system

Abstract

The system according to the embodiment aims to seamlessly output 3D model data generated in a VR environment in the real world. [Solution] A system according to an embodiment includes an acquisition unit, a conversion unit, and an output unit. The acquisition unit acquires 3D model data generated within a VR environment. The conversion unit analyzes the 3D model data acquired by the acquisition unit and converts it into a format that can be output by a 3D printer. The output unit transmits the data converted by the conversion unit to the 3D printer, and performs 3D printing in the real world.

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, the process of outputting 3D model data generated in a VR environment into the real world was complicated, making it difficult to do so seamlessly.

[0005] The system according to the embodiment aims to seamlessly output 3D model data generated in a VR environment in the real world. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a conversion unit, and an output unit. The acquisition unit acquires 3D model data generated in a VR environment. The conversion unit analyzes the 3D model data acquired by the acquisition unit and converts it into a format that can be output by a 3D printer. The output unit transmits the data converted by the conversion unit to the 3D printer, and performs 3D printing in the real world. [Effects of the Invention]

[0007] The system according to the embodiment can seamlessly output 3D model data generated in a VR environment in the real 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) A system according to an embodiment of the present invention automatically acquires 3D model data generated in a VR environment, analyzes it using a generation AI, converts it into a format that can be output by a 3D printer, and performs 3D printing in the real world. This system acquires 3D model data generated in the VR environment, analyzes it using a generation AI, converts it into a format that can be output by a 3D printer, and performs 3D printing in the real world, thereby seamlessly teleporting objects created in the VR world to the real world. For example, the system collects data on objects created by a user in the VR environment. For example, it can acquire 3D model data for characters and items created in a VR game. Next, the system uses a generation AI to automatically convert the acquired 3D model data into a format that can be output by a 3D printer. The generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. For example, it can convert an object with a complex shape created in the VR world into a shape that can be output by a 3D printer. Next, the system transmits the converted data to a 3D printer and performs 3D printing in the real world. For example, it sends the converted data to a 3D printer to actually print the object. This allows the system to seamlessly teleport objects created in the VR world to the real world. This allows the system to bring creative activities in the VR world back into the real world, allowing users to actually touch the objects they create. For example, a character figurine created in a VR game can be 3D printed and displayed in the real world. Also, a prototype created using a VR design tool can be 3D printed and viewed as a physical object. In this way, the system realizes seamless teleportation between the VR world and the real world, enriching users' creative activities.

[0029] A system according to an embodiment includes an acquisition unit, a conversion unit, and an output unit. The acquisition unit acquires 3D model data generated within a VR environment. For example, the acquisition unit collects data on objects created by a user within the VR environment. The acquisition unit can also acquire 3D model data of characters and items created within a VR game. The conversion unit uses a generation AI to analyze the 3D model data acquired by the acquisition unit and convert it into a format that can be output by a 3D printer. For example, the conversion unit converts a complex-shaped object created in the VR world into a shape that can be output by a 3D printer. The generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. For example, the generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. The output unit transmits the data converted by the conversion unit to a 3D printer and performs 3D printing in the real world. For example, the output unit transmits the converted data to a 3D printer and actually prints the object. This enables the system according to an embodiment to 3D print 3D model data generated in the VR world in the real world.

[0030] The acquisition unit can collect data on objects created by the user in the VR environment. For example, the acquisition unit collects data on objects created by the user in the VR environment. For example, the acquisition unit acquires 3D model data of characters and items created in a VR game. The acquisition unit can also collect data on prototypes created using a VR design tool. This allows data on objects created by the user in the VR environment to be collected efficiently. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input data on objects created by the user in the VR environment into AI and cause the AI ​​to collect the data.

[0031] The conversion unit can analyze the 3D model data and convert it into a format that can be output by a 3D printer. The conversion unit can, for example, use a generation AI to analyze the 3D model data and convert it into a format that can be output by a 3D printer. For example, the conversion unit can convert an object with a complex shape created in a VR world into a shape that can be output by a 3D printer. The generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. For example, the generation AI can analyze the 3D model data and convert it into a format that can be output by a 3D printer. This allows the 3D model data to be automatically converted into a format that can be output by a 3D printer. Some or all of the above-described processing in the conversion unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the conversion unit can input the 3D model data into the generation AI and have the generation AI convert the data.

[0032] The output unit transmits the converted data to a 3D printer, allowing 3D printing to be performed in the real world. For example, the output unit transmits the converted data to a 3D printer, which actually prints the object. For example, the output unit transmits the converted data to a 3D printer, which actually prints the object. The output unit can also automatically adjust the settings of the 3D printer to obtain optimal printing results. For example, the output unit can automatically adjust the settings of the 3D printer to obtain optimal printing results. This allows the converted data to be transmitted to the 3D printer, allowing 3D printing to be performed in the real world. Some or all of the above-described processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the converted data to AI and have the AI ​​print the data.

[0033] The conversion unit can convert an object of a complex shape created in the VR world into a shape that can be output by a 3D printer. The conversion unit, for example, uses a generation AI to convert an object of a complex shape created in the VR world into a shape that can be output by a 3D printer. For example, the conversion unit converts an object of a complex shape created in the VR world into a shape that can be output by a 3D printer. The generation AI analyzes 3D model data and converts it into a format that can be output by a 3D printer. For example, the generation AI analyzes 3D model data and converts it into a format that can be output by a 3D printer. This allows the object of a complex shape to be converted into a shape that can be output by a 3D printer. Some or all of the above-described processing in the conversion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the conversion unit inputs 3D model data of a complex shape into the generation AI and causes the generation AI to convert the data.

[0034] The acquisition unit can analyze the user's past activity history in the VR environment and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past activity history in the VR environment and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquiring data of objects that the user frequently used in the past. The acquisition unit can also predict the type of data to be acquired during a specific time period based on the user's past activity history and acquire the data in the optimal manner. The acquisition unit can also analyze the user's past operation patterns and select the most efficient acquisition method. This allows the optimal acquisition method to be selected based on the user's past activity history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's past activity history data into AI and have the AI ​​analyze the data.

[0035] The acquisition unit may perform filtering based on the user's current project or area of ​​interest when acquiring 3D model data. For example, the acquisition unit may perform filtering based on the user's current project or area of ​​interest when acquiring 3D model data. For example, the acquisition unit may preferentially acquire 3D model data related to a project currently underway by the user. The acquisition unit may also filter and acquire highly relevant 3D model data based on the user's area of ​​interest. The acquisition unit may also acquire 3D model data related to areas in which the user has previously shown interest. This allows highly relevant data to be acquired based on the user's current project or area of ​​interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit may input data on the user's project or area of ​​interest into AI and have the AI ​​filter the data.

[0036] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring 3D model data. For example, when acquiring 3D model data, the acquisition unit selects the optimal acquisition means depending on the user's input method. For example, if the user uses voice input, the acquisition unit acquires 3D model data based on a voice command. Also, if the user uses text input, the acquisition unit can acquire 3D model data based on a keyword. Also, if the user uses gesture input, the acquisition unit can acquire 3D model data corresponding to the gesture. This makes it possible to select the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to AI and have the AI ​​select the optimal acquisition means.

[0037] When acquiring 3D model data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when acquiring 3D model data, the acquisition unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring 3D model data related to that area. Furthermore, when the user is traveling, the acquisition unit can also acquire 3D model data related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also acquire 3D model data related to the area around the user's home. This makes it possible to acquire highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to acquire highly relevant data.

[0038] The acquisition unit can analyze the user's social media activities and acquire related data when acquiring 3D model data. For example, the acquisition unit can analyze the user's social media activities and acquire related data when acquiring 3D model data. For example, the acquisition unit can acquire 3D model data based on images and videos shared by the user on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related 3D model data. The acquisition unit can also acquire related 3D model data by referring to the activities of the user's friends on social media. This makes it possible to acquire related data based on the user's social media activities. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media data into AI and have the AI ​​analyze the data.

[0039] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring 3D model data. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring 3D model data. For example, the acquisition unit prioritizes acquisition of 3D model data that the user has previously rated highly. The acquisition unit can also acquire 3D model data by excluding 3D model data that the user has previously rated poorly. The acquisition unit can also customize the optimal acquisition method based on the user's past feedback. This allows the acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into AI and have the AI ​​analyze the data.

[0040] The conversion unit can adjust the level of detail of the conversion based on the importance of the 3D model during conversion. For example, the conversion unit can adjust the level of detail of the conversion based on the importance of the 3D model during conversion. For example, the conversion unit can perform detailed conversion on 3D models with high importance to increase accuracy. The conversion unit can also perform simplified conversion on 3D models with low importance to shorten processing time. The conversion unit can also gradually adjust the level of detail of the conversion according to the importance. This allows the level of detail of the conversion to be adjusted according to the importance of the 3D model. Some or all of the above-mentioned processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input importance data of the 3D model to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0041] The conversion unit can apply different conversion algorithms depending on the category of the 3D model during conversion. For example, the conversion unit applies different conversion algorithms depending on the category of the 3D model during conversion. For example, the conversion unit applies a conversion algorithm dedicated to architecture to a 3D model of a building. The conversion unit can also apply a conversion algorithm dedicated to characters to a 3D model of a character. The conversion unit can also apply a conversion algorithm dedicated to product design to a 3D model of a product design. This makes it possible to apply an optimal conversion algorithm depending on the category of the 3D model. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, a generation AI. For example, the conversion unit can input category data of the 3D model to the generation AI and cause the generation AI to apply the optimal conversion algorithm.

[0042] The conversion unit can improve the accuracy of conversion by referring to the user's past conversion results during conversion. For example, the conversion unit improves the accuracy of conversion by referring to the user's past conversion results during conversion. For example, the conversion unit improves the accuracy of similar conversions based on the user's past conversion results. The conversion unit can also analyze errors from the user's past conversion results and reflect them in the next conversion. The conversion unit can also learn the user's past conversion results and suggest the optimal conversion method. This makes it possible to improve the accuracy of conversion by referring to the user's past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input the user's past conversion result data into the generation AI and have the generation AI improve the conversion accuracy.

[0043] The conversion unit can determine the priority of conversion based on the creation date of the 3D model during conversion. For example, the conversion unit determines the priority of conversion based on the creation date of the 3D model during conversion. For example, the conversion unit prioritizes conversion of the most recent 3D model. The conversion unit can also convert older 3D models later. The conversion unit can also gradually adjust the priority of conversion according to the creation date. This allows the priority of conversion to be determined based on the creation date of the 3D model. Some or all of the above-described processing in the conversion unit may be performed using, or without, the generation AI. For example, the conversion unit can input creation date data of the 3D model into the generation AI and have the generation AI determine the priority of conversion.

[0044] The conversion unit can adjust the order of conversion based on the relevance of the 3D models during conversion. For example, the conversion unit can prioritize converting 3D models related to the user's current project. The conversion unit can also convert less relevant 3D models later. The conversion unit can also gradually adjust the order of conversion according to the relevance. This allows the order of conversion to be adjusted based on the relevance of the 3D models. Some or all of the above-described processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input relevance data of the 3D models into the generation AI and cause the generation AI to adjust the order of conversion.

[0045] The conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise during conversion. For example, the conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise during conversion. For example, the conversion unit performs conversion that uses a lot of technical terminology for users with high levels of expertise. The conversion unit can also perform conversion that explains things in simpler terms for users with low levels of expertise. The conversion unit can also gradually adjust the use of technical terminology according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the conversion according to the user's level of expertise. Some or all of the above-mentioned processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0046] The output unit can analyze the user's past 3D printing history and select the optimal output method at the time of output. For example, the output unit can select the optimal output method at the time of output by analyzing the user's past 3D printing history. For example, the output unit can select the optimal output method based on the user's past successful 3D printing settings. The output unit can also analyze errors from the user's past 3D printing history and reflect them in the next output. The output unit can also learn the user's past 3D printing history and suggest the optimal output method. This allows the optimal output method to be selected based on the user's past 3D printing history. Some or all of the above-described processing in the output unit can be performed using, or without, AI. For example, the output unit can input the user's past 3D printing history data into AI and have the AI ​​select the optimal output method.

[0047] The output unit can customize the output means based on the user's current project at the time of output. For example, the output unit can provide optimal 3D printing settings for the user's current project. The output unit can also adjust the output means according to the progress of the user's project. The output unit can also suggest the optimal output means based on the type of the user's project. This allows the optimal output means to be provided based on the user's current project. Some or all of the above-described processing in the output unit can be performed using, or without, AI. For example, the output unit can input the user's project data into AI and have the AI ​​customize the output means.

[0048] The output unit can improve the output method by reflecting user feedback at the time of output. For example, the output unit can improve the output method by reflecting user feedback at the time of output. For example, the output unit can adjust output settings based on feedback provided by the user in the past. The output unit can also improve the output method by reflecting user feedback in real time. The output unit can also analyze user feedback and propose an optimal output method. This makes it possible to improve the output method based on user feedback. Some or all of the above-mentioned processing in the output unit can be performed using AI, for example, or can be performed without using AI. For example, the output unit can input user feedback data into AI and have the AI ​​improve the output method.

[0049] The output unit can select the optimal output method at the time of output by taking into account the user's geographical location information. For example, the output unit selects the optimal output method at the time of output by taking into account the user's geographical location information. For example, when the user is in a specific area, the output unit provides the optimal output method for that area. Furthermore, when the user is traveling, the output unit can also suggest an output method suitable for the travel destination. Furthermore, when the user is at home, the output unit can also provide the optimal output method for the home environment. In this way, the optimal output method can be selected based on the user's geographical location information. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's geographical location information to AI and have the AI ​​select the optimal output method.

[0050] The output unit can analyze the user's social media activity and suggest an output method at the time of output. For example, the output unit can analyze the user's social media activity and suggest an output method at the time of output. For example, the output unit can suggest an output method based on images and videos shared by the user on social media. The output unit can also analyze the content of the user's social media posts and suggest a related output method. The output unit can also suggest a related output method by referring to the activity of the user's friends on social media. This makes it possible to suggest the optimal output method based on the user's social media activity. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media data into AI and have the AI ​​analyze the data.

[0051] The output unit can customize the output method by reflecting the user's past feedback at the time of output. The output unit, for example, customizes the output method by reflecting the user's past feedback at the time of output. For example, the output unit preferentially uses output methods that the user has previously rated highly. The output unit can also exclude output methods that the user has previously rated poorly. The output unit can also customize the optimal output method based on the user's past feedback. This makes it possible to customize the output method based on the user's past feedback. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's past feedback data into AI and have the AI ​​customize the output method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] When converting 3D model data, the conversion unit can learn the user's past design style and perform conversion that suits the user's preferences. For example, the conversion unit analyzes the characteristics of designs created by the user in the past and performs conversion in a similar style. The conversion unit can also perform conversion that reflects the user's preferred colors and shapes. Furthermore, the conversion unit can learn the user's design patterns and suggest the optimal conversion method. This makes it possible to convert 3D model data according to the user's preferences.

[0054] The acquisition unit can analyze a user's social media activity and acquire 3D model data based on topics in which the user is interested. For example, the acquisition unit can analyze the content that the user frequently shares on social media and prioritize acquiring related 3D model data. The acquisition unit can also acquire data based on content shared by the user's followers and friends. Furthermore, the acquisition unit can learn the user's social media activity patterns and suggest the optimal data acquisition method. This makes it possible to acquire 3D model data based on the user's interests.

[0055] The output unit can analyze the user's past print history and automatically select the optimal print settings during 3D printing. For example, the output unit applies similar settings based on the user's past successful print settings. The output unit can also analyze past failures and suggest settings to avoid errors. Furthermore, the output unit can learn the user's print history and provide the optimal printing method. This allows the optimal print settings to be selected based on the user's past print history.

[0056] When converting 3D model data, the conversion unit can select the optimal conversion method by taking into account the user's geographical location information. For example, if the user is in a specific area, the conversion unit can apply a conversion method suitable for that area. Also, if the user is traveling, the conversion unit can suggest a conversion method suitable for the user's destination. Furthermore, if the user is at home, the conversion unit can provide a conversion method that is optimal for the user's home environment. This allows the optimal conversion method to be selected based on the user's geographical location information.

[0057] The acquisition unit can improve the 3D model data acquisition method by reflecting the user's past feedback. For example, the acquisition unit can prioritize the use of data acquisition methods that the user has previously rated highly. It can also acquire data by excluding methods that the user has rated poorly. Furthermore, the acquisition unit can analyze the user's feedback and suggest the optimal data acquisition method. This allows the 3D model data acquisition method to be improved based on the user's past feedback.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The acquisition unit acquires 3D model data generated within the VR environment. For example, it collects data on objects created by the user within the VR environment, or 3D model data on characters and items created within a VR game. Step 2: The conversion unit uses the generation AI to analyze the 3D model data acquired by the acquisition unit and convert it into a format that can be output by a 3D printer. For example, it converts an object with a complex shape created in the VR world into a shape that can be output by a 3D printer. Step 3: The output unit transmits the data converted by the conversion unit to a 3D printer and performs 3D printing in the real world. For example, the converted data is sent to a 3D printer to actually print the object.

[0060] (Example 2) A system according to an embodiment of the present invention automatically acquires 3D model data generated in a VR environment, analyzes it using a generation AI, converts it into a format that can be output by a 3D printer, and performs 3D printing in the real world. This system acquires 3D model data generated in the VR environment, analyzes it using a generation AI, converts it into a format that can be output by a 3D printer, and performs 3D printing in the real world, thereby seamlessly teleporting objects created in the VR world to the real world. For example, the system collects data on objects created by a user in the VR environment. For example, it can acquire 3D model data for characters and items created in a VR game. Next, the system uses a generation AI to automatically convert the acquired 3D model data into a format that can be output by a 3D printer. The generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. For example, it can convert an object with a complex shape created in the VR world into a shape that can be output by a 3D printer. Next, the system transmits the converted data to a 3D printer and performs 3D printing in the real world. For example, it sends the converted data to a 3D printer to actually print the object. This allows the system to seamlessly teleport objects created in the VR world to the real world. This allows the system to bring creative activities in the VR world back into the real world, allowing users to actually touch the objects they create. For example, a character figurine created in a VR game can be 3D printed and displayed in the real world. Also, a prototype created using a VR design tool can be 3D printed and viewed as a physical object. In this way, the system realizes seamless teleportation between the VR world and the real world, enriching users' creative activities.

[0061] A system according to an embodiment includes an acquisition unit, a conversion unit, and an output unit. The acquisition unit acquires 3D model data generated within a VR environment. For example, the acquisition unit collects data on objects created by a user within the VR environment. The acquisition unit can also acquire 3D model data of characters and items created within a VR game. The conversion unit uses a generation AI to analyze the 3D model data acquired by the acquisition unit and convert it into a format that can be output by a 3D printer. For example, the conversion unit converts a complex-shaped object created in the VR world into a shape that can be output by a 3D printer. The generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. For example, the generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. The output unit transmits the data converted by the conversion unit to a 3D printer and performs 3D printing in the real world. For example, the output unit transmits the converted data to a 3D printer and actually prints the object. This enables the system according to an embodiment to 3D print 3D model data generated in the VR world in the real world.

[0062] The acquisition unit can collect data on objects created by the user in the VR environment. For example, the acquisition unit collects data on objects created by the user in the VR environment. For example, the acquisition unit acquires 3D model data of characters and items created in a VR game. The acquisition unit can also collect data on prototypes created using a VR design tool. This allows data on objects created by the user in the VR environment to be collected efficiently. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input data on objects created by the user in the VR environment into AI and cause the AI ​​to collect the data.

[0063] The conversion unit can analyze the 3D model data and convert it into a format that can be output by a 3D printer. The conversion unit can, for example, use a generation AI to analyze the 3D model data and convert it into a format that can be output by a 3D printer. For example, the conversion unit can convert an object with a complex shape created in a VR world into a shape that can be output by a 3D printer. The generation AI analyzes the 3D model data and converts it into a format that can be output by a 3D printer. For example, the generation AI can analyze the 3D model data and convert it into a format that can be output by a 3D printer. This allows the 3D model data to be automatically converted into a format that can be output by a 3D printer. Some or all of the above-described processing in the conversion unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the conversion unit can input the 3D model data into the generation AI and have the generation AI convert the data.

[0064] The output unit transmits the converted data to a 3D printer, allowing 3D printing to be performed in the real world. For example, the output unit transmits the converted data to a 3D printer, which actually prints the object. For example, the output unit transmits the converted data to a 3D printer, which actually prints the object. The output unit can also automatically adjust the settings of the 3D printer to obtain optimal printing results. For example, the output unit can automatically adjust the settings of the 3D printer to obtain optimal printing results. This allows the converted data to be transmitted to the 3D printer, allowing 3D printing to be performed in the real world. Some or all of the above-described processing in the output unit may be performed using AI, for example, or may be performed without using AI. For example, the output unit can input the converted data to AI and have the AI ​​print the data.

[0065] The conversion unit can convert an object of a complex shape created in the VR world into a shape that can be output by a 3D printer. The conversion unit, for example, uses a generation AI to convert an object of a complex shape created in the VR world into a shape that can be output by a 3D printer. For example, the conversion unit converts an object of a complex shape created in the VR world into a shape that can be output by a 3D printer. The generation AI analyzes 3D model data and converts it into a format that can be output by a 3D printer. For example, the generation AI analyzes 3D model data and converts it into a format that can be output by a 3D printer. This allows the object of a complex shape to be converted into a shape that can be output by a 3D printer. Some or all of the above-described processing in the conversion unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the conversion unit inputs 3D model data of a complex shape into the generation AI and causes the generation AI to convert the data.

[0066] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring 3D model data based on the estimated user's emotion. For example, the acquisition unit can estimate the user's emotion and adjust the timing of acquiring 3D model data based on the estimated user's emotion. For example, if the user is excited, the acquisition unit can immediately acquire 3D model data and quickly proceed to the next step. Furthermore, if the user is relaxed, the acquisition unit can acquire 3D model data at regular intervals to maintain the flow of work. Furthermore, if the user is concentrating, the acquisition unit can acquire 3D model data when the user completes a task. This allows the timing of acquiring 3D model data to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit can be performed using, for example, an AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0067] The acquisition unit can analyze the user's past activity history in the VR environment and select the optimal acquisition method. The acquisition unit, for example, analyzes the user's past activity history in the VR environment and selects the optimal acquisition method. For example, the acquisition unit prioritizes acquiring data of objects that the user frequently used in the past. The acquisition unit can also predict the type of data to be acquired during a specific time period based on the user's past activity history and acquire the data in the optimal manner. The acquisition unit can also analyze the user's past operation patterns and select the most efficient acquisition method. This allows the optimal acquisition method to be selected based on the user's past activity history. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's past activity history data into AI and have the AI ​​analyze the data.

[0068] The acquisition unit may perform filtering based on the user's current project or area of ​​interest when acquiring 3D model data. For example, the acquisition unit may perform filtering based on the user's current project or area of ​​interest when acquiring 3D model data. For example, the acquisition unit may preferentially acquire 3D model data related to a project currently underway by the user. The acquisition unit may also filter and acquire highly relevant 3D model data based on the user's area of ​​interest. The acquisition unit may also acquire 3D model data related to areas in which the user has previously shown interest. This allows highly relevant data to be acquired based on the user's current project or area of ​​interest. Some or all of the above-described processing by the acquisition unit may be performed using, or without, AI. For example, the acquisition unit may input data on the user's project or area of ​​interest into AI and have the AI ​​filter the data.

[0069] The acquisition unit can select the optimal acquisition means depending on the user's input method when acquiring 3D model data. For example, when acquiring 3D model data, the acquisition unit selects the optimal acquisition means depending on the user's input method. For example, if the user uses voice input, the acquisition unit acquires 3D model data based on a voice command. Also, if the user uses text input, the acquisition unit can acquire 3D model data based on a keyword. Also, if the user uses gesture input, the acquisition unit can acquire 3D model data corresponding to the gesture. This makes it possible to select the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's input data to AI and have the AI ​​select the optimal acquisition means.

[0070] The acquisition unit can estimate the user's emotion and determine the priority of the 3D model data to be acquired based on the estimated user's emotion. The acquisition unit, for example, estimates the user's emotion and determines the priority of the 3D model data to be acquired based on the estimated user's emotion. For example, when the user is excited, the acquisition unit prioritizes acquiring 3D model data with high importance. Furthermore, when the user is relaxed, the acquisition unit can also postpone acquiring 3D model data with low importance. Furthermore, when the user is concentrating, the acquisition unit can prioritize acquiring 3D model data related to work. This allows the priority of the 3D model data to be determined according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the acquisition unit may be performed using an AI, for example, or without an AI. For example, the acquisition unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0071] When acquiring 3D model data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. For example, when acquiring 3D model data, the acquisition unit prioritizes acquiring highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring 3D model data related to that area. Furthermore, when the user is traveling, the acquisition unit can also acquire 3D model data related to the travel destination. Furthermore, when the user is at home, the acquisition unit can also acquire 3D model data related to the area around the user's home. This makes it possible to acquire highly relevant data based on the user's geographical location information. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit may input the user's geographical location information to AI and cause the AI ​​to acquire highly relevant data.

[0072] The acquisition unit can analyze the user's social media activities and acquire related data when acquiring 3D model data. For example, the acquisition unit can analyze the user's social media activities and acquire related data when acquiring 3D model data. For example, the acquisition unit can acquire 3D model data based on images and videos shared by the user on social media. The acquisition unit can also analyze the content of the user's social media posts and acquire related 3D model data. The acquisition unit can also acquire related 3D model data by referring to the activities of the user's friends on social media. This makes it possible to acquire related data based on the user's social media activities. Some or all of the above-described processing by the acquisition unit can be performed using, for example, AI, or can be performed without using AI. For example, the acquisition unit can input the user's social media data into AI and have the AI ​​analyze the data.

[0073] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring 3D model data. For example, the acquisition unit customizes the acquisition method by reflecting the user's past feedback when acquiring 3D model data. For example, the acquisition unit prioritizes acquisition of 3D model data that the user has previously rated highly. The acquisition unit can also acquire 3D model data by excluding 3D model data that the user has previously rated poorly. The acquisition unit can also customize the optimal acquisition method based on the user's past feedback. This allows the acquisition method to be customized based on the user's past feedback. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's past feedback data into AI and have the AI ​​analyze the data.

[0074] The conversion unit can estimate the user's emotion and adjust the conversion expression method based on the estimated user's emotion. For example, the conversion unit can estimate the user's emotion and adjust the conversion expression method based on the estimated user's emotion. For example, if the user is relaxed, the conversion unit can perform conversion at a leisurely pace. If the user is in a hurry, the conversion unit can also perform conversion quickly and provide results in the shortest time. If the user is excited, the conversion unit can also perform conversion with a visually stimulating effect. This allows the conversion expression method to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the conversion unit can be performed using, for example, the generation AI. For example, the conversion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] The conversion unit can adjust the level of detail of the conversion based on the importance of the 3D model during conversion. For example, the conversion unit can adjust the level of detail of the conversion based on the importance of the 3D model during conversion. For example, the conversion unit can perform detailed conversion on 3D models with high importance to increase accuracy. The conversion unit can also perform simplified conversion on 3D models with low importance to shorten processing time. The conversion unit can also gradually adjust the level of detail of the conversion according to the importance. This allows the level of detail of the conversion to be adjusted according to the importance of the 3D model. Some or all of the above-mentioned processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input importance data of the 3D model to the generation AI and cause the generation AI to adjust the level of detail of the conversion.

[0076] The conversion unit can apply different conversion algorithms depending on the category of the 3D model during conversion. For example, the conversion unit applies different conversion algorithms depending on the category of the 3D model during conversion. For example, the conversion unit applies a conversion algorithm dedicated to architecture to a 3D model of a building. The conversion unit can also apply a conversion algorithm dedicated to characters to a 3D model of a character. The conversion unit can also apply a conversion algorithm dedicated to product design to a 3D model of a product design. This makes it possible to apply an optimal conversion algorithm depending on the category of the 3D model. Some or all of the above-mentioned processing in the conversion unit may be performed using, or without, a generation AI. For example, the conversion unit can input category data of the 3D model to the generation AI and cause the generation AI to apply the optimal conversion algorithm.

[0077] The conversion unit can improve the accuracy of conversion by referring to the user's past conversion results during conversion. For example, the conversion unit improves the accuracy of conversion by referring to the user's past conversion results during conversion. For example, the conversion unit improves the accuracy of similar conversions based on the user's past conversion results. The conversion unit can also analyze errors from the user's past conversion results and reflect them in the next conversion. The conversion unit can also learn the user's past conversion results and suggest the optimal conversion method. This makes it possible to improve the accuracy of conversion by referring to the user's past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the conversion unit can input the user's past conversion result data into the generation AI and have the generation AI improve the conversion accuracy.

[0078] The conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user's emotion. For example, the conversion unit can estimate the user's emotion and adjust the length of the conversion based on the estimated user's emotion. For example, if the user is in a hurry, the conversion unit can perform a short, to-the-point conversion. If the user is relaxed, the conversion unit can perform a longer conversion including detailed explanations. If the user is excited, the conversion unit can perform a conversion that adds a visually stimulating effect. This allows the length of the conversion to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit can be performed using, for example, the generation AI. For example, the conversion unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0079] The conversion unit can determine the priority of conversion based on the creation date of the 3D model during conversion. For example, the conversion unit determines the priority of conversion based on the creation date of the 3D model during conversion. For example, the conversion unit prioritizes conversion of the most recent 3D model. The conversion unit can also convert older 3D models later. The conversion unit can also gradually adjust the priority of conversion according to the creation date. This allows the priority of conversion to be determined based on the creation date of the 3D model. Some or all of the above-described processing in the conversion unit may be performed using, or without, the generation AI. For example, the conversion unit can input creation date data of the 3D model into the generation AI and have the generation AI determine the priority of conversion.

[0080] The conversion unit can adjust the order of conversion based on the relevance of the 3D models during conversion. For example, the conversion unit can prioritize converting 3D models related to the user's current project. The conversion unit can also convert less relevant 3D models later. The conversion unit can also gradually adjust the order of conversion according to the relevance. This allows the order of conversion to be adjusted based on the relevance of the 3D models. Some or all of the above-described processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input relevance data of the 3D models into the generation AI and cause the generation AI to adjust the order of conversion.

[0081] The conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise during conversion. For example, the conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise during conversion. For example, the conversion unit performs conversion that uses a lot of technical terminology for users with high levels of expertise. The conversion unit can also perform conversion that explains things in simpler terms for users with low levels of expertise. The conversion unit can also gradually adjust the use of technical terminology according to the user's level of expertise. This makes it possible to adjust the use of technical terminology in the conversion according to the user's level of expertise. Some or all of the above-mentioned processing in the conversion unit can be performed using, or without, a generation AI. For example, the conversion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0082] The output unit can estimate the user's emotion and adjust the output method based on the estimated user's emotion. For example, the output unit can estimate the user's emotion and adjust the output method based on the estimated user's emotion. For example, if the user is nervous, the output unit can provide a simple, highly visible output method. If the user is relaxed, the output unit can provide an output method including detailed information. If the user is in a hurry, the output unit can provide an output method that focuses on the main points. This allows the output method to be adjusted according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the output unit can be performed using an AI, for example, or without an AI. For example, the output unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0083] The output unit can analyze the user's past 3D printing history and select the optimal output method at the time of output. For example, the output unit can select the optimal output method at the time of output by analyzing the user's past 3D printing history. For example, the output unit can select the optimal output method based on the user's past successful 3D printing settings. The output unit can also analyze errors from the user's past 3D printing history and reflect them in the next output. The output unit can also learn the user's past 3D printing history and suggest the optimal output method. This allows the optimal output method to be selected based on the user's past 3D printing history. Some or all of the above-described processing in the output unit can be performed using, or without, AI. For example, the output unit can input the user's past 3D printing history data into AI and have the AI ​​select the optimal output method.

[0084] The output unit can customize the output means based on the user's current project at the time of output. For example, the output unit can provide optimal 3D printing settings for the user's current project. The output unit can also adjust the output means according to the progress of the user's project. The output unit can also suggest the optimal output means based on the type of the user's project. This allows the optimal output means to be provided based on the user's current project. Some or all of the above-described processing in the output unit can be performed using, or without, AI. For example, the output unit can input the user's project data into AI and have the AI ​​customize the output means.

[0085] The output unit can improve the output method by reflecting user feedback at the time of output. For example, the output unit can improve the output method by reflecting user feedback at the time of output. For example, the output unit can adjust output settings based on feedback provided by the user in the past. The output unit can also improve the output method by reflecting user feedback in real time. The output unit can also analyze user feedback and propose an optimal output method. This makes it possible to improve the output method based on user feedback. Some or all of the above-mentioned processing in the output unit can be performed using AI, for example, or can be performed without using AI. For example, the output unit can input user feedback data into AI and have the AI ​​improve the output method.

[0086] The output unit can estimate the user's emotions and determine output priorities based on the estimated user emotions. The output unit, for example, estimates the user's emotions and determines output priorities based on the estimated user emotions. For example, when the user is in a hurry, the output unit prioritizes important outputs. Furthermore, when the user is relaxed, the output unit can adjust the order of outputs to perform them efficiently. Furthermore, when the user is excited, the output unit can prioritize visually stimulating outputs. This allows output priorities to be determined according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the output unit can be performed using, for example, an AI, or without an AI. For example, the output unit can input the user's emotion data to the generation AI and cause the generation AI to estimate the emotion.

[0087] The output unit can select the optimal output method at the time of output by taking into account the user's geographical location information. For example, the output unit selects the optimal output method at the time of output by taking into account the user's geographical location information. For example, when the user is in a specific area, the output unit provides the optimal output method for that area. Furthermore, when the user is traveling, the output unit can also suggest an output method suitable for the travel destination. Furthermore, when the user is at home, the output unit can also provide the optimal output method for the home environment. In this way, the optimal output method can be selected based on the user's geographical location information. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's geographical location information to AI and have the AI ​​select the optimal output method.

[0088] The output unit can analyze the user's social media activity and suggest an output method at the time of output. For example, the output unit can analyze the user's social media activity and suggest an output method at the time of output. For example, the output unit can suggest an output method based on images and videos shared by the user on social media. The output unit can also analyze the content of the user's social media posts and suggest a related output method. The output unit can also suggest a related output method by referring to the activity of the user's friends on social media. This makes it possible to suggest the optimal output method based on the user's social media activity. Some or all of the above-mentioned processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's social media data into AI and have the AI ​​analyze the data.

[0089] The output unit can customize the output method by reflecting the user's past feedback at the time of output. The output unit, for example, customizes the output method by reflecting the user's past feedback at the time of output. For example, the output unit preferentially uses output methods that the user has previously rated highly. The output unit can also exclude output methods that the user has previously rated poorly. The output unit can also customize the optimal output method based on the user's past feedback. This makes it possible to customize the output method based on the user's past feedback. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without using AI. For example, the output unit can input the user's past feedback data into AI and have the AI ​​customize the output method. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, conversion unit, and output unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires 3D model data generated in the VR environment using the camera 42 and communication I / F 44 of the smart device 14. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the 3D model data using a generation AI, and converts it into a format that can be output by a 3D printer. The output unit is realized, for example, by the control unit 46A of the smart device 14, and sends the converted data to the 3D printer to perform 3D printing in the real world. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned acquisition unit, conversion unit, and output unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires 3D model data generated in the VR environment using the camera 42 and communication I / F 44 of the smart glasses 214. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the 3D model data using a generation AI and converts it into a format that can be output by a 3D printer. The output unit is realized, for example, by the control unit 46A of the smart glasses 214, and sends the converted data to the 3D printer to perform 3D printing in the real world. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, conversion unit, and output unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the acquisition unit acquires 3D model data generated in the VR environment using the camera 42 and communication I / F 44 of the headset-type terminal 314. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the 3D model data using a generation AI and converts it into a format that can be output by a 3D printer. The output unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and sends the converted data to the 3D printer, where 3D printing is performed in the real world. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, conversion unit, and output unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires 3D model data generated in the VR environment using the camera 42 and communication I / F 44 of the robot 414. The conversion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the 3D model data using a generation AI and converts it into a format that can be output by a 3D printer. The output unit is realized, for example, by the control unit 46A of the robot 414, and sends the converted data to the 3D printer, where it is 3D printed in the real world.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The acquisition unit acquires the user's biometric information and can adjust the timing of acquiring 3D model data based on the user's health condition. For example, the acquisition unit monitors the user's heart rate and blood pressure and acquires data when the user is relaxed. The acquisition unit can also temporarily delay data acquisition when the user is feeling stressed. Furthermore, the acquisition unit can analyze the user's sleep patterns and acquire data at the optimal timing. This allows the timing of acquiring 3D model data to be adjusted according to the user's health condition.

[0092] When converting 3D model data, the conversion unit can learn the user's past design style and perform conversion that suits the user's preferences. For example, the conversion unit analyzes the characteristics of designs created by the user in the past and performs conversion in a similar style. The conversion unit can also perform conversion that reflects the user's preferred colors and shapes. Furthermore, the conversion unit can learn the user's design patterns and suggest the optimal conversion method. This makes it possible to convert 3D model data according to the user's preferences.

[0093] The output unit can estimate the user's emotions during 3D printing and adjust the printing speed based on the estimated emotions. For example, the output unit can increase the printing speed if the user is in a hurry, or return the printing speed to normal if the user is relaxed. Furthermore, if the user is excited, the output unit can adjust the printing speed to provide optimal results. This allows the 3D printing speed to be adjusted according to the user's emotions.

[0094] The acquisition unit can analyze a user's social media activity and acquire 3D model data based on topics in which the user is interested. For example, the acquisition unit can analyze the content that the user frequently shares on social media and prioritize acquiring related 3D model data. The acquisition unit can also acquire data based on content shared by the user's followers and friends. Furthermore, the acquisition unit can learn the user's social media activity patterns and suggest the optimal data acquisition method. This makes it possible to acquire 3D model data based on the user's interests.

[0095] The conversion unit can estimate the user's emotions when converting 3D model data and adjust the accuracy of the conversion based on the estimated emotions. For example, the conversion unit can perform conversion with high accuracy when the user is concentrating. Alternatively, the conversion unit can perform conversion with normal accuracy when the user is relaxed. Furthermore, if the user is in a hurry, the conversion accuracy can be slightly reduced to provide quick results. This allows the conversion accuracy of 3D model data to be adjusted according to the user's emotions.

[0096] The output unit can analyze the user's past print history and automatically select the optimal print settings during 3D printing. For example, the output unit applies similar settings based on the user's past successful print settings. The output unit can also analyze past failures and suggest settings to avoid errors. Furthermore, the output unit can learn the user's print history and provide the optimal printing method. This allows the optimal print settings to be selected based on the user's past print history.

[0097] The acquisition unit can estimate the user's emotions and customize the method for acquiring 3D model data based on the estimated emotions. For example, the acquisition unit can acquire data quickly if the user is excited. Alternatively, the acquisition unit can acquire data slowly if the user is relaxed. Furthermore, the acquisition unit can acquire data at an optimal timing if the user is concentrating. This allows the method for acquiring 3D model data to be customized according to the user's emotions.

[0098] When converting 3D model data, the conversion unit can select the optimal conversion method by taking into account the user's geographical location information. For example, if the user is in a specific area, the conversion unit can apply a conversion method suitable for that area. Also, if the user is traveling, the conversion unit can suggest a conversion method suitable for the user's destination. Furthermore, if the user is at home, the conversion unit can provide a conversion method that is optimal for the user's home environment. This allows the optimal conversion method to be selected based on the user's geographical location information.

[0099] The output unit can estimate the user's emotions during 3D printing and adjust the order of printing based on the estimated emotions. For example, if the user is in a hurry, the output unit can prioritize important printing. If the user is relaxed, the output unit can print in the normal order. Furthermore, if the user is excited, the output unit can prioritize visually stimulating printing. This allows the order of 3D printing to be adjusted according to the user's emotions.

[0100] The acquisition unit can improve the 3D model data acquisition method by reflecting the user's past feedback. For example, the acquisition unit can prioritize the use of data acquisition methods that the user has previously rated highly. It can also acquire data by excluding methods that the user has rated poorly. Furthermore, the acquisition unit can analyze the user's feedback and suggest the optimal data acquisition method. This allows the 3D model data acquisition method to be improved based on the user's past feedback.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The acquisition unit acquires 3D model data generated within the VR environment. For example, it collects data on objects created by the user within the VR environment, or 3D model data on characters and items created within a VR game. Step 2: The conversion unit uses the generation AI to analyze the 3D model data acquired by the acquisition unit and convert it into a format that can be output by a 3D printer. For example, it converts an object with a complex shape created in the VR world into a shape that can be output by a 3D printer. Step 3: The output unit transmits the data converted by the conversion unit to a 3D printer and performs 3D printing in the real world. For example, the converted data is sent to a 3D printer to actually print the object.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires 3D model data generated within the VR environment; a conversion unit that analyzes the 3D model data acquired by the acquisition unit and converts it into a format that can be output by a 3D printer; an output unit that transmits the data converted by the conversion unit to a 3D printer and performs 3D printing in the real world. A system characterized by:

2. The acquisition unit Collect data on objects created by users in the VR environment 2. The system of claim 1.

3. The conversion unit Analyzes 3D model data and converts it into a format that can be output by a 3D printer 2. The system of claim 1.

4. The output unit The converted data is sent to a 3D printer and 3D printed in the real world.

2. The system of claim 1.

5. The conversion unit Converting complex objects created in the VR world into shapes that can be output by a 3D printer 2. The system of claim 1.

6. The acquisition unit Estimate the user's emotions and adjust the timing of 3D model data acquisition based on the estimated user emotions.

2. The system of claim 1.

7. The acquisition unit Analyze the user's past activity history in the VR environment and select the optimal acquisition method 2. The system of claim 1.

8. The acquisition unit Filter 3D model data acquisition based on your current project or area of ​​interest 2. The system of claim 1.

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