combines and creates each element of the game. For example, it responds to instructions such as, "Add a scene where the main character uses magic." This improves user convenience by creating games based on voice instructions.

[0032] The game creation unit can analyze a user's handwritten sketches and generate game character designs and backgrounds based on those sketches. For example, when a user uploads a handwritten character sketch, the generation AI analyzes the sketch and automatically generates digital character designs. For example, a detailed digital dragon is created based on a handwritten drawing of a dragon. The generation AI also analyzes handwritten sketches and generates game backgrounds based on those sketches. For example, a realistic forest background is automatically generated based on a user's sketch of a forest. Furthermore, the generation AI analyzes a scenario sketch handwritten by the user and generates a game storyline based on that scenario. For example, a detailed game scenario is created based on a handwritten adventure map. This brings out the user's creativity by generating character designs and backgrounds based on handwritten sketches.

[0033] The game creation unit can reference game elements created by other users and combine optimal elements to create a new game. The generation AI, for example, references character designs and backgrounds created by other users and combines them to create a new game. For example, it generates a new game by combining popular characters and backgrounds. The generation AI also references game scenarios created by other users and creates a new storyline based on those scenarios. For example, it combines multiple scenarios to generate a complex story. Furthermore, the generation AI references game mechanics created by other users and combines them to create new gameplay. For example, it combines different game mechanics to provide a new game experience. This improves the efficiency of game creation by utilizing elements created by other users.

[0034] The monetization unit can analyze game play data, identify popular game elements, and automatically generate advertisements that emphasize those elements. The generation AI, for example, analyzes game play data and identifies the most popular characters and scenarios. It then automatically generates advertisements that emphasize those elements and delivers them to users. The generation AI also identifies the game mechanics that users enjoy most based on the play data and generates advertisements that emphasize those mechanics. For example, it creates advertisements that emphasize specific action scenes. The generation AI also analyzes play data, identifies the most popular game elements, and automatically generates advertisements based on those elements. For example, it creates advertisements that emphasize levels and stages that users play frequently. This improves monetization efficiency by automatically generating advertisements that emphasize popular game elements.

[0035] The monetization unit can analyze a user's play style and propose a monetization model that matches the play style. The generation AI, for example, analyzes a user's play style and proposes the optimal monetization model that matches the play style. For example, for a user who likes action games, advertisements specialized for action scenes are displayed. The generation AI also proposes a monetization model that the user is most interested in based on the play style. For example, for a user who likes strategy games, a payment system for strategic items is proposed. Furthermore, the generation AI analyzes a user's play style and customizes the monetization model based on that data. For example, for a user who likes casual games, advertisements that can be completed in a short time are displayed. This improves monetization efficiency by proposing a monetization model that matches the user's play style.

[0036] The monetization unit can analyze interaction data between users in a game and provide special monetization options to users who interact frequently. The generation AI, for example, analyzes interaction data between users in a game and provides special monetization options to users who interact frequently. For example, selling limited items to users who interact frequently. The generation AI also proposes special monetization options based on the interaction data between users. For example, displaying special advertisements to users who interact frequently. Furthermore, the generation AI analyzes the interaction data between users and builds a system that provides special monetization options to users who interact frequently. For example, providing special paid content to users who interact frequently. This improves monetization efficiency by providing special monetization options to users who interact frequently.

[0037] The monetization department can collaborate with other platforms to achieve cross-platform monetization. The generation AI can collaborate with, for example, social media and video streaming services to achieve cross-platform monetization. For example, in-game gameplay videos can be shared on social media and advertisements can be displayed on those videos. The generation AI can also collaborate with other platforms to build a system that achieves cross-platform monetization. For example, in-game items can be sold on video streaming services. Furthermore, the generation AI can collaborate with other platforms to propose advertising and billing systems to achieve cross-platform monetization. For example, it can provide monetization options based on the number of followers on social media. This expands the scope of monetization by collaborating with other platforms.

[0038] The schedule management unit can learn from past project data and automatically generate optimal schedules and resource allocations. The generation AI, for example, analyzes past project data and automatically generates optimal schedules. For example, it proposes efficient schedules based on past project progress and resource usage. The generation AI also learns from past project data and automatically generates optimal resource allocations. For example, it efficiently allocates necessary resources based on past resource usage data. Furthermore, the generation AI builds a system that optimizes schedules and resource allocations based on past project data. For example, it proposes optimal schedules and resource allocations by referring to data from past successful projects. In this way, it can automatically generate optimal schedules and resource allocations by learning from past project data.

[0039] The schedule management unit can monitor resource usage in real time and automatically adjust resource surpluses and shortages. The generation AI, for example, builds a system that monitors resource usage in real time and automatically adjusts resource surpluses and shortages. For example, if there is a resource shortage, it automatically allocates additional resources. The generation AI also monitors resource usage in real time and makes adjustments to prevent overuse. For example, if a resource is being overused, it reallocates the resource to other projects. Furthermore, the generation AI analyzes resource usage in real time and automatically adjusts the optimal allocation of resources. For example, it dynamically changes resource allocation depending on resource usage. This enables efficient resource use by monitoring resource usage in real time and automatically adjusting surpluses and shortages.

[0040] The schedule management section can work in conjunction with other project management tools to achieve integrated schedule management. The generation AI, for example, works in conjunction with other project management tools to build a system that achieves integrated schedule management. For example, it imports data from existing project management tools and generates an integrated schedule. The generation AI also works in conjunction with other project management tools to improve the efficiency of schedule management. For example, it integrates data from different tools and manages it centrally. Furthermore, the generation AI works in conjunction with other project management tools to develop a system that updates the schedule in real time. For example, it immediately reflects changes in other tools. This makes integrated schedule management possible by working in conjunction with other project management tools.

[0041] The schedule management unit can analyze the resource usage history and propose the optimal method for reusing resources. The generation AI, for example, builds a system that analyzes the resource usage history and proposes the optimal method for reusing resources. For example, it proposes a method for reusing resources used in past projects. The generation AI also proposes an efficient method for reusing resources based on the resource usage history. For example, it allocates unused resources to other projects. Furthermore, the generation AI analyzes the resource usage history and automatically generates the optimal method for reusing resources. For example, it proposes a plan for reusing resources used in past projects. In this way, the optimal method for reusing resources can be proposed by analyzing the resource usage history.

[0042] The development process optimization unit analyzes data at each stage of the development process and can propose optimal development methods in real time. The generative AI, for example, analyzes data at each stage of the development process and builds a system that proposes optimal development methods in real time. For example, it proposes the optimal method according to the progress of development. The generative AI also proposes optimal development methods in real time based on data from the development process. For example, it proposes the optimal solution when a specific problem occurs. The generative AI also analyzes data at each stage of the development process and automatically generates optimal development methods. For example, it proposes the optimal tools and technologies according to the progress of development. This makes it possible to propose optimal development methods in real time by analyzing data at each stage of the development process.

[0043] The development process optimization unit can refer to data from other development projects and propose the optimal development process. The generation AI, for example, builds a system that refers to data from other development projects and proposes the optimal development process. For example, it proposes the optimal development method based on data from past successful projects. The generation AI also proposes the optimal development process based on data from other development projects. For example, it refers to data from similar projects and selects the optimal development method. Furthermore, the generation AI refers to data from other development projects and automatically generates the optimal development process. For example, it proposes the optimal tools and technologies based on data from past projects. This makes it possible to propose the optimal development process by referring to data from other development projects.

[0044] The development process optimization unit can visualize the progress of the development process and provide an interactive dashboard. The generation AI, for example, builds a system that visualizes the progress of the development process and provides an interactive dashboard. For example, it displays the development progress in real time. The generation AI also provides an interactive dashboard based on the progress of the development process. For example, it visualizes the progress of tasks and resource usage. Furthermore, the generation AI develops a system that visualizes the progress of the development process and provides an interactive dashboard. For example, it displays the development progress in graphs and charts. This makes it possible to provide an interactive dashboard by visualizing the progress of the development process.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The game creation unit can analyze a user's handwritten sketches and generate game character designs and backgrounds based on those sketches. For example, when a user uploads a handwritten character sketch, the generation AI analyzes the sketch and automatically generates digital character designs. For example, a detailed digital dragon is created based on a handwritten drawing of a dragon. The generation AI also analyzes handwritten sketches and generates game backgrounds based on those sketches. For example, a realistic forest background is automatically generated based on a user's sketch of a forest. Furthermore, the generation AI analyzes a scenario sketch handwritten by the user and generates a game storyline based on that scenario. For example, a detailed game scenario is created based on a handwritten adventure map. This allows the user's creativity to be brought out by generating character designs and backgrounds based on handwritten sketches.

[0047] The game creation unit can reference game elements created by other users and combine optimal elements to create a new game. The generation AI, for example, references character designs and backgrounds created by other users and combines them to create a new game. For example, it generates a new game by combining popular characters and backgrounds. The generation AI also references game scenarios created by other users and creates a new storyline based on those scenarios. For example, it combines multiple scenarios to generate a complex story. Furthermore, the generation AI references game mechanics created by other users and combines them to create new gameplay. For example, it combines different game mechanics to provide a new game experience. This improves the efficiency of game creation by utilizing elements created by other users.

[0048] The monetization unit can analyze game play data, identify popular game elements, and automatically generate advertisements that emphasize those elements. The generation AI, for example, analyzes game play data and identifies the most popular characters and scenarios. It then automatically generates advertisements that emphasize those elements and delivers them to users. The generation AI also identifies the game mechanics that users enjoy most based on the play data and generates advertisements that emphasize those mechanics. For example, it creates advertisements that emphasize specific action scenes. The generation AI also analyzes play data, identifies the most popular game elements, and automatically generates advertisements based on those elements. For example, it creates advertisements that emphasize levels and stages that users play frequently. This improves monetization efficiency by automatically generating advertisements that emphasize popular game elements.

[0049] The monetization unit can analyze a user's play style and propose a monetization model that matches the play style. The generation AI, for example, analyzes a user's play style and proposes the optimal monetization model that matches the play style. For example, for a user who likes action games, advertisements specialized for action scenes are displayed. The generation AI also proposes a monetization model that the user is most interested in based on the play style. For example, for a user who likes strategy games, a payment system for strategic items is proposed. Furthermore, the generation AI analyzes a user's play style and customizes the monetization model based on that data. For example, for a user who likes casual games, advertisements that can be completed in a short time are displayed. This improves monetization efficiency by proposing a monetization model that matches the user's play style.

[0050] The monetization department can collaborate with other platforms to achieve cross-platform monetization. The generation AI can collaborate with, for example, social media and video streaming services to achieve cross-platform monetization. For example, in-game gameplay videos can be shared on social media and advertisements can be displayed on those videos. The generation AI can also collaborate with other platforms to build a system that achieves cross-platform monetization. For example, in-game items can be sold on video streaming services. Furthermore, the generation AI can collaborate with other platforms to propose advertising and billing systems to achieve cross-platform monetization. For example, it can provide monetization options based on the number of followers on social media. This expands the scope of monetization by collaborating with other platforms.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The game creation unit uses a generation AI to create a game. The generation AI analyzes the game requirements entered by the user and automatically creates a game based on those requirements. For example, if a user enters a requirement such as "an action game with a scene in which the main character fights a dragon," the generation AI analyzes the requirements and generates an appropriate game scenario, character design, background, music, etc. The generation AI receives prompts containing instructions on what the user wants the generation AI to do, and the generation AI creates a game based on those prompts. Furthermore, the generation AI learns the user's past game creation history and automatically suggests game elements that match the user's preferences. The generation AI can also analyze the user's voice input and create a game based on the voice instructions. Furthermore, the generation AI can use its emotion estimation function to analyze the user's emotions in real time while creating a game and suggest game elements that elicit positive emotions. Step 2: The distribution unit distributes the games created by the game creation unit. For example, games created by the generation AI can be easily distributed on the platform. Users can make the games they create publicly available to other users and let them play them. Step 3: The monetization department earns revenue from the games distributed by the distribution department. For example, revenue can be earned through game distribution by introducing advertising revenue or a payment system, similar to the YouTube business model.

[0053] (Example 2) A game creation platform according to an embodiment of the present invention is a system that uses generative AI to allow individuals to easily create and distribute games and earn revenue. This allows individuals to create and distribute games and earn revenue.

[0054] A game creation platform according to an embodiment includes a generation AI, a game creation unit, a distribution unit, and a monetization unit. The generation AI analyzes game requirements input by a user and automatically creates a game based on those requirements. For example, if a user inputs a requirement such as "an action game with a scene in which the main character fights a dragon," the generation AI analyzes the requirements and generates an appropriate game scenario, character design, background, music, and so on. The generation AI receives input from the user in the form of prompts containing instructions on what the user wants the generation AI to do, and the generation AI creates a game based on those prompts. The game creation unit uses the generation AI to create a game. For example, the generation AI can learn the user's past game creation history and automatically suggest game elements tailored to the user's preferences. The generation AI can also analyze the user's voice input and create a game based on the voice instructions. Furthermore, the generation AI can use an emotion estimation function to analyze the user's emotions in real time while creating a game and suggest game elements that elicit positive emotions. The distribution unit distributes the game created by the game creation unit. For example, games created by the generation AI can be easily distributed on the platform. Users can publish the games they have created to other users and allow them to play them. The monetization unit earns revenue from the games distributed by the distribution unit. For example, revenue can be earned through game distribution by introducing advertising revenue or a billing system, similar to the YouTube business model. As a result, the game creation platform according to the embodiment allows individuals to easily create and distribute games and earn revenue.

[0055] The game creation unit can learn the user's past game creation history and automatically suggest game elements that match the user's preferences. The generation AI, for example, analyzes the user's past game creation history to identify the user's preferred game genres and design elements. For example, it automatically suggests characters and storylines that the user prefers based on data from games created in the past. The generation AI also learns the user's past game creation history and prioritizes suggesting game elements that the user frequently uses. For example, it automatically selects specific game mechanics and art styles. Furthermore, the generation AI suggests new game elements that match the user's preferences based on the user's past game creation history. For example, it generates new characters and scenarios that reflect the user's preferred themes and settings. This improves the efficiency of game creation by automatically suggesting game elements that match the user's preferences.

[0056] The game creation unit can analyze the user's voice input and create a game based on the voice instructions. For example, if the user verbally instructs, "I want to create a game where people fight dragons in a fantasy world," the generation AI analyzes the voice and automatically generates an appropriate game scenario and character design. The generation AI also analyzes the voice input and generates game backgrounds and music based on the user's instructions. For example, based on the voice instruction, "I want to create a battle scene in a dark forest," the generation AI selects appropriate background images and music. Furthermore, when the user verbally instructs the user on detailed game requirements, the generation AI analyzes the requirements and automatically combines and creates each element of the game. For example, it responds to instructions such as, "Add a scene where the main character uses magic." This improves user convenience by creating games based on voice instructions.

[0057] The game creation unit can use the emotion estimation function to analyze the emotions felt by the user while creating the game in real time and suggest game elements that elicit positive emotions. The generation AI, for example, analyzes the emotions felt by the user while creating the game in real time and suggests game elements that elicit positive emotions. For example, when the user is having fun, it adds more fun elements. The generation AI also uses the emotion estimation function to suggest elements that can help the user relax when they are feeling stressed. For example, when the user is tired, it suggests simple game mechanics or relaxing music. Furthermore, the generation AI analyzes the user's emotional state and provides interactive feedback to elicit positive emotions. For example, when the user is happy, it displays compliments or encouraging messages. This improves user satisfaction by suggesting game elements based on the user's emotions.

[0058] The game creation unit can analyze a user's handwritten sketches and generate game character designs and backgrounds based on those sketches. For example, when a user uploads a handwritten character sketch, the generation AI analyzes the sketch and automatically generates digital character designs. For example, a detailed digital dragon is created based on a handwritten drawing of a dragon. The generation AI also analyzes handwritten sketches and generates game backgrounds based on those sketches. For example, a realistic forest background is automatically generated based on a user's sketch of a forest. Furthermore, the generation AI analyzes a scenario sketch handwritten by the user and generates a game storyline based on that scenario. For example, a detailed game scenario is created based on a handwritten adventure map. This brings out the user's creativity by generating character designs and backgrounds based on handwritten sketches.

[0059] The game creation unit can reference game elements created by other users and combine optimal elements to create a new game. The generation AI, for example, references character designs and backgrounds created by other users and combines them to create a new game. For example, it generates a new game by combining popular characters and backgrounds. The generation AI also references game scenarios created by other users and creates a new storyline based on those scenarios. For example, it combines multiple scenarios to generate a complex story. Furthermore, the generation AI references game mechanics created by other users and combines them to create new gameplay. For example, it combines different game mechanics to provide a new game experience. This improves the efficiency of game creation by utilizing elements created by other users.

[0060] The game creation unit can use the emotion estimation function to analyze the emotional reactions to the requirements entered by the user and prioritize reflecting the requirements that interest the user most. The generation AI, for example, analyzes the emotional reactions to the requirements entered by the user in real time and prioritizes reflecting the requirements that interest the user most. For example, it prioritizes incorporating requirements that the user is excited about into the game. The generation AI also uses the emotion estimation function to analyze positive emotional reactions to the requirements entered by the user and reflects those requirements into the game. For example, it prioritizes incorporating requirements that the user enjoys. Furthermore, the generation AI identifies the requirements that interest the user most based on the user's emotional reaction data and builds a system that reflects those requirements into the game. For example, it prioritizes incorporating requirements that the user enjoys into the game. In this way, user satisfaction is improved by reflecting requirements based on the user's emotional reactions.

[0061] The monetization unit can analyze game play data, identify popular game elements, and automatically generate advertisements that emphasize those elements. The generation AI, for example, analyzes game play data and identifies the most popular characters and scenarios. It then automatically generates advertisements that emphasize those elements and delivers them to users. The generation AI also identifies the game mechanics that users enjoy most based on the play data and generates advertisements that emphasize those mechanics. For example, it creates advertisements that emphasize specific action scenes. The generation AI also analyzes play data, identifies the most popular game elements, and automatically generates advertisements based on those elements. For example, it creates advertisements that emphasize levels and stages that users play frequently. This improves monetization efficiency by automatically generating advertisements that emphasize popular game elements.

[0062] The monetization unit can analyze a user's play style and propose a monetization model that matches the play style. The generation AI, for example, analyzes a user's play style and proposes the optimal monetization model that matches the play style. For example, for a user who likes action games, advertisements specialized for action scenes are displayed. The generation AI also proposes a monetization model that the user is most interested in based on the play style. For example, for a user who likes strategy games, a payment system for strategic items is proposed. Furthermore, the generation AI analyzes a user's play style and customizes the monetization model based on that data. For example, for a user who likes casual games, advertisements that can be completed in a short time are displayed. This improves monetization efficiency by proposing a monetization model that matches the user's play style.

[0063] The monetization unit can analyze interaction data between users in a game and provide special monetization options to users who interact frequently. The generation AI, for example, analyzes interaction data between users in a game and provides special monetization options to users who interact frequently. For example, selling limited items to users who interact frequently. The generation AI also proposes special monetization options based on the interaction data between users. For example, displaying special advertisements to users who interact frequently. Furthermore, the generation AI analyzes the interaction data between users and builds a system that provides special monetization options to users who interact frequently. For example, providing special paid content to users who interact frequently. This improves monetization efficiency by providing special monetization options to users who interact frequently.

[0064] The monetization department can collaborate with other platforms to achieve cross-platform monetization. The generation AI can collaborate with, for example, social media and video streaming services to achieve cross-platform monetization. For example, in-game gameplay videos can be shared on social media and advertisements can be displayed on those videos. The generation AI can also collaborate with other platforms to build a system that achieves cross-platform monetization. For example, in-game items can be sold on video streaming services. Furthermore, the generation AI can collaborate with other platforms to propose advertising and billing systems to achieve cross-platform monetization. For example, it can provide monetization options based on the number of followers on social media. This expands the scope of monetization by collaborating with other platforms.

[0065] The monetization department can use the emotion estimation function to suggest monetization content that is likely to resonate emotionally with players based on the emotions they feel in the game. The generation AI, for example, uses the emotion estimation function to analyze the emotions felt by players in the game and suggest monetization content that is likely to resonate emotionally with players. For example, when a player is moved, an emotional story is sold. The generation AI also builds a system that suggests monetization content that is likely to resonate emotionally with players based on the player's emotional data. For example, when a player is happy, a special item is sold. Furthermore, the generation AI uses the emotion estimation function to analyze the player's emotional state and customize monetization content that is likely to resonate emotionally with players. For example, when a player is excited, content that increases the player's excitement is suggested. This improves monetization efficiency by suggesting monetization content based on the player's emotions.

[0066] The schedule management unit can learn from past project data and automatically generate optimal schedules and resource allocations. The generation AI, for example, analyzes past project data and automatically generates optimal schedules. For example, it proposes efficient schedules based on past project progress and resource usage. The generation AI also learns from past project data and automatically generates optimal resource allocations. For example, it efficiently allocates necessary resources based on past resource usage data. Furthermore, the generation AI builds a system that optimizes schedules and resource allocations based on past project data. For example, it proposes optimal schedules and resource allocations by referring to data from past successful projects. In this way, it can automatically generate optimal schedules and resource allocations by learning from past project data.

[0067] The schedule management unit can monitor resource usage in real time and automatically adjust resource surpluses and shortages. The generation AI, for example, builds a system that monitors resource usage in real time and automatically adjusts resource surpluses and shortages. For example, if there is a resource shortage, it automatically allocates additional resources. The generation AI also monitors resource usage in real time and makes adjustments to prevent overuse. For example, if a resource is being overused, it reallocates the resource to other projects. Furthermore, the generation AI analyzes resource usage in real time and automatically adjusts the optimal allocation of resources. For example, it dynamically changes resource allocation depending on resource usage. This enables efficient resource use by monitoring resource usage in real time and automatically adjusting surpluses and shortages.

[0068] The schedule management unit can use the emotion estimation function to analyze the emotional state of development team members and propose schedules that are less emotionally stressful. The generation AI, for example, uses the emotion estimation function to analyze the emotional state of development team members in real time and propose schedules that are less stressful. For example, if a member is tired, it can suggest increasing rest time. The generation AI also automatically generates schedules that are less emotionally stressful based on the emotional data of development team members. For example, it can assign important tasks to times when members can relax. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional state of development team members and propose schedules that are more emotionally positive. For example, it can assign important tasks to times when members can concentrate best. This makes it possible to provide a less stressful development environment by proposing schedules based on the emotional state of development team members.

[0069] The schedule management section can work in conjunction with other project management tools to achieve integrated schedule management. The generation AI, for example, works in conjunction with other project management tools to build a system that achieves integrated schedule management. For example, it imports data from existing project management tools and generates an integrated schedule. The generation AI also works in conjunction with other project management tools to improve the efficiency of schedule management. For example, it integrates data from different tools and manages it centrally. Furthermore, the generation AI works in conjunction with other project management tools to develop a system that updates the schedule in real time. For example, it immediately reflects changes in other tools. This makes integrated schedule management possible by working in conjunction with other project management tools.

[0070] The schedule management unit can analyze the resource usage history and propose the optimal method for reusing resources. The generation AI, for example, builds a system that analyzes the resource usage history and proposes the optimal method for reusing resources. For example, it proposes a method for reusing resources used in past projects. The generation AI also proposes an efficient method for reusing resources based on the resource usage history. For example, it allocates unused resources to other projects. Furthermore, the generation AI analyzes the resource usage history and automatically generates the optimal method for reusing resources. For example, it proposes a plan for reusing resources used in past projects. In this way, the optimal method for reusing resources can be proposed by analyzing the resource usage history.

[0071] The schedule management unit can use the emotion estimation function to propose emotionally positive resource allocations based on the emotional state of development team members. The generation AI, for example, uses the emotion estimation function to analyze the emotional state of development team members in real time and propose emotionally positive resource allocations. For example, it allocates resources that allow members to be most relaxed. The generation AI also automatically generates emotionally positive resource allocations based on the emotional data of development team members. For example, it allocates resources that allow members to be most focused. Furthermore, the generation AI uses the emotion estimation function to build a system that analyzes the emotional state of development team members and proposes emotionally positive resource allocations. For example, it allocates resources that make members feel most motivated. This makes it possible to provide a positive development environment by proposing resource allocations based on the emotional state of development team members.

[0072] The development process optimization unit analyzes data at each stage of the development process and can propose optimal development methods in real time. The generative AI, for example, analyzes data at each stage of the development process and builds a system that proposes optimal development methods in real time. For example, it proposes the optimal method according to the progress of development. The generative AI also proposes optimal development methods in real time based on data from the development process. For example, it proposes the optimal solution when a specific problem occurs. The generative AI also analyzes data at each stage of the development process and automatically generates optimal development methods. For example, it proposes the optimal tools and technologies according to the progress of development. This makes it possible to propose optimal development methods in real time by analyzing data at each stage of the development process.

[0073] The development process optimization unit can use the emotion estimation function to analyze the emotional state of development team members and propose an emotionally positive development environment. The generation AI, for example, uses the emotion estimation function to analyze the emotional state of development team members in real time and propose an emotionally positive development environment. For example, it can provide an environment in which members can be most relaxed. The generation AI also automatically generates an emotionally positive development environment based on the emotional data of development team members. For example, it can provide an environment in which members can be most concentrated. Furthermore, the generation AI uses the emotion estimation function to build a system that analyzes the emotional state of development team members and proposes an emotionally positive development environment. For example, it can provide an environment in which members feel most motivated. This makes it possible to provide a positive development environment by proposing a development environment based on the emotional state of development team members.

[0074] The development process optimization unit can refer to data from other development projects and propose the optimal development process. The generation AI, for example, builds a system that refers to data from other development projects and proposes the optimal development process. For example, it proposes the optimal development method based on data from past successful projects. The generation AI also proposes the optimal development process based on data from other development projects. For example, it refers to data from similar projects and selects the optimal development method. Furthermore, the generation AI refers to data from other development projects and automatically generates the optimal development process. For example, it proposes the optimal tools and technologies based on data from past projects. This makes it possible to propose the optimal development process by referring to data from other development projects.

[0075] The development process optimization unit can visualize the progress of the development process and provide an interactive dashboard. The generation AI, for example, builds a system that visualizes the progress of the development process and provides an interactive dashboard. For example, it displays the development progress in real time. The generation AI also provides an interactive dashboard based on the progress of the development process. For example, it visualizes the progress of tasks and resource usage. Furthermore, the generation AI develops a system that visualizes the progress of the development process and provides an interactive dashboard. For example, it displays the development progress in graphs and charts. This makes it possible to provide an interactive dashboard by visualizing the progress of the development process.

[0076] The development process optimization unit can use the emotion estimation function to propose an emotionally positive development process based on the emotional state of the development team members. The generation AI, for example, uses the emotion estimation function to analyze the emotional state of the development team members in real time and propose an emotionally positive development process. For example, it provides a process in which the members can be most relaxed. The generation AI also automatically generates an emotionally positive development process based on the emotional data of the development team members. For example, it provides a process in which the members can be most focused. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional state of the development team members and build a system that proposes an emotionally positive development process. For example, it provides a process in which the members feel most motivated. This makes it possible to provide a positive development environment by proposing a development process based on the emotional state of the development team members.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The game creation unit can analyze a user's handwritten sketches and generate game character designs and backgrounds based on those sketches. For example, when a user uploads a handwritten character sketch, the generation AI analyzes the sketch and automatically generates digital character designs. For example, a detailed digital dragon is created based on a handwritten drawing of a dragon. The generation AI also analyzes handwritten sketches and generates game backgrounds based on those sketches. For example, a realistic forest background is automatically generated based on a user's sketch of a forest. Furthermore, the generation AI analyzes a scenario sketch handwritten by the user and generates a game storyline based on that scenario. For example, a detailed game scenario is created based on a handwritten adventure map. This allows the user's creativity to be brought out by generating character designs and backgrounds based on handwritten sketches.

[0079] The game creation unit can reference game elements created by other users and combine optimal elements to create a new game. The generation AI, for example, references character designs and backgrounds created by other users and combines them to create a new game. For example, it generates a new game by combining popular characters and backgrounds. The generation AI also references game scenarios created by other users and creates a new storyline based on those scenarios. For example, it combines multiple scenarios to generate a complex story. Furthermore, the generation AI references game mechanics created by other users and combines them to create new gameplay. For example, it combines different game mechanics to provide a new game experience. This improves the efficiency of game creation by utilizing elements created by other users.

[0080] The monetization unit can analyze game play data, identify popular game elements, and automatically generate advertisements that emphasize those elements. The generation AI, for example, analyzes game play data and identifies the most popular characters and scenarios. It then automatically generates advertisements that emphasize those elements and delivers them to users. The generation AI also identifies the game mechanics that users enjoy most based on the play data and generates advertisements that emphasize those mechanics. For example, it creates advertisements that emphasize specific action scenes. The generation AI also analyzes play data, identifies the most popular game elements, and automatically generates advertisements based on those elements. For example, it creates advertisements that emphasize levels and stages that users play frequently. This improves monetization efficiency by automatically generating advertisements that emphasize popular game elements.

[0081] The monetization unit can analyze a user's play style and propose a monetization model that matches the play style. The generation AI, for example, analyzes a user's play style and proposes the optimal monetization model that matches the play style. For example, for a user who likes action games, advertisements specialized for action scenes are displayed. The generation AI also proposes a monetization model that the user is most interested in based on the play style. For example, for a user who likes strategy games, a payment system for strategic items is proposed. Furthermore, the generation AI analyzes a user's play style and customizes the monetization model based on that data. For example, for a user who likes casual games, advertisements that can be completed in a short time are displayed. This improves monetization efficiency by proposing a monetization model that matches the user's play style.

[0082] The monetization department can collaborate with other platforms to achieve cross-platform monetization. The generation AI can collaborate with, for example, social media and video streaming services to achieve cross-platform monetization. For example, in-game gameplay videos can be shared on social media and advertisements can be displayed on those videos. The generation AI can also collaborate with other platforms to build a system that achieves cross-platform monetization. For example, in-game items can be sold on video streaming services. Furthermore, the generation AI can collaborate with other platforms to propose advertising and billing systems to achieve cross-platform monetization. For example, it can provide monetization options based on the number of followers on social media. This expands the scope of monetization by collaborating with other platforms.

[0083] The game creation unit can use the emotion estimation function to analyze the emotional reactions to the requirements entered by the user and prioritize reflecting the requirements that interest the user most. The generation AI, for example, analyzes the emotional reactions to the requirements entered by the user in real time and prioritizes reflecting the requirements that interest the user most. For example, it prioritizes incorporating requirements that the user is excited about into the game. The generation AI also uses the emotion estimation function to analyze positive emotional reactions to the requirements entered by the user and reflects those requirements into the game. For example, it prioritizes incorporating requirements that the user enjoys. Furthermore, the generation AI identifies the requirements that interest the user most based on the user's emotional reaction data and builds a system that reflects those requirements into the game. For example, it prioritizes incorporating requirements that the user enjoys into the game. In this way, user satisfaction is improved by reflecting requirements based on the user's emotional reactions.

[0084] The monetization department can use the emotion estimation function to suggest monetization content that is likely to resonate emotionally with players based on the emotions they feel in the game. The generation AI, for example, uses the emotion estimation function to analyze the emotions felt by players in the game and suggest monetization content that is likely to resonate emotionally with players. For example, when a player is moved, an emotional story is sold. The generation AI also builds a system that suggests monetization content that is likely to resonate emotionally with players based on the player's emotional data. For example, when a player is happy, a special item is sold. Furthermore, the generation AI uses the emotion estimation function to analyze the player's emotional state and customize monetization content that is likely to resonate emotionally with players. For example, when a player is excited, content that increases the player's excitement is suggested. This improves monetization efficiency by suggesting monetization content based on the player's emotions.

[0085] The schedule management unit can use the emotion estimation function to analyze the emotional state of development team members and propose schedules that are less emotionally stressful. The generation AI, for example, uses the emotion estimation function to analyze the emotional state of development team members in real time and propose schedules that are less stressful. For example, if a member is tired, it can suggest increasing rest time. The generation AI also automatically generates schedules that are less emotionally stressful based on the emotional data of development team members. For example, it can assign important tasks to times when members can relax. Furthermore, the generation AI uses the emotion estimation function to analyze the emotional state of development team members and propose schedules that are more emotionally positive. For example, it can assign important tasks to times when members can concentrate best. This makes it possible to provide a less stressful development environment by proposing schedules based on the emotional state of development team members.

[0086] The schedule management unit can use the emotion estimation function to propose emotionally positive resource allocations based on the emotional state of development team members. The generation AI, for example, uses the emotion estimation function to analyze the emotional state of development team members in real time and propose emotionally positive resource allocations. For example, it allocates resources that allow members to be most relaxed. The generation AI also automatically generates emotionally positive resource allocations based on the emotional data of development team members. For example, it allocates resources that allow members to be most focused. Furthermore, the generation AI uses the emotion estimation function to build a system that analyzes the emotional state of development team members and proposes emotionally positive resource allocations. For example, it allocates resources that make members feel most motivated. This makes it possible to provide a positive development environment by proposing resource allocations based on the emotional state of development team members.

[0087] The development process optimization unit can use the emotion estimation function to analyze the emotional state of development team members and propose an emotionally positive development environment. The generation AI, for example, uses the emotion estimation function to analyze the emotional state of development team members in real time and propose an emotionally positive development environment. For example, it can provide an environment in which members can be most relaxed. The generation AI also automatically generates an emotionally positive development environment based on the emotional data of development team members. For example, it can provide an environment in which members can be most concentrated. Furthermore, the generation AI uses the emotion estimation function to build a system that analyzes the emotional state of development team members and proposes an emotionally positive development environment. For example, it can provide an environment in which members feel most motivated. This makes it possible to provide a positive development environment by proposing a development environment based on the emotional state of development team members.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The game creation unit uses a generation AI to create a game. The generation AI analyzes the game requirements entered by the user and automatically creates a game based on those requirements. For example, if a user enters a requirement such as "an action game with a scene in which the main character fights a dragon," the generation AI analyzes the requirements and generates an appropriate game scenario, character design, background, music, etc. The generation AI receives prompts containing instructions on what the user wants the generation AI to do, and the generation AI creates a game based on those prompts. Furthermore, the generation AI learns the user's past game creation history and automatically suggests game elements that match the user's preferences. The generation AI can also analyze the user's voice input and create a game based on the voice instructions. Furthermore, the generation AI can use its emotion estimation function to analyze the user's emotions in real time while creating a game and suggest game elements that elicit positive emotions. Step 2: The distribution unit distributes the games created by the game creation unit. For example, games created by the generation AI can be easily distributed on the platform. Users can make the games they create publicly available to other users and let them play them. Step 3: The monetization department earns revenue from the games distributed by the distribution department. For example, revenue can be earned through game distribution by introducing advertising revenue or a payment system, similar to the YouTube business model.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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, RAM 30, and 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.

[0096] 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.

[0097] 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 audio according to instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0099] 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 carried out in a secure state.

[0100] Fig. 4 shows an example of the 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.

[0101] 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.

[0102] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0106] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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, RAM 30, and 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.

[0111] 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.

[0112] 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 audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] 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 carried out in a secure state.

[0115] 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.

[0116] 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.

[0117] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0121] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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, RAM 30, and 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.

[0126] 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.

[0127] 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 audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] 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 carried out in a secure state.

[0130] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0131] 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.

[0132] 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.

[0133] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] 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 voice indicating a user input regarding 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.

[0137] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or 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.

[0139] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0140] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0141] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0142] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0145] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0146] 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.

[0147] 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 USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0148] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0149] 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.

[0150] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0151] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0152] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0153] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0154] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0155] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a game creation unit that creates games using generative AI; a distribution unit that distributes the game created by the game creation unit; a monetization unit that obtains revenue from the game distributed by the distribution unit; A system characterized by:

2. The game creation unit Analyzing a user's handwritten sketch and generating character designs and backgrounds for the game based on the sketch 2. The system of claim 1.

3. The monetization unit Analyze the gameplay data of the game, identify popular game elements, and automatically generate advertisements that emphasize those elements.

2. The system of claim 1.

4. The schedule management department Learn from past project data to automatically generate optimal schedules and resource allocations 2. The system of claim 1.

5. The Development Process Optimization Department Analyze data at each stage of the development process and propose optimal development methods in real time 2. The system of claim 1.

6. The game creation unit Analyzing the emotions felt by users while creating a game in real time and suggesting game elements that elicit positive emotions 2. The system of claim 1.

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