system

Abstract

An object of a system according to an embodiment is to link an equipment item in a game with an actual equipment item so that the equipment item can be used both actually and virtually.SOLUTION: A system according to an embodiment includes a generation AI, a 3D printer, an accessory interlocking kit, and a VR headset. The generation AI generates the equipment based on the instruction of the user using the generation AI. The 3D printer creates the outfit generated by the generation AI as a real object. The gear interlock kit interlocks the real gear created by the 3D printer with the in-game gear. The VR headset makes available both real and virtual accessories that are interlocked by the accessory interlock kit.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] With conventional technology, it is difficult to link in-game equipment with real-world equipment, and there is room for improvement in terms of making it usable in both reality and virtual reality.

[0005] The system according to the embodiment aims to link in-game equipment with real-world equipment, making it possible to use the equipment in both the real world and the virtual world. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a 3D printer, an equipment linking kit, and a VR headset. The generation AI uses the generation AI to generate equipment based on user instructions. The 3D printer creates the equipment generated by the generation AI as a real item. The equipment linking kit links the real equipment created by the 3D printer with equipment in a game. The VR headset makes the equipment linked by the equipment linking kit available in both reality and virtual reality. [Effects of the Invention]

[0007] The system according to the embodiment links in-game equipment with real-world equipment, making it possible to use the equipment in both the real world and the virtual world. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. 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) An equipment generation system according to an embodiment of the present invention uses a generation AI to generate equipment based on user instructions, creates the equipment as a real object using a 3D printer, links the real equipment with in-game equipment using an equipment linking kit, and makes it available in both reality and virtual reality through a VR headset. This allows the equipment generation system to generate equipment based on user instructions, create it as a real object, and make it available in both reality and virtual reality.

[0029] An equipment generation system according to an embodiment includes a generation AI, a 3D printer, an equipment linkage kit, and a VR headset. The generation AI generates equipment based on user instructions. For example, if a user inputs specific instructions such as "I want the sword to be shaped like dragon wings, have a fire-wrapped technique, and increase attack power," the generation AI analyzes the instructions and generates the equipment's design, technique, and stats. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate equipment based on the user's wishes. The 3D printer creates the equipment generated by the generation AI as a physical object. For example, design data for a dragon wing-like sword generated by the generation AI is sent to the 3D printer, which then creates an actual sword of that shape. 3D printers can create equipment with high precision using materials such as plastic and metal. The equipment linkage kit links the physical equipment created by the 3D printer with in-game equipment. For example, a sensor can be attached to the physical sword, and the sensor can be linked to the sword's movement and stats in the game. The equipment linking kit uses sensors and communication modules to link real-world equipment with in-game equipment in real time. A VR headset allows equipment linked by the equipment linking kit to be used in both the real world and the virtual world. For example, when a user wears a VR headset and swings a real sword, the same movement is reproduced in the VR space, allowing the user to inflict damage on enemies. The VR headset has a high-resolution display and a wide viewing angle, providing the user with an immersive experience. This allows the equipment creation system according to the embodiment to generate equipment based on the user's instructions, create it as a real object, and make it available in both the real world and the virtual world. For example, users can fight in the VR space with their own original equipment and enjoy leveling up their in-game skills and stats. Furthermore, by utilizing a payment system, they can differentiate themselves from other players and obtain more powerful equipment.

[0030] The generation AI can analyze a user's past play data and suggest equipment that is optimal for their play style. For example, the generation AI can analyze a user's past play data and suggest equipment based on their play style. For example, it can suggest equipment with high attack power to a user with an offensive play style. The generation AI can also generate equipment designs and stats that are optimal for their play style based on the user's past play data. For example, it can suggest equipment with high defensive power to a user with a defensive-oriented play style. The generation AI can also analyze a user's play data and suggest customizing equipment according to their play style. For example, it can suggest balanced equipment to a user with a balanced play style. This makes it possible to suggest equipment that is optimal for the user's play style.

[0031] The generation AI can receive user feedback in real time and instantly modify the equipment design. For example, the generation AI adds a function to receive user feedback in real time and instantly modify the equipment design. For example, if the user wants to change part of the design, it will be reflected immediately. The generation AI also allows the user to provide feedback on the equipment design in real time and modify the design based on that feedback. For example, changes to color or shape will be reflected immediately. The generation AI also analyzes user feedback in real time and adds a function to instantly modify the equipment design and status. For example, if the user does not like part of the design, it will be modified immediately. This allows the equipment design to be instantly modified based on user feedback.

[0032] The generation AI can create equipment that fits better by reflecting the user's physical data. For example, the generation AI can adjust the design of equipment based on the user's physical data (height, weight, etc.) to create equipment that fits better. For example, it can suggest the size and shape of equipment to suit the user's body type. The generation AI can also input the user's physical data and customize the design and status of equipment based on that data. For example, it can suggest a long sword for a tall user. The generation AI can also analyze the user's physical data and generate designs to improve the fit of equipment. For example, it can suggest highly durable equipment for a heavy user. This makes it possible to create equipment that fits better based on the user's physical data.

[0033] The generation AI can refer to other users' equipment designs and incorporate popular design elements. For example, the generation AI can analyze other users' equipment designs and add a function to incorporate popular design elements. For example, popular design elements can be automatically suggested. The generation AI can also refer to other users' equipment designs and generate equipment that incorporates popular design elements. For example, specific design patterns or colors can be incorporated. The generation AI can also collect other users' equipment design data and add a function to customize equipment based on popular design elements. For example, popular decorations and shapes can be incorporated. This makes it possible to incorporate popular design elements from other users.

[0034] 3D printers can use different materials simultaneously to adjust the strength and flexibility of equipment. 3D printers add the ability to use different materials simultaneously to adjust the strength and flexibility of equipment. For example, a hard material is used for the blade of a sword and a flexible material for the handle. 3D printers also customize the characteristics of equipment by using different materials simultaneously. For example, a hard material is used for the outside of armor and a soft material for the inside. 3D printers also have the ability to use different materials simultaneously to adjust the strength and flexibility of equipment. For example, a highly durable material is used for the surface of a shield and a lightweight material is used for the back. This allows the strength and flexibility of equipment to be adjusted by using different materials simultaneously.

[0035] 3D printers can embed sensors inside equipment and monitor the condition of the equipment in real time. 3D printers, for example, have the ability to embed sensors inside equipment to monitor the condition of the equipment in real time. For example, they can monitor the condition of a sword blade or the durability of armor. 3D printers can also embed sensors inside equipment and create equipment that incorporates those sensors. For example, the sensors can detect the equipment's usage and damage in real time. 3D printers can also embed sensors inside equipment to create a system that monitors the equipment's condition in real time. For example, sensors can monitor the temperature and humidity of the equipment. This allows the equipment's condition to be monitored in real time.

[0036] A 3D printer can first create a miniature prototype based on the design data of equipment, allowing the user to check it. For example, a 3D printer can first create a miniature prototype based on the design data of equipment, allowing the user to check it. For example, a miniature sword can be created, and the user can check the design. A 3D printer can also create a miniature prototype first, allowing the user to check the equipment design and make modifications as necessary. For example, a miniature armor can be created, and the user can check the fit. A system can also be built in which a 3D printer can create a miniature prototype based on the design data of equipment, allowing the user to check it. For example, the overall appearance of the equipment can be checked using a miniature version. This allows the user to check the miniature prototype.

[0037] 3D printers can modularize parts of equipment based on equipment design data and create them as interchangeable parts. For example, 3D printers can modularize parts of equipment based on equipment design data and create interchangeable parts. For example, they can create sword blades and handles as interchangeable parts. 3D printers can also create modularized parts based on equipment design data, allowing users to freely combine them. For example, they can make each part of armor interchangeable. 3D printers can also build systems that modularize parts of equipment based on equipment design data and create them as interchangeable parts. For example, this makes it easier to customize equipment. This allows parts of equipment to be modularized and created as interchangeable parts.

[0038] The equipment linking kit can track the user's movements with high accuracy and reflect them in in-game actions. The equipment linking kit, for example, tracks the user's movements with high accuracy and reflects them in in-game actions. For example, it accurately reflects sword swings and defensive movements in the game. The equipment linking kit also tracks the user's movements in real time and reproduces in-game actions based on that data. For example, it accurately reflects jumps and evasion movements. Furthermore, using high-accuracy tracking technology, the equipment linking kit builds a system that reflects the user's movements in the game. For example, it accurately reproduces small hand movements and body movements. This allows the user's movements to be tracked with high accuracy and reflected in in-game actions.

[0039] The equipment linking kit can monitor the user's health state and affect the in-game status. The equipment linking kit, for example, monitors the user's health state (heart rate, body temperature, etc.) and affects the in-game status. For example, if the heart rate is high, attack power increases. The equipment linking kit also monitors the user's health state in real time and adjusts the in-game status based on that data. For example, if the body temperature is high, defensive power increases. Furthermore, a system is constructed in which sensors that monitor health state are incorporated into equipment and the equipment linking kit dynamically adjusts the in-game status based on that data. For example, if the heart rate is low, recovery power increases. In this way, the user's health state can be monitored and affect the in-game status.

[0040] The equipment linkage kit can link with other smart devices to provide more diverse feedback. The equipment linkage kit can link with other smart devices (smartwatches, smart glasses, etc.) to provide more diverse feedback. For example, it uses vibrations from a smartwatch or visual feedback from smart glasses. The equipment linkage kit also builds a system that provides more diverse feedback by linking with other smart devices. For example, it displays in-game information through smart glasses. Furthermore, by strengthening the linkage with smart devices, the equipment linkage kit can provide more diverse feedback to the user. For example, it adjusts feedback using heart rate data from a smartwatch. This allows it to link with other smart devices to provide more diverse feedback.

[0041] The equipment linking kit can be designed to be compatible with different game platforms. For example, the equipment linking kit can be designed to be compatible with different game platforms (PC, console, mobile). For example, it can be used in both PC games and console games. The equipment linking kit can also be optimized in design to ensure compatibility across different platforms. For example, it can be used in mobile games. The equipment linking kit can also be used to build a system designed to be compatible with multiple game platforms. For example, it can synchronize data between different platforms. This allows it to be designed to be compatible with different game platforms.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The equipment generation system may further include a health management unit that monitors the user's health condition. For example, it may measure the user's heart rate and body temperature and adjust the equipment status according to the user's health condition. It is possible to adjust the equipment status by increasing attack power when the heart rate is high and increasing defense power when the body temperature is low. The health management unit may also display an alert urging the user to take a break based on the user's health condition. This allows the user to enjoy the game while maintaining their health.

[0044] The generation AI can analyze a user's past purchase history and suggest equipment that matches their preferences. For example, it can suggest new equipment that the user will like based on the design and status of previously purchased equipment. The generation AI can also generate equipment that matches a specific theme or style based on the user's purchase history. This allows it to provide equipment that matches the user's preferences.

[0045] The generation AI can analyze a user's social media posts and suggest equipment based on their interests. For example, if a user frequently posts about a particular character or theme, it can suggest equipment related to that character or theme. The generation AI can also generate equipment that matches trends based on the content of the user's social media posts. This makes it possible to provide equipment based on the user's interests.

[0046] The equipment creation system may further include a feedback collection unit that collects user feedback. For example, the system may collect user feedback and suggestions for improvement after using the equipment and reflect these in the next equipment creation. The feedback collection unit may also analyze the user feedback and provide data for improving the design and status of the equipment. This allows the equipment to be improved based on the user feedback.

[0047] The equipment creation system can further include a history management unit that records the user's equipment usage history. For example, the design and status of equipment used in the past can be recorded and used as a reference when the user wants to create the same equipment again. The history management unit can also suggest optimal equipment based on the user's equipment usage history. This makes it possible to suggest equipment based on the user's equipment usage history.

[0048] The equipment creation system may further include a sharing unit for sharing users' equipment designs. For example, a user can share equipment he or she has designed with other users and receive ratings and comments. The sharing unit may also be used to view and refer to other users' equipment designs. This allows users to share equipment designs and communicate with each other.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The generation AI generates equipment based on the user's instructions. For example, if the user inputs specific instructions such as "I want the sword to be shaped like a dragon's wing, have a fire-wrapped technique, and increase attack power," the generation AI analyzes the instructions and generates the equipment design, techniques, and stats. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate equipment based on the user's wishes. Step 2: The 3D printer creates the equipment generated by the generative AI. For example, the design data for a dragon's wing-like sword generated by the generative AI is sent to the 3D printer, which then creates an actual sword of that shape. 3D printers can create equipment with high precision using materials such as plastic and metal. Step 3: The equipment linkage kit links the real equipment created by the 3D printer with the equipment in the game. For example, a sensor is attached to the real sword, and the sensor is linked to the sword's movement and status in the game. The equipment linkage kit uses sensors and communication modules to link the real equipment with the equipment in the game in real time. Step 4: The VR headset makes the equipment linked by the equipment linkage kit available in both the real world and the virtual world. For example, when a user puts on a VR headset and swings a real sword, the same movement is reproduced in the VR space, allowing the user to inflict damage on enemies. VR headsets have high-resolution displays and wide viewing angles, providing users with an immersive experience.

[0051] (Example 2) An equipment generation system according to an embodiment of the present invention uses a generation AI to generate equipment based on user instructions, creates the equipment as a real object using a 3D printer, links the real equipment with in-game equipment using an equipment linking kit, and makes it available in both reality and virtual reality through a VR headset. This allows the equipment generation system to generate equipment based on user instructions, create it as a real object, and make it available in both reality and virtual reality.

[0052] An equipment generation system according to an embodiment includes a generation AI, a 3D printer, an equipment linkage kit, and a VR headset. The generation AI generates equipment based on user instructions. For example, if a user inputs specific instructions such as "I want the sword to be shaped like dragon wings, have a fire-wrapped technique, and increase attack power," the generation AI analyzes the instructions and generates the equipment's design, technique, and stats. The generation AI uses a text generation AI (e.g., LLM) or a multimodal generation AI to generate equipment based on the user's wishes. The 3D printer creates the equipment generated by the generation AI as a physical object. For example, design data for a dragon wing-like sword generated by the generation AI is sent to the 3D printer, which then creates an actual sword of that shape. 3D printers can create equipment with high precision using materials such as plastic and metal. The equipment linkage kit links the physical equipment created by the 3D printer with in-game equipment. For example, a sensor can be attached to the physical sword, and the sensor can be linked to the sword's movement and stats in the game. The equipment linking kit uses sensors and communication modules to link real-world equipment with in-game equipment in real time. A VR headset allows equipment linked by the equipment linking kit to be used in both the real world and the virtual world. For example, when a user wears a VR headset and swings a real sword, the same movement is reproduced in the VR space, allowing the user to inflict damage on enemies. The VR headset has a high-resolution display and a wide viewing angle, providing the user with an immersive experience. This allows the equipment creation system according to the embodiment to generate equipment based on the user's instructions, create it as a real object, and make it available in both the real world and the virtual world. For example, users can fight in the VR space with their own original equipment and enjoy leveling up their in-game skills and stats. Furthermore, by utilizing a payment system, they can differentiate themselves from other players and obtain more powerful equipment.

[0053] The generation AI incorporates an emotion estimation function, allowing it to adjust the design and status of equipment based on the user's emotions. For example, the generation AI incorporates an emotion estimation function and analyzes the user's emotions in real time when designing equipment. For example, if the user is excited, it will suggest a more aggressive design and status. Furthermore, when the user inputs an equipment design, the generation AI uses the emotion estimation function to analyze the user's emotional state and prioritizes generating designs that evoke strong positive emotions. For example, if the user feels strong joy or excitement, it will suggest a flashy design. Furthermore, the generation AI uses the emotion estimation function to automatically adjust the design and status of equipment based on the user's emotions. For example, if the user is relaxed, it will suggest equipment with high defensive power. This allows the design and status of equipment to be adjusted based on the user's emotions.

[0054] The generation AI can analyze a user's past play data and suggest equipment that is optimal for their play style. For example, the generation AI can analyze a user's past play data and suggest equipment based on their play style. For example, it can suggest equipment with high attack power to a user with an offensive play style. The generation AI can also generate equipment designs and stats that are optimal for their play style based on the user's past play data. For example, it can suggest equipment with high defensive power to a user with a defensive-oriented play style. The generation AI can also analyze a user's play data and suggest customizing equipment according to their play style. For example, it can suggest balanced equipment to a user with a balanced play style. This makes it possible to suggest equipment that is optimal for the user's play style.

[0055] The generation AI can receive user feedback in real time and instantly modify the equipment design. For example, the generation AI adds a function to receive user feedback in real time and instantly modify the equipment design. For example, if the user wants to change part of the design, it will be reflected immediately. The generation AI also allows the user to provide feedback on the equipment design in real time and modify the design based on that feedback. For example, changes to color or shape will be reflected immediately. The generation AI also analyzes user feedback in real time and adds a function to instantly modify the equipment design and status. For example, if the user does not like part of the design, it will be modified immediately. This allows the equipment design to be instantly modified based on user feedback.

[0056] The generation AI can create equipment that fits better by reflecting the user's physical data. For example, the generation AI can adjust the design of equipment based on the user's physical data (height, weight, etc.) to create equipment that fits better. For example, it can suggest the size and shape of equipment to suit the user's body type. The generation AI can also input the user's physical data and customize the design and status of equipment based on that data. For example, it can suggest a long sword for a tall user. The generation AI can also analyze the user's physical data and generate designs to improve the fit of equipment. For example, it can suggest highly durable equipment for a heavy user. This makes it possible to create equipment that fits better based on the user's physical data.

[0057] The generation AI can refer to other users' equipment designs and incorporate popular design elements. For example, the generation AI can analyze other users' equipment designs and add a function to incorporate popular design elements. For example, popular design elements can be automatically suggested. The generation AI can also refer to other users' equipment designs and generate equipment that incorporates popular design elements. For example, specific design patterns or colors can be incorporated. The generation AI can also collect other users' equipment design data and add a function to customize equipment based on popular design elements. For example, popular decorations and shapes can be incorporated. This makes it possible to incorporate popular design elements from other users.

[0058] The generative AI can use the emotion estimation function to generate designs that will excite users the most. The generative AI can, for example, use the emotion estimation function to generate designs that will excite users the most. For example, it can adjust the design based on the user's emotion score. The generative AI can also analyze the user's emotional response and suggest highly exciting designs based on that data. For example, it can incorporate design elements with high emotion scores. The generative AI can also use the emotion estimation function to build a system that generates designs that will excite users the most. For example, it can dynamically adjust the design according to changes in the user's emotions. This makes it possible to generate designs that will excite users the most.

[0059] 3D printers can use different materials simultaneously to adjust the strength and flexibility of equipment. 3D printers add the ability to use different materials simultaneously to adjust the strength and flexibility of equipment. For example, a hard material is used for the blade of a sword and a flexible material for the handle. 3D printers also customize the characteristics of equipment by using different materials simultaneously. For example, a hard material is used for the outside of armor and a soft material for the inside. 3D printers also have the ability to use different materials simultaneously to adjust the strength and flexibility of equipment. For example, a highly durable material is used for the surface of a shield and a lightweight material is used for the back. This allows the strength and flexibility of equipment to be adjusted by using different materials simultaneously.

[0060] 3D printers can embed sensors inside equipment and monitor the condition of the equipment in real time. 3D printers, for example, have the ability to embed sensors inside equipment to monitor the condition of the equipment in real time. For example, they can monitor the condition of a sword blade or the durability of armor. 3D printers can also embed sensors inside equipment and create equipment that incorporates those sensors. For example, the sensors can detect the equipment's usage and damage in real time. 3D printers can also embed sensors inside equipment to create a system that monitors the equipment's condition in real time. For example, sensors can monitor the temperature and humidity of the equipment. This allows the equipment's condition to be monitored in real time.

[0061] A 3D printer can use an emotion estimation function to automatically select colors and textures according to the user's emotions. For example, a 3D printer can use the emotion estimation function to automatically select colors and textures according to the user's emotions. For example, if the user is excited, a vivid color is selected. The 3D printer can also analyze the user's emotional response and automatically select colors and textures based on that data. For example, if the user is relaxed, a soft texture is selected. The 3D printer can also use the emotion estimation function to build a system that automatically selects colors and textures according to the user's emotions. For example, if the emotion score is high, a glossy texture is selected. This makes it possible to automatically select colors and textures according to the user's emotions.

[0062] A 3D printer can first create a miniature prototype based on the design data of equipment, allowing the user to check it. For example, a 3D printer can first create a miniature prototype based on the design data of equipment, allowing the user to check it. For example, a miniature sword can be created, and the user can check the design. A 3D printer can also create a miniature prototype first, allowing the user to check the equipment design and make modifications as necessary. For example, a miniature armor can be created, and the user can check the fit. A system can also be built in which a 3D printer can create a miniature prototype based on the design data of equipment, allowing the user to check it. For example, the overall appearance of the equipment can be checked using a miniature version. This allows the user to check the miniature prototype.

[0063] 3D printers can modularize parts of equipment based on equipment design data and create them as interchangeable parts. For example, 3D printers can modularize parts of equipment based on equipment design data and create interchangeable parts. For example, they can create sword blades and handles as interchangeable parts. 3D printers can also create modularized parts based on equipment design data, allowing users to freely combine them. For example, they can make each part of armor interchangeable. 3D printers can also build systems that modularize parts of equipment based on equipment design data and create them as interchangeable parts. For example, this makes it easier to customize equipment. This allows parts of equipment to be modularized and created as interchangeable parts.

[0064] A 3D printer can use an emotion estimation function to make fine adjustments to achieve a finish that satisfies the user most. For example, a 3D printer can use the emotion estimation function to make fine adjustments to achieve a finish that satisfies the user most. For example, the color and texture can be adjusted based on the user's emotion score. The 3D printer can also analyze the user's emotional response and fine-tune the finish based on that data. For example, a glossy finish can be selected if the emotion score is high. The 3D printer can also use the emotion estimation function to build a system that makes fine adjustments to achieve a finish that satisfies the user most. For example, the finish can be dynamically adjusted in response to changes in the user's emotion. This allows fine adjustments to be made to achieve a finish that satisfies the user most.

[0065] The equipment linking kit can use an emotion estimation function to provide feedback according to the user's emotions. The equipment linking kit, for example, uses the emotion estimation function to provide feedback according to the user's emotions. For example, if the user is excited, stronger feedback is provided. The equipment linking kit also analyzes the user's emotional response and adjusts the feedback based on that data. For example, if the user is relaxed, gentle feedback is provided. The equipment linking kit also uses the emotion estimation function to build a system that provides feedback according to the user's emotions. For example, the strength of the feedback is adjusted according to changes in the user's emotions. This makes it possible to provide feedback according to the user's emotions.

[0066] The equipment linking kit can track the user's movements with high accuracy and reflect them in in-game actions. The equipment linking kit, for example, tracks the user's movements with high accuracy and reflects them in in-game actions. For example, it accurately reflects sword swings and defensive movements in the game. The equipment linking kit also tracks the user's movements in real time and reproduces in-game actions based on that data. For example, it accurately reflects jumps and evasion movements. Furthermore, using high-accuracy tracking technology, the equipment linking kit builds a system that reflects the user's movements in the game. For example, it accurately reproduces small hand movements and body movements. This allows the user's movements to be tracked with high accuracy and reflected in in-game actions.

[0067] The equipment linking kit can monitor the user's health state and affect the in-game status. The equipment linking kit, for example, monitors the user's health state (heart rate, body temperature, etc.) and affects the in-game status. For example, if the heart rate is high, attack power increases. The equipment linking kit also monitors the user's health state in real time and adjusts the in-game status based on that data. For example, if the body temperature is high, defensive power increases. Furthermore, a system is constructed in which sensors that monitor health state are incorporated into equipment and the equipment linking kit dynamically adjusts the in-game status based on that data. For example, if the heart rate is low, recovery power increases. In this way, the user's health state can be monitored and affect the in-game status.

[0068] The equipment linkage kit can link with other smart devices to provide more diverse feedback. The equipment linkage kit can link with other smart devices (smartwatches, smart glasses, etc.) to provide more diverse feedback. For example, it uses vibrations from a smartwatch or visual feedback from smart glasses. The equipment linkage kit also builds a system that provides more diverse feedback by linking with other smart devices. For example, it displays in-game information through smart glasses. Furthermore, by strengthening the linkage with smart devices, the equipment linkage kit can provide more diverse feedback to the user. For example, it adjusts feedback using heart rate data from a smartwatch. This allows it to link with other smart devices to provide more diverse feedback.

[0069] The equipment linking kit can be designed to be compatible with different game platforms. For example, the equipment linking kit can be designed to be compatible with different game platforms (PC, console, mobile). For example, it can be used in both PC games and console games. The equipment linking kit can also be optimized in design to ensure compatibility across different platforms. For example, it can be used in mobile games. The equipment linking kit can also be used to build a system designed to be compatible with multiple game platforms. For example, it can synchronize data between different platforms. This allows it to be designed to be compatible with different game platforms.

[0070] The equipment linking kit can use the emotion estimation function to provide the user with the most enjoyable gameplay experience. The equipment linking kit, for example, uses the emotion estimation function to provide the user with the most enjoyable gameplay experience. For example, it adjusts in-game events based on the user's emotion score. The equipment linking kit also analyzes the user's emotional response and optimizes the gameplay experience based on the data. For example, it increases the difficulty level when the emotion score is high. The emotion estimation function is also used to build a system in which the equipment linking kit provides the user with the most enjoyable gameplay experience. For example, it adjusts in-game feedback according to changes in the user's emotions. This allows the user to have the most enjoyable gameplay experience.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The equipment generation system may further include a health management unit that monitors the user's health condition. For example, it may measure the user's heart rate and body temperature and adjust the equipment status according to the user's health condition. It is possible to adjust the equipment status by increasing attack power when the heart rate is high and increasing defense power when the body temperature is low. The health management unit may also display an alert urging the user to take a break based on the user's health condition. This allows the user to enjoy the game while maintaining their health.

[0073] The generation AI can analyze a user's past purchase history and suggest equipment that matches their preferences. For example, it can suggest new equipment that the user will like based on the design and status of previously purchased equipment. The generation AI can also generate equipment that matches a specific theme or style based on the user's purchase history. This allows it to provide equipment that matches the user's preferences.

[0074] The generation AI can analyze a user's social media posts and suggest equipment based on their interests. For example, if a user frequently posts about a particular character or theme, it can suggest equipment related to that character or theme. The generation AI can also generate equipment that matches trends based on the content of the user's social media posts. This makes it possible to provide equipment based on the user's interests.

[0075] The generative AI can estimate the user's emotions and suggest equipment to reduce stress. For example, if the user is feeling stressed, it will suggest equipment with a design or status that has a relaxing effect. The generative AI can also customize equipment that helps reduce stress based on the user's emotional state. This makes it possible to provide equipment that suits the user's emotional state.

[0076] The generative AI can estimate the user's emotions and suggest equipment to boost motivation. For example, if the user is feeling unmotivated, it will suggest equipment with a design or status that will boost motivation. The generative AI can also customize equipment that helps boost motivation based on the user's emotional state. This makes it possible to provide equipment that matches the user's emotional state.

[0077] The generation AI can estimate the user's emotions and suggest equipment that has a relaxing effect. For example, if the user is tense, it will suggest equipment with a design or status that has a relaxing effect. The generation AI can also customize equipment that enhances the relaxation effect based on the user's emotional state. This makes it possible to provide equipment that suits the user's emotional state.

[0078] The generative AI can estimate the user's emotions and suggest equipment to improve concentration. For example, if the user is lacking in concentration, it will suggest equipment with a design or status that will improve concentration. The generative AI can also customize equipment that helps improve concentration based on the user's emotional state. This makes it possible to provide equipment that matches the user's emotional state.

[0079] The equipment creation system may further include a feedback collection unit that collects user feedback. For example, the system may collect user feedback and suggestions for improvement after using the equipment and reflect these in the next equipment creation. The feedback collection unit may also analyze the user feedback and provide data for improving the design and status of the equipment. This allows the equipment to be improved based on the user feedback.

[0080] The equipment creation system can further include a history management unit that records the user's equipment usage history. For example, the design and status of equipment used in the past can be recorded and used as a reference when the user wants to create the same equipment again. The history management unit can also suggest optimal equipment based on the user's equipment usage history. This makes it possible to suggest equipment based on the user's equipment usage history.

[0081] The equipment creation system may further include a sharing unit for sharing users' equipment designs. For example, a user can share equipment he or she has designed with other users and receive ratings and comments. The sharing unit may also be used to view and refer to other users' equipment designs. This allows users to share equipment designs and communicate with each other.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The generation AI generates equipment based on the user's instructions. For example, if the user inputs specific instructions such as "I want the sword to be shaped like a dragon's wing, have a fire-wrapped technique, and increase attack power," the generation AI analyzes the instructions and generates the equipment design, techniques, and stats. The generation AI uses text generation AI (e.g., LLM) or multimodal generation AI to generate equipment based on the user's wishes. Step 2: The 3D printer creates the equipment generated by the generative AI. For example, the design data for a dragon's wing-like sword generated by the generative AI is sent to the 3D printer, which then creates an actual sword of that shape. 3D printers can create equipment with high precision using materials such as plastic and metal. Step 3: The equipment linkage kit links the real equipment created by the 3D printer with the equipment in the game. For example, a sensor is attached to the real sword, and the sensor is linked to the sword's movement and status in the game. The equipment linkage kit uses sensors and communication modules to link the real equipment with the equipment in the game in real time. Step 4: The VR headset makes the equipment linked by the equipment linkage kit available in both the real world and the virtual world. For example, when a user puts on a VR headset and swings a real sword, the same movement is reproduced in the VR space, allowing the user to inflict damage on enemies. VR headsets have high-resolution displays and wide viewing angles, providing users with an immersive experience.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, 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. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 generation AI that generates equipment based on user instructions using a generation AI; a 3D printer that creates the equipment generated by the generation AI as a real object; An equipment linking kit that links the real equipment created by the 3D printer with equipment in the game; a VR headset that enables the equipment linked by the equipment linking kit to be used both in reality and virtually. A system characterized by:

2. The generated AI is Incorporates emotion estimation functionality to adjust the design and status of the equipment based on the user's emotions.

2. The system of claim 1.

3. The generated AI is Analyze the user's past play data and suggest the equipment that best suits their play style.

2. The system of claim 1.

4. The generated AI is Receive real-time user feedback and instantly modify the design of the device 2. The system of claim 1.

5. The generated AI is Reflecting the user's physical data to create equipment that fits better 2. The system of claim 1.

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