system

Abstract

The system according to this embodiment aims to enable users to create design drawings and 3D models without specialized knowledge, and to integrate with a 3D printer. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, and a cooperation unit. The reception unit receives instructions from the user. The generation unit creates design drawings and 3D models based on the instructions received by the reception unit. The provision unit provides the information generated by the generation unit. The cooperation unit cooperates with a 3D printer based on the information provided by the provision unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process in which a user creates a design drawing or 3D modeling and cooperates with a 3D printer is complicated and difficult to execute without expertise.

[0005] The system according to the embodiment aims to enable a user to create a design drawing or 3D modeling and cooperate with a 3D printer without expertise.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a cooperation unit. The reception unit receives instructions from the user. The generation unit creates design drawings and 3D models based on the instructions received by the reception unit. The provision unit provides the information generated by the generation unit. The cooperation unit cooperates with a 3D printer based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to create design drawings and 3D models and integrate with a 3D printer without requiring specialized knowledge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The hobby-focused AI agent system according to an embodiment of the present invention is a system designed to enrich users' leisure time. This system provides an AI agent that supports DIY furniture projects and creative activities using 3D printers. For example, when a user undertakes a DIY furniture project, they instruct the AI ​​agent to "make a bookshelf." The AI ​​agent creates a design drawing based on the size of the lumber the user purchases and provides a list of necessary screws and tools. This allows the user to easily assemble the furniture according to the design drawing. It is similar to the instruction manual for IKEA's assembly furniture. Next, when a user engages in creative activities using a 3D printer, they instruct the AI ​​agent to "want a fighter jet object." The AI ​​agent performs 3D modeling based on the user's image and outputs the object in cooperation with the 3D printer. This allows the user to easily output what they want with a 3D printer without needing to master modeling software. Through this mechanism, a hobby-focused AI agent can support users' DIY furniture projects and creative activities using 3D printers, enriching their leisure time. Users can achieve professional-level results simply by consulting with the AI ​​agent. This allows hobby-focused AI agent systems to enrich users' leisure time and support their creative activities.

[0029] The hobby-specific AI agent system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a cooperation unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can receive voice instructions from the user using, for example, voice recognition technology. The reception unit can also receive text instructions from the user using a text input interface. Furthermore, the reception unit can also receive gesture instructions from the user using gesture recognition technology. The generation unit performs design drawings and 3D models based on the instructions received by the reception unit. The generation unit generates design drawings and 3D models in, for example, CAD data or STL files. For example, if the user instructs "I want to build a bookshelf," the generation unit creates a design drawing based on the size of the lumber the user will purchase. For example, if the user instructs "I want a fighter jet object," the generation unit performs 3D modeling based on the user's image. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit inputs user instructions as prompts to the generation AI, which then generates blueprints and 3D models. The supply unit provides the information generated by the generation unit. The supply unit provides information in the form of, for example, a preview of the blueprint or a display of the 3D model. The supply unit provides the user with a list of necessary screws and tools. The supply unit displays a preview of the blueprint so that the user can review it. The supply unit also displays the 3D model so that the user can review it. Some or all of the above processing in the supply unit may be performed using the generation AI, or without using the generation AI. For example, the supply unit inputs the generated blueprints and 3D models as prompts to the generation AI, which then provides the information. The collaboration unit collaborates with the 3D printer based on the information provided by the supply unit. The collaboration unit collaborates with the 3D printer in the form of, for example, a method for sending data or a method for managing print jobs.The collaboration unit, for example, sends the generated 3D model to the 3D printer and instructs the 3D printer to output the object. The collaboration unit also manages print jobs and monitors the operating status of the 3D printer. Some or all of the above processes in the collaboration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collaboration unit prompts the generative AI for a method of collaboration with the 3D printer, and the generative AI adjusts the collaboration method. In this way, the hobby-specific AI agent system according to the embodiment can support the user's creative activities by creating design drawings and 3D models based on user instructions, providing information, and collaborating with the 3D printer.

[0030] The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can receive voice instructions from the user using, for example, speech recognition technology. Specifically, a deep learning-based speech recognition model is used as the speech recognition technology. This model converts the user's voice into text in a format that the system can understand. The reception unit can also receive text instructions from the user using a text input interface. The text input interface allows the user to input instructions using a keyboard or touchscreen, and the entered text is immediately sent to the system. Furthermore, the reception unit can also receive gesture instructions from the user using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the user's hand and body movements and interprets them as instructions. For example, a user can send a specific command to the system by waving their hand. This allows the reception unit to support a variety of input methods, enabling users to give instructions in the way that is most comfortable for them. Furthermore, the reception unit can use these input methods in combination; for example, voice instructions and gesture instructions can be combined to give more complex instructions. This allows the reception desk to respond to diverse user needs and provide flexible and intuitive operation.

[0031] The generation unit creates design drawings and 3D models based on instructions received by the reception unit. The generation unit generates design drawings and 3D models in formats such as CAD data and STL files. Specifically, the generation unit analyzes the user's instructions and identifies the necessary design elements. For example, if the user instructs "I want to make a bookshelf," the generation unit creates an appropriate design drawing based on the size and shape of the lumber specified by the user. When creating the design drawing, the generation unit considers strength and stability and designs the optimal structure. Also, if the user instructs "I want a fighter jet object," the generation unit creates a 3D model based on the user's image. To materialize the user's image, the generation unit adds detailed design elements and creates a realistic 3D model. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit inputs the user's instructions as prompts to the generation AI, and the generation AI generates design drawings and 3D models. The generation AI understands the user's instructions using natural language processing technology and generates an appropriate design. The generation AI can learn from past design data and patterns, and propose the optimal design for the user's requirements. This allows the generation unit to quickly and accurately generate blueprints and 3D models based on user instructions, supporting the user's creative activities.

[0032] The providing unit provides information generated by the generating unit. The providing unit provides information in the form of, for example, a preview of a design drawing or a display of a 3D model. Specifically, the providing unit has a preview function that allows the user to visually confirm the generated design drawing. The user can check the details of the design drawing and request corrections as needed. The providing unit also has a 3D model display function, allowing the user to check the generated 3D model from various angles. The providing unit provides, for example, a list of screws and tools that the user needs. This allows the user to prepare the materials and tools necessary for the project in advance. Some or all of the above processing in the providing unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the providing unit inputs the generated design drawing or 3D model as a prompt to the generating AI, and the generating AI provides the information. The generating AI can refer to past data and patterns in order to provide the optimal information according to the user's request. This allows the providing unit to provide the user with quick and accurate information and support the progress of the project. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and usefulness of the information it provides. This allows the service provider to deliver high-quality information that meets user needs and improve user satisfaction.

[0033] The integration unit interacts with the 3D printer based on information provided by the supply unit. The integration unit interacts with the 3D printer in various ways, such as determining how to send data and how to manage print jobs. Specifically, the integration unit sends the generated 3D model to the 3D printer and instructs the 3D printer to output the object. The integration unit manages print jobs and monitors the 3D printer's operating status. This allows the integration unit to support the efficient operation of the 3D printer and enable users to create objects smoothly. Some or all of the above-described processes in the integration unit may be performed using, for example, a generative AI, or without one. For example, the integration unit prompts the generative AI for the method of interacting with the 3D printer, and the generative AI adjusts the method. The generative AI can propose the optimal method of interaction according to the characteristics of the 3D printer and the user's requirements. This allows the integration unit to optimize interaction with the 3D printer and efficiently support the user's creative activities. Furthermore, the integration unit can collect 3D printer maintenance information and error logs and perform necessary maintenance and troubleshooting. This allows the integrated unit to improve the reliability and uptime of 3D printers, supporting the success of users' projects.

[0034] The generation unit can create a design drawing based on the size of the lumber purchased by the user. For example, the generation unit receives dimensions such as length, width, and height of the lumber purchased by the user as input and creates a design drawing based on that. For example, the generation unit can apply an algorithm to generate an optimal design drawing considering the size of the lumber. For example, the generation unit can also simultaneously generate a list of necessary screws and tools based on the size of the lumber. This allows the generation unit to provide an appropriate design drawing based on the size of the lumber purchased by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit inputs the size of the lumber as a prompt to the generation AI, and the generation AI generates a design drawing. The size of the lumber includes, but is not limited to, dimensions such as length, width, and height.

[0035] The generation unit can perform 3D modeling based on the user's image. For example, the generation unit can receive user-provided sketches, reference images, or verbal descriptions as input and perform 3D modeling based on them. The generation unit can apply algorithms to generate the optimal 3D model, taking the user's image into consideration. The generation unit can also simultaneously generate the necessary materials and print settings based on the user's image. This allows for the generation of objects that meet the user's expectations based on their image. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's image as a prompt to the generation AI, which then generates the 3D model. The user's image includes, but is not limited to, sketches, reference images, or verbal descriptions.

[0036] The providing unit can provide a list of necessary screws and tools. The providing unit generates a list of necessary screws and tools based, for example, on blueprints or 3D models generated by the generating unit. The providing unit can provide a list that includes information such as screw size and type, and tool type and application. The providing unit can also display a list of necessary screws and tools simultaneously when a user is viewing blueprints or 3D models. This makes it easy for the user to gather the necessary materials. Some or all of the above processing in the providing unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the providing unit inputs blueprints or 3D models as prompts to the generating AI, and the generating AI generates a list of necessary screws and tools. The list of necessary screws and tools may include, for example, screw size and tool type, but is not limited to such examples.

[0037] The integration unit can work with a 3D printer to output objects. For example, the integration unit can send a 3D model generated by the generation unit to the 3D printer and instruct the 3D printer to output the object. The integration unit can manage print jobs and monitor the operating status of the 3D printer. The integration unit can set settings such as print accuracy and material type and make adjustments to obtain optimal print results. This allows users to easily use the 3D printer. Some or all of the above processes in the integration unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the integration unit inputs a 3D model as a prompt to the generation AI, and the generation AI adjusts how to interact with the 3D printer. The output of an object includes, but is not limited to, print accuracy and material type.

[0038] The hobby-specific AI agent system according to the embodiment further comprises an update unit. The update unit can update and maintain the database. For example, the update unit can periodically update the system information to keep it up-to-date. For example, the update unit can manage the database update frequency and maintenance procedures. For example, the update unit can rebuild the database index to optimize the system performance. This ensures that the system information is always up-to-date. Some or all of the above-described processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit inputs the database update procedure as a prompt to the generative AI, and the generative AI optimizes the update procedure. Database updates and maintenance include, but are not limited to, the update frequency and maintenance procedures.

[0039] The hobby-specific AI agent system according to this embodiment further comprises an interface unit. The interface unit can provide an interface that is easy for the user to operate. The interface unit, for example, conducts usability tests and designs intuitive operating methods. The interface unit, for example, provides button layouts and menu configurations that are easy for the user to operate. The interface unit, for example, provides visually easy-to-understand icons and graphical user interfaces (GUIs). This makes the system easy to use. Some or all of the above-described processes in the interface unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the interface unit inputs the results of a usability test as a prompt to the generative AI, and the generative AI designs the optimal interface. An easy-to-operate interface includes, but is not limited to, examples such as usability tests and intuitive operating methods.

[0040] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit may prioritize suggesting instruction methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit may automatically suggest relevant instructions based on the user's past project history. For example, the reception unit may suggest the optimal reception method for a specific time period based on the user's past instruction history. This allows the reception unit to provide the optimal reception method. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit may input the user's past instruction history as a prompt to the generative AI, and the generative AI may select the optimal reception method. The optimal reception method may include, but is not limited to, methods for analyzing past instruction history and optimization algorithms.

[0041] The reception unit can filter instructions based on the user's current projects and areas of interest when receiving them. For example, the reception unit may prioritize receiving instructions related to the user's current projects. For example, the reception unit may filter and suggest relevant instructions based on the user's areas of interest. For example, the reception unit may prioritize receiving relevant instructions based on projects the user has shown interest in in the past. This ensures that highly relevant instructions are prioritized. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit may input the user's current projects and areas of interest as prompts to the generative AI, which then performs the filtering. Filtering may include, but is not limited to, the type of project or how to identify areas of interest.

[0042] The reception unit can prioritize receiving instructions based on the user's geographical location information. For example, if the user is at home, the reception unit will prioritize instructions related to projects that can be done at home. If the user is out, the reception unit will prioritize instructions related to projects that can be done while out. If the user is in a specific location, the reception unit will prioritize instructions related to that location. This enables responses tailored to the user's situation. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit inputs the user's geographical location information as a prompt to the generative AI, which then filters out the most relevant instructions. Geographical location information includes, but is not limited to, GPS data and location services.

[0043] The reception unit can analyze the user's social media activity when receiving instructions and receive relevant instructions. For example, the reception unit can prioritize instructions related to projects the user has shared on social media. For example, the reception unit can prioritize instructions based on topics the user has shown interest in on social media. For example, the reception unit can prioritize instructions related to projects that the user's social media followers are interested in. This allows for the priority of receiving relevant instructions. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit inputs the user's social media activity as a prompt to the generative AI, and the generative AI filters out relevant instructions. Social media activity includes, but is not limited to, analyzing post content and evaluating activity frequency.

[0044] The generation unit can adjust the level of detail in the design based on the quality and characteristics of the timber when creating the design drawing. For example, when using high-quality timber, the generation unit provides a detailed design drawing. For example, when using low-quality timber, the generation unit provides a simplified design drawing. For example, when using timber with specific characteristics, the generation unit provides a design drawing that corresponds to those characteristics. This allows for the provision of an appropriate design drawing. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the quality and characteristics of the timber as prompts to the generation AI, and the generation AI adjusts the level of detail in the design. The quality and characteristics of the timber include, but are not limited to, strength, durability, and grain pattern.

[0045] The generation unit can apply different modeling algorithms during 3D modeling depending on the user's image. For example, if the user has a specific image, the generation unit applies a modeling algorithm based on that image. For example, if the user has an abstract image, the generation unit applies an abstract modeling algorithm. For example, if the user has multiple images, the generation unit applies a modeling algorithm that combines them. This makes it possible to create 3D models that meet the user's wishes. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's image as a prompt to the generation AI, and the generation AI applies the optimal modeling algorithm. Different modeling algorithms include, but are not limited to, polygon modeling and sculpting.

[0046] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, when providing highly important information, the provider will provide information that includes detailed explanations. For example, when providing less important information, the provider will provide concise information. The provider may adjust the level of detail of the information in stages according to its importance. This enables the provision of appropriate information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider may input the importance of the information as a prompt to the generative AI, and the generative AI will adjust the level of detail of the information. The importance of the information includes, but is not limited to, the user's level of interest or the urgency of the information.

[0047] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, when providing technical information, the provider may apply a specialized algorithm. For example, when providing general information, the provider may apply a concise algorithm. For example, the provider may select the optimal information provision algorithm depending on a specific category. This enables the provision of appropriate information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider may input the category of information as a prompt to the generative AI, and the generative AI may apply the optimal information provision algorithm. Categories of information include, but are not limited to, technical information and business information.

[0048] The integration unit can adjust the level of detail of the integration based on the performance and characteristics of the 3D printer during integration. For example, when using a high-performance 3D printer, the integration unit provides a detailed integration method. For example, when using a low-performance 3D printer, the integration unit provides a simplified integration method. For example, when using a 3D printer with specific characteristics, the integration unit provides an integration method tailored to those characteristics. This enables appropriate integration. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit inputs the performance and characteristics of the 3D printer as prompts to the generating AI, and the generating AI adjusts the level of detail of the integration. The performance and characteristics of the 3D printer include, but are not limited to, print resolution and material type.

[0049] The integration unit can apply different integration algorithms depending on the progress of the user's project during integration. For example, the integration unit applies a basic integration algorithm in the early stages of the project. For example, the integration unit applies a detailed integration algorithm in the middle stages of the project. For example, the integration unit applies an optimized integration algorithm in the final stages of the project. This enables integration that is tailored to the progress of the project. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs the project progress as a prompt to the generative AI, and the generative AI applies the optimal integration algorithm. Integration algorithms include, but are not limited to, project management algorithms and scheduling algorithms.

[0050] The integration unit can adjust the integration order based on the installation location of the 3D printers during integration. For example, the integration unit prioritizes integration with 3D printers installed nearby. For example, the integration unit postpones integration with 3D printers installed far away. For example, the integration unit adjusts the integration order in stages according to the installation location. This enables efficient integration. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit inputs the installation location of the 3D printer as a prompt to the generating AI, and the generating AI adjusts the integration order. The installation location of the 3D printer includes, but is not limited to, the coordinates of the installation location or a layout diagram.

[0051] The integration unit can adjust the integration procedure based on the user's related projects during integration. For example, the integration unit may prioritize integrations related to projects currently underway. For example, the integration unit may postpone integrations related to past projects. For example, the integration unit may systematically perform integrations related to projects planned for the future. This enables integration tailored to each project. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit may input the user's related projects as prompts to the generative AI, and the generative AI will adjust the integration procedure. Related projects include, but are not limited to, the content and progress of projects.

[0052] The update unit can optimize the update algorithm by referring to past update history during updates. For example, the update unit analyzes past update history to determine the optimal update timing. For example, the update unit adjusts the update frequency based on the update history. For example, the update unit determines update priorities by referring to the update history. This enables efficient updates. Some or all of the above processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit inputs past update history as a prompt to the generative AI, and the generative AI optimizes the update algorithm. The update algorithm includes, but is not limited to, data integrity checks and efficient data update methods.

[0053] The update unit can weight the data to be updated based on its importance during the update process. For example, the update unit may prioritize updating data with high importance. For example, the update unit may postpone updating data with low importance. For example, the update unit may adjust the update order in stages according to the importance of the data. This allows important data to be updated preferentially. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit may input the importance of the data as a prompt to the generative AI, and the generative AI will weight the data to be updated. The weighting of the data to be updated may include, but is not limited to, data importance and urgency of update.

[0054] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit may prioritize providing display methods previously used by the user. For example, the interface unit may propose the optimal display method based on the user's operation history. For example, the interface unit may analyze the user's operation history and provide the most efficient display method. This enables efficient display. Some or all of the above processing in the interface unit may be performed using, for example, a generating AI, or without a generating AI. For example, the interface unit inputs the user's past operation history as a prompt to the generating AI, and the generating AI selects the optimal display method. The optimal display method includes, but is not limited to, a method for analyzing the user's operation history and criteria for evaluating the display method.

[0055] The interface unit can select the optimal display method based on the user's device information when displaying the interface. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the interface unit provides a concise and highly visible display method. This enables efficient display. Some or all of the above processing in the interface unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interface unit inputs the user's device information as a prompt to the generative AI, and the generative AI selects the optimal display method. Device information includes, but is not limited to, the type of device, performance, and usage status.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The reception system can suggest the most suitable method of receiving instructions from a user by referring to the user's past instruction history. For example, it can prioritize suggesting instruction methods that the user has frequently used in the past (voice, text, etc.). It can also automatically suggest relevant instructions based on the user's past project history. Furthermore, it can suggest the most suitable method of receiving instructions for a specific time period based on the user's past instruction history. This allows users to select the most suitable method of receiving instructions and give instructions efficiently.

[0058] The generation unit can adjust the level of detail in the design based on the quality and characteristics of the lumber when creating a design drawing based on the size of the lumber purchased by the user. For example, when using high-quality lumber, a detailed design drawing can be provided. Conversely, when using low-quality lumber, a simplified design drawing can be provided. Furthermore, when using lumber with specific characteristics, a design drawing tailored to those characteristics can be provided. This allows the user to obtain an appropriate design drawing according to the quality and characteristics of the lumber.

[0059] The reception desk can prioritize receiving user instructions based on the user's geographical location. For example, if a user is at home, it can prioritize instructions related to projects that can be done at home. If a user is out, it can prioritize instructions related to projects that can be done while out. Furthermore, if a user is in a specific location, it can prioritize instructions related to that location. This allows for responses tailored to the user's situation.

[0060] The generation unit can apply different modeling algorithms when creating 3D models based on the user's image. For example, if the user has a specific image, a modeling algorithm based on that image can be applied. If the user has an abstract image, an abstract modeling algorithm can be applied. Furthermore, if the user has multiple images, a modeling algorithm that combines them can be applied. This makes it possible to create 3D models that meet the user's wishes.

[0061] The information provider can adjust the level of detail provided based on the importance of the information being offered. For example, when providing highly important information, it can include detailed explanations. Conversely, when providing less important information, it can provide concise information. Furthermore, the level of detail can be adjusted in stages according to importance. This enables the provision of appropriate information.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception unit receives user instructions. User instructions include voice instructions, text instructions, and gesture instructions. For example, voice instructions can be received using speech recognition technology, text instructions can be received using a text input interface, and gesture instructions can be received using gesture recognition technology. Step 2: The generation unit creates blueprints and 3D models based on the instructions received by the reception unit. For example, it generates blueprints and 3D models in formats such as CAD data and STL files. If the user instructs "I want to make a bookshelf," it creates blueprints based on the size of the lumber the user will purchase. If the user instructs "I want a fighter jet object," it creates a 3D model based on the user's image. Processing in the generation unit may also be performed using generation AI. Step 3: The providing unit provides the information generated by the generating unit. For example, it provides information in the form of a preview of a design drawing or a display of a 3D model. This could include providing the user with a list of necessary screws and tools, displaying a preview of a design drawing so the user can review it, or displaying a 3D model so the user can review it. Processing in the providing unit may also be performed using a generating AI. Step 4: The integration unit interacts with the 3D printer based on the information provided by the supply unit. For example, it interacts with the 3D printer in the form of data transmission methods and print job management methods. It sends the generated 3D model to the 3D printer, instructs the 3D printer to output the object, manages print jobs, and monitors the 3D printer's operating status. Processing in the integration unit may also be performed using generation AI.

[0064] (Example of form 2) The hobby-focused AI agent system according to an embodiment of the present invention is a system designed to enrich users' leisure time. This system provides an AI agent that supports DIY furniture projects and creative activities using 3D printers. For example, when a user undertakes a DIY furniture project, they instruct the AI ​​agent to "make a bookshelf." The AI ​​agent creates a design drawing based on the size of the lumber the user purchases and provides a list of necessary screws and tools. This allows the user to easily assemble the furniture according to the design drawing. It is similar to the instruction manual for IKEA's assembly furniture. Next, when a user engages in creative activities using a 3D printer, they instruct the AI ​​agent to "want a fighter jet object." The AI ​​agent performs 3D modeling based on the user's image and outputs the object in cooperation with the 3D printer. This allows the user to easily output what they want with a 3D printer without needing to master modeling software. Through this mechanism, a hobby-focused AI agent can support users' DIY furniture projects and creative activities using 3D printers, enriching their leisure time. Users can achieve professional-level results simply by consulting with the AI ​​agent. This allows hobby-focused AI agent systems to enrich users' leisure time and support their creative activities.

[0065] The hobby-specific AI agent system according to this embodiment comprises a reception unit, a generation unit, a provision unit, and a cooperation unit. The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can receive voice instructions from the user using, for example, voice recognition technology. The reception unit can also receive text instructions from the user using a text input interface. Furthermore, the reception unit can also receive gesture instructions from the user using gesture recognition technology. The generation unit performs design drawings and 3D models based on the instructions received by the reception unit. The generation unit generates design drawings and 3D models in, for example, CAD data or STL files. For example, if the user instructs "I want to build a bookshelf," the generation unit creates a design drawing based on the size of the lumber the user will purchase. For example, if the user instructs "I want a fighter jet object," the generation unit performs 3D modeling based on the user's image. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit inputs user instructions as prompts to the generation AI, which then generates blueprints and 3D models. The supply unit provides the information generated by the generation unit. The supply unit provides information in the form of, for example, a preview of the blueprint or a display of the 3D model. The supply unit provides the user with a list of necessary screws and tools. The supply unit displays a preview of the blueprint so that the user can review it. The supply unit also displays the 3D model so that the user can review it. Some or all of the above processing in the supply unit may be performed using the generation AI, or without using the generation AI. For example, the supply unit inputs the generated blueprints and 3D models as prompts to the generation AI, which then provides the information. The collaboration unit collaborates with the 3D printer based on the information provided by the supply unit. The collaboration unit collaborates with the 3D printer in the form of, for example, a method for sending data or a method for managing print jobs.The collaboration unit, for example, sends the generated 3D model to the 3D printer and instructs the 3D printer to output the object. The collaboration unit also manages print jobs and monitors the operating status of the 3D printer. Some or all of the above processes in the collaboration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the collaboration unit prompts the generative AI for a method of collaboration with the 3D printer, and the generative AI adjusts the collaboration method. In this way, the hobby-specific AI agent system according to the embodiment can support the user's creative activities by creating design drawings and 3D models based on user instructions, providing information, and collaborating with the 3D printer.

[0066] The reception unit receives user instructions. User instructions include, but are not limited to, voice instructions, text instructions, and gesture instructions. The reception unit can receive voice instructions from the user using, for example, speech recognition technology. Specifically, a deep learning-based speech recognition model is used as the speech recognition technology. This model converts the user's voice into text in a format that the system can understand. The reception unit can also receive text instructions from the user using a text input interface. The text input interface allows the user to input instructions using a keyboard or touchscreen, and the entered text is immediately sent to the system. Furthermore, the reception unit can also receive gesture instructions from the user using gesture recognition technology. Gesture recognition technology uses cameras and sensors to detect the user's hand and body movements and interprets them as instructions. For example, a user can send a specific command to the system by waving their hand. This allows the reception unit to support a variety of input methods, enabling users to give instructions in the way that is most comfortable for them. Furthermore, the reception unit can use these input methods in combination; for example, voice instructions and gesture instructions can be combined to give more complex instructions. This allows the reception desk to respond to diverse user needs and provide flexible and intuitive operation.

[0067] The generation unit creates design drawings and 3D models based on instructions received by the reception unit. The generation unit generates design drawings and 3D models in formats such as CAD data and STL files. Specifically, the generation unit analyzes the user's instructions and identifies the necessary design elements. For example, if the user instructs "I want to make a bookshelf," the generation unit creates an appropriate design drawing based on the size and shape of the lumber specified by the user. When creating the design drawing, the generation unit considers strength and stability and designs the optimal structure. Also, if the user instructs "I want a fighter jet object," the generation unit creates a 3D model based on the user's image. To materialize the user's image, the generation unit adds detailed design elements and creates a realistic 3D model. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit inputs the user's instructions as prompts to the generation AI, and the generation AI generates design drawings and 3D models. The generation AI understands the user's instructions using natural language processing technology and generates an appropriate design. The generation AI can learn from past design data and patterns, and propose the optimal design for the user's requirements. This allows the generation unit to quickly and accurately generate blueprints and 3D models based on user instructions, supporting the user's creative activities.

[0068] The providing unit provides information generated by the generating unit. The providing unit provides information in the form of, for example, a preview of a design drawing or a display of a 3D model. Specifically, the providing unit has a preview function that allows the user to visually confirm the generated design drawing. The user can check the details of the design drawing and request corrections as needed. The providing unit also has a 3D model display function, allowing the user to check the generated 3D model from various angles. The providing unit provides, for example, a list of screws and tools that the user needs. This allows the user to prepare the materials and tools necessary for the project in advance. Some or all of the above processing in the providing unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the providing unit inputs the generated design drawing or 3D model as a prompt to the generating AI, and the generating AI provides the information. The generating AI can refer to past data and patterns in order to provide the optimal information according to the user's request. This allows the providing unit to provide the user with quick and accurate information and support the progress of the project. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and usefulness of the information it provides. This allows the service provider to deliver high-quality information that meets user needs and improve user satisfaction.

[0069] The integration unit interacts with the 3D printer based on information provided by the supply unit. The integration unit interacts with the 3D printer in various ways, such as determining how to send data and how to manage print jobs. Specifically, the integration unit sends the generated 3D model to the 3D printer and instructs the 3D printer to output the object. The integration unit manages print jobs and monitors the 3D printer's operating status. This allows the integration unit to support the efficient operation of the 3D printer and enable users to create objects smoothly. Some or all of the above-described processes in the integration unit may be performed using, for example, a generative AI, or without one. For example, the integration unit prompts the generative AI for the method of interacting with the 3D printer, and the generative AI adjusts the method. The generative AI can propose the optimal method of interaction according to the characteristics of the 3D printer and the user's requirements. This allows the integration unit to optimize interaction with the 3D printer and efficiently support the user's creative activities. Furthermore, the integration unit can collect 3D printer maintenance information and error logs and perform necessary maintenance and troubleshooting. This allows the integrated unit to improve the reliability and uptime of 3D printers, supporting the success of users' projects.

[0070] The generation unit can create a design drawing based on the size of the lumber purchased by the user. For example, the generation unit receives dimensions such as length, width, and height of the lumber purchased by the user as input and creates a design drawing based on that. For example, the generation unit can apply an algorithm to generate an optimal design drawing considering the size of the lumber. For example, the generation unit can also simultaneously generate a list of necessary screws and tools based on the size of the lumber. This allows the generation unit to provide an appropriate design drawing based on the size of the lumber purchased by the user. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit inputs the size of the lumber as a prompt to the generation AI, and the generation AI generates a design drawing. The size of the lumber includes, but is not limited to, dimensions such as length, width, and height.

[0071] The generation unit can perform 3D modeling based on the user's image. For example, the generation unit can receive user-provided sketches, reference images, or verbal descriptions as input and perform 3D modeling based on them. The generation unit can apply algorithms to generate the optimal 3D model, taking the user's image into consideration. The generation unit can also simultaneously generate the necessary materials and print settings based on the user's image. This allows for the generation of objects that meet the user's expectations based on their image. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's image as a prompt to the generation AI, which then generates the 3D model. The user's image includes, but is not limited to, sketches, reference images, or verbal descriptions.

[0072] The providing unit can provide a list of necessary screws and tools. The providing unit generates a list of necessary screws and tools based, for example, on blueprints or 3D models generated by the generating unit. The providing unit can provide a list that includes information such as screw size and type, and tool type and application. The providing unit can also display a list of necessary screws and tools simultaneously when a user is viewing blueprints or 3D models. This makes it easy for the user to gather the necessary materials. Some or all of the above processing in the providing unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the providing unit inputs blueprints or 3D models as prompts to the generating AI, and the generating AI generates a list of necessary screws and tools. The list of necessary screws and tools may include, for example, screw size and tool type, but is not limited to such examples.

[0073] The integration unit can work with a 3D printer to output objects. For example, the integration unit can send a 3D model generated by the generation unit to the 3D printer and instruct the 3D printer to output the object. The integration unit can manage print jobs and monitor the operating status of the 3D printer. The integration unit can set settings such as print accuracy and material type and make adjustments to obtain optimal print results. This allows users to easily use the 3D printer. Some or all of the above processes in the integration unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the integration unit inputs a 3D model as a prompt to the generation AI, and the generation AI adjusts how to interact with the 3D printer. The output of an object includes, but is not limited to, print accuracy and material type.

[0074] The hobby-specific AI agent system according to the embodiment further comprises an update unit. The update unit can update and maintain the database. For example, the update unit can periodically update the system information to keep it up-to-date. For example, the update unit can manage the database update frequency and maintenance procedures. For example, the update unit can rebuild the database index to optimize the system performance. This ensures that the system information is always up-to-date. Some or all of the above-described processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit inputs the database update procedure as a prompt to the generative AI, and the generative AI optimizes the update procedure. Database updates and maintenance include, but are not limited to, the update frequency and maintenance procedures.

[0075] The hobby-specific AI agent system according to this embodiment further comprises an interface unit. The interface unit can provide an interface that is easy for the user to operate. The interface unit, for example, conducts usability tests and designs intuitive operating methods. The interface unit, for example, provides button layouts and menu configurations that are easy for the user to operate. The interface unit, for example, provides visually easy-to-understand icons and graphical user interfaces (GUIs). This makes the system easy to use. Some or all of the above-described processes in the interface unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the interface unit inputs the results of a usability test as a prompt to the generative AI, and the generative AI designs the optimal interface. An easy-to-operate interface includes, but is not limited to, examples such as usability tests and intuitive operating methods.

[0076] The reception unit can estimate the user's emotions and adjust the timing of instruction acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit may temporarily delay instruction acceptance to provide time to relax. For example, if the user is excited, the reception unit may quickly accept instructions to allow the project to start immediately. For example, if the user is tired, the reception unit may delay instruction acceptance and display a message encouraging rest. This allows for responses tailored to the user's state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit inputs user emotion data as a prompt to the generative AI, and the generative AI adjusts the timing of instruction acceptance. Specific methods for estimating user emotions include, but are not limited to, facial recognition and voice analysis.

[0077] The reception unit can analyze the user's past instruction history and select the optimal reception method. For example, the reception unit may prioritize suggesting instruction methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception unit may automatically suggest relevant instructions based on the user's past project history. For example, the reception unit may suggest the optimal reception method for a specific time period based on the user's past instruction history. This allows the reception unit to provide the optimal reception method. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit may input the user's past instruction history as a prompt to the generative AI, and the generative AI may select the optimal reception method. The optimal reception method may include, but is not limited to, methods for analyzing past instruction history and optimization algorithms.

[0078] The reception unit can filter instructions based on the user's current projects and areas of interest when receiving them. For example, the reception unit may prioritize receiving instructions related to the user's current projects. For example, the reception unit may filter and suggest relevant instructions based on the user's areas of interest. For example, the reception unit may prioritize receiving relevant instructions based on projects the user has shown interest in in the past. This ensures that highly relevant instructions are prioritized. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit may input the user's current projects and areas of interest as prompts to the generative AI, which then performs the filtering. Filtering may include, but is not limited to, the type of project or how to identify areas of interest.

[0079] The reception unit can estimate the user's emotions and determine the priority of instructions to accept based on the estimated emotions. For example, if the user is stressed, the reception unit will postpone less important instructions and prioritize more important ones. If the user is relaxed, the reception unit will accept all instructions equally. If the user is in a hurry, the reception unit will prioritize urgent instructions. This ensures that important instructions are accepted preferentially. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit inputs the user's emotion data as a prompt to the generative AI, which then determines the priority of instructions. The instruction priority may include, but is not limited to, importance evaluation criteria or prioritization algorithms.

[0080] The reception unit can prioritize receiving instructions based on the user's geographical location information. For example, if the user is at home, the reception unit will prioritize instructions related to projects that can be done at home. If the user is out, the reception unit will prioritize instructions related to projects that can be done while out. If the user is in a specific location, the reception unit will prioritize instructions related to that location. This enables responses tailored to the user's situation. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit inputs the user's geographical location information as a prompt to the generative AI, which then filters out the most relevant instructions. Geographical location information includes, but is not limited to, GPS data and location services.

[0081] The reception unit can analyze the user's social media activity when receiving instructions and receive relevant instructions. For example, the reception unit can prioritize instructions related to projects the user has shared on social media. For example, the reception unit can prioritize instructions based on topics the user has shown interest in on social media. For example, the reception unit can prioritize instructions related to projects that the user's social media followers are interested in. This allows for the priority of receiving relevant instructions. Some or all of the above processing in the reception unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception unit inputs the user's social media activity as a prompt to the generative AI, and the generative AI filters out relevant instructions. Social media activity includes, but is not limited to, analyzing post content and evaluating activity frequency.

[0082] The generation unit can estimate the user's emotions and adjust the representation of blueprints and 3D models based on the estimated emotions. For example, if the user is relaxed, the generation unit provides detailed blueprints and 3D models. If the user is in a hurry, the generation unit provides concise and to-the-point blueprints and 3D models. If the user is excited, the generation unit provides blueprints and 3D models with visually appealing effects. This enables the provision of information tailored to the user's state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit inputs user emotion data as a prompt to the generative AI, and the generative AI adjusts the representation of blueprints and 3D models. Methods of representing designs and 3D models include, but are not limited to, changing colors and adjusting shapes.

[0083] The generation unit can adjust the level of detail in the design based on the quality and characteristics of the timber when creating the design drawing. For example, when using high-quality timber, the generation unit provides a detailed design drawing. For example, when using low-quality timber, the generation unit provides a simplified design drawing. For example, when using timber with specific characteristics, the generation unit provides a design drawing that corresponds to those characteristics. This allows for the provision of an appropriate design drawing. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the quality and characteristics of the timber as prompts to the generation AI, and the generation AI adjusts the level of detail in the design. The quality and characteristics of the timber include, but are not limited to, strength, durability, and grain pattern.

[0084] The generation unit can apply different modeling algorithms during 3D modeling depending on the user's image. For example, if the user has a specific image, the generation unit applies a modeling algorithm based on that image. For example, if the user has an abstract image, the generation unit applies an abstract modeling algorithm. For example, if the user has multiple images, the generation unit applies a modeling algorithm that combines them. This makes it possible to create 3D models that meet the user's wishes. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's image as a prompt to the generation AI, and the generation AI applies the optimal modeling algorithm. Different modeling algorithms include, but are not limited to, polygon modeling and sculpting.

[0085] The service provider can estimate the user's emotions and adjust the presentation of the information based on the estimated emotions. For example, if the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can provide concise and to-the-point information. If the user is excited, the service provider can provide information with visually appealing effects. This enables the provision of information tailored to the user's state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the processing described above in the service provider may be performed using a generative AI or not. For example, the service provider inputs the user's emotion data as a prompt to the generative AI, which then adjusts the presentation of the information. The presentation of information includes, but is not limited to, text formatting and image display methods.

[0086] The information provider can adjust the level of detail provided based on the importance of the information at the time of provision. For example, when providing highly important information, the provider will provide information that includes detailed explanations. For example, when providing less important information, the provider will provide concise information. The provider may adjust the level of detail of the information in stages according to its importance. This enables the provision of appropriate information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the provider may input the importance of the information as a prompt to the generative AI, and the generative AI will adjust the level of detail of the information. The importance of the information includes, but is not limited to, the user's level of interest or the urgency of the information.

[0087] The information provider can apply different information provision algorithms depending on the category of information at the time of provision. For example, when providing technical information, the provider may apply a specialized algorithm. For example, when providing general information, the provider may apply a concise algorithm. For example, the provider may select the optimal information provision algorithm depending on a specific category. This enables the provision of appropriate information. Some or all of the above processing in the information provider may be performed using, for example, a generative AI, or without a generative AI. For example, the information provider may input the category of information as a prompt to the generative AI, and the generative AI may apply the optimal information provision algorithm. Categories of information include, but are not limited to, technical information and business information.

[0088] The integration unit can estimate the user's emotions and adjust the method of integration with the 3D printer based on the estimated user emotions. For example, if the user is relaxed, the integration unit provides a detailed integration method. For example, if the user is in a hurry, the integration unit provides a concise and to-the-point integration method. For example, if the user is excited, the integration unit provides an integration method with visually appealing effects. This enables integration tailored to the user's state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit inputs user emotion data as a prompt to the generative AI, and the generative AI adjusts the integration method. The method of integration with the 3D printer may include, but is not limited to, data transfer protocols and print job management methods.

[0089] The integration unit can adjust the level of detail of the integration based on the performance and characteristics of the 3D printer during integration. For example, when using a high-performance 3D printer, the integration unit provides a detailed integration method. For example, when using a low-performance 3D printer, the integration unit provides a simplified integration method. For example, when using a 3D printer with specific characteristics, the integration unit provides an integration method tailored to those characteristics. This enables appropriate integration. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit inputs the performance and characteristics of the 3D printer as prompts to the generating AI, and the generating AI adjusts the level of detail of the integration. The performance and characteristics of the 3D printer include, but are not limited to, print resolution and material type.

[0090] The integration unit can apply different integration algorithms depending on the progress of the user's project during integration. For example, the integration unit applies a basic integration algorithm in the early stages of the project. For example, the integration unit applies a detailed integration algorithm in the middle stages of the project. For example, the integration unit applies an optimized integration algorithm in the final stages of the project. This enables integration that is tailored to the progress of the project. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or without a generative AI. For example, the integration unit inputs the project progress as a prompt to the generative AI, and the generative AI applies the optimal integration algorithm. Integration algorithms include, but are not limited to, project management algorithms and scheduling algorithms.

[0091] The integration unit can estimate the user's emotions and determine the priority of integration with the 3D printer based on the estimated emotions. For example, if the user is relaxed, the integration unit will perform all integrations equally. If the user is in a hurry, the integration unit will prioritize urgent integrations. If the user is excited, the integration unit will prioritize high-priority integrations. This ensures that important integrations are prioritized. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit inputs user emotion data as a prompt to the generative AI, which then determines the priority of integrations. Prioritization of integrations may include, but is not limited to, project importance or user requirement.

[0092] The integration unit can adjust the integration order based on the installation location of the 3D printers during integration. For example, the integration unit prioritizes integration with 3D printers installed nearby. For example, the integration unit postpones integration with 3D printers installed far away. For example, the integration unit adjusts the integration order in stages according to the installation location. This enables efficient integration. Some or all of the above processing in the integration unit may be performed using, for example, a generating AI, or without a generating AI. For example, the integration unit inputs the installation location of the 3D printer as a prompt to the generating AI, and the generating AI adjusts the integration order. The installation location of the 3D printer includes, but is not limited to, the coordinates of the installation location or a layout diagram.

[0093] The integration unit can adjust the integration procedure based on the user's related projects during integration. For example, the integration unit may prioritize integrations related to projects currently underway. For example, the integration unit may postpone integrations related to past projects. For example, the integration unit may systematically perform integrations related to projects planned for the future. This enables integration tailored to each project. Some or all of the above processing in the integration unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the integration unit may input the user's related projects as prompts to the generative AI, and the generative AI will adjust the integration procedure. Related projects include, but are not limited to, the content and progress of projects.

[0094] The update unit can estimate the user's emotions and adjust the timing of database updates based on the estimated emotions. For example, if the user is relaxed, the update unit will update the database regularly. For example, if the user is in a hurry, the update unit will temporarily delay updates. For example, if the user is excited, the update unit will prioritize updating important data. This ensures updates are performed at the appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the update unit may input user emotion data as a prompt to the generative AI, which will adjust the update timing. Database update timing may include, but is not limited to, regular update schedules or updates based on user requests.

[0095] The update unit can optimize the update algorithm by referring to past update history during updates. For example, the update unit analyzes past update history to determine the optimal update timing. For example, the update unit adjusts the update frequency based on the update history. For example, the update unit determines update priorities by referring to the update history. This enables efficient updates. Some or all of the above processes in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit inputs past update history as a prompt to the generative AI, and the generative AI optimizes the update algorithm. The update algorithm includes, but is not limited to, data integrity checks and efficient data update methods.

[0096] The update unit can estimate the user's emotions and adjust the update frequency based on the estimated emotions. For example, if the user is relaxed, the update unit will perform regular updates. For example, if the user is in a hurry, the update unit will reduce the update frequency. For example, if the user is excited, the update unit will prioritize updating important data. This enables updates at an appropriate frequency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the update unit may be performed using a generative AI or not. For example, the update unit inputs user emotion data as a prompt to the generative AI, which then adjusts the update frequency. The update frequency includes, but is not limited to, a regular update schedule or updates based on user requests.

[0097] The update unit can weight the data to be updated based on its importance during the update process. For example, the update unit may prioritize updating data with high importance. For example, the update unit may postpone updating data with low importance. For example, the update unit may adjust the update order in stages according to the importance of the data. This allows important data to be updated preferentially. Some or all of the above processing in the update unit may be performed using, for example, a generative AI, or without a generative AI. For example, the update unit may input the importance of the data as a prompt to the generative AI, and the generative AI will weight the data to be updated. The weighting of the data to be updated may include, but is not limited to, data importance and urgency of update.

[0098] The interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is relaxed, the interface unit will display detailed information. If the user is in a hurry, the interface unit will display concise and to-the-point information. If the user is excited, the interface unit will display information with visually appealing effects. This enables display tailored to the user's state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the interface unit may be performed using a generative AI, or not. For example, the interface unit inputs user emotion data as a prompt to the generative AI, and the generative AI adjusts the display method. The interface display method includes, but is not limited to, changing colors or adjusting the layout.

[0099] The interface unit can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the interface unit may prioritize providing display methods previously used by the user. For example, the interface unit may propose the optimal display method based on the user's operation history. For example, the interface unit may analyze the user's operation history and provide the most efficient display method. This enables efficient display. Some or all of the above processing in the interface unit may be performed using, for example, a generating AI, or without a generating AI. For example, the interface unit inputs the user's past operation history as a prompt to the generating AI, and the generating AI selects the optimal display method. The optimal display method includes, but is not limited to, a method for analyzing the user's operation history and criteria for evaluating the display method.

[0100] The interface unit can estimate the user's emotions and adjust the interface's operating procedures based on the estimated user emotions. For example, if the user is relaxed, the interface unit provides detailed operating procedures. For example, if the user is in a hurry, the interface unit provides concise and to-the-point operating procedures. For example, if the user is excited, the interface unit provides operating procedures with visually appealing effects. This enables operation tailored to the user's state. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interface unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the interface unit inputs user emotion data as a prompt to the generative AI, and the generative AI adjusts the operating procedures. The interface's operating procedures include, but are not limited to, simplification of operations and adjustments based on the user's operation history.

[0101] The interface unit can select the optimal display method based on the user's device information when displaying the interface. For example, if the user is using a smartphone, the interface unit provides a display method that matches the screen size. For example, if the user is using a tablet, the interface unit provides a display method optimized for a large screen. For example, if the user is using a smartwatch, the interface unit provides a concise and highly visible display method. This enables efficient display. Some or all of the above processing in the interface unit may be performed using, for example, a generative AI, or without a generative AI. For example, the interface unit inputs the user's device information as a prompt to the generative AI, and the generative AI selects the optimal display method. Device information includes, but is not limited to, the type of device, performance, and usage status.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The reception system can suggest the most suitable method of receiving instructions from a user by referring to the user's past instruction history. For example, it can prioritize suggesting instruction methods that the user has frequently used in the past (voice, text, etc.). It can also automatically suggest relevant instructions based on the user's past project history. Furthermore, it can suggest the most suitable method of receiving instructions for a specific time period based on the user's past instruction history. This allows users to select the most suitable method of receiving instructions and give instructions efficiently.

[0104] The generation unit can adjust the level of detail in the design based on the quality and characteristics of the lumber when creating a design drawing based on the size of the lumber purchased by the user. For example, when using high-quality lumber, a detailed design drawing can be provided. Conversely, when using low-quality lumber, a simplified design drawing can be provided. Furthermore, when using lumber with specific characteristics, a design drawing tailored to those characteristics can be provided. This allows the user to obtain an appropriate design drawing according to the quality and characteristics of the lumber.

[0105] The generation unit can estimate the user's emotions when creating 3D models based on the user's image, and adjust the model's representation based on those emotions. For example, if the user is relaxed, a detailed 3D model can be provided. If the user is in a hurry, a concise and to-the-point 3D model can be provided. Furthermore, if the user is excited, a 3D model with visually appealing effects can be provided. This enables 3D modeling tailored to the user's state.

[0106] The system can estimate the user's mood when providing a list of necessary screws and tools, and adjust how the list is displayed based on that estimate. For example, if the user is relaxed, a detailed list can be provided. If the user is in a hurry, a concise and to-the-point list can be provided. Furthermore, if the user is excited, a list with visually appealing effects can be provided. This makes it possible to provide lists tailored to the user's state.

[0107] The integration unit can estimate the user's emotions when outputting objects in conjunction with a 3D printer, and adjust the integration method based on the estimated emotions. For example, if the user is relaxed, it can provide a detailed integration method. If the user is in a hurry, it can provide a concise and to-the-point integration method. Furthermore, if the user is excited, it can provide an integration method with visually appealing effects. This enables integration tailored to the user's state.

[0108] The update unit can estimate the user's emotions when updating or maintaining the database, and adjust the update timing based on the estimated emotions. For example, if the user is relaxed, the database can be updated regularly. If the user is in a hurry, updates can be temporarily delayed. Furthermore, if the user is excited, important data can be prioritized for updates. This ensures that updates are performed at the appropriate time.

[0109] The interface unit can estimate the user's emotions and adjust the interface display based on those emotions when providing an easy-to-use interface. For example, if the user is relaxed, detailed information can be displayed. If the user is in a hurry, concise and to-the-point information can be displayed. Furthermore, if the user is excited, information with visually appealing effects can be displayed. This enables display tailored to the user's state.

[0110] The reception desk can prioritize receiving user instructions based on the user's geographical location. For example, if a user is at home, it can prioritize instructions related to projects that can be done at home. If a user is out, it can prioritize instructions related to projects that can be done while out. Furthermore, if a user is in a specific location, it can prioritize instructions related to that location. This allows for responses tailored to the user's situation.

[0111] The generation unit can apply different modeling algorithms when creating 3D models based on the user's image. For example, if the user has a specific image, a modeling algorithm based on that image can be applied. If the user has an abstract image, an abstract modeling algorithm can be applied. Furthermore, if the user has multiple images, a modeling algorithm that combines them can be applied. This makes it possible to create 3D models that meet the user's wishes.

[0112] The information provider can adjust the level of detail provided based on the importance of the information being offered. For example, when providing highly important information, it can include detailed explanations. Conversely, when providing less important information, it can provide concise information. Furthermore, the level of detail can be adjusted in stages according to importance. This enables the provision of appropriate information.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception unit receives user instructions. User instructions include voice instructions, text instructions, and gesture instructions. For example, voice instructions can be received using speech recognition technology, text instructions can be received using a text input interface, and gesture instructions can be received using gesture recognition technology. Step 2: The generation unit creates blueprints and 3D models based on the instructions received by the reception unit. For example, it generates blueprints and 3D models in formats such as CAD data and STL files. If the user instructs "I want to make a bookshelf," it creates blueprints based on the size of the lumber the user will purchase. If the user instructs "I want a fighter jet object," it creates a 3D model based on the user's image. Processing in the generation unit may also be performed using generation AI. Step 3: The providing unit provides the information generated by the generating unit. For example, it provides information in the form of a preview of a design drawing or a display of a 3D model. This could include providing the user with a list of necessary screws and tools, displaying a preview of a design drawing so the user can review it, or displaying a 3D model so the user can review it. Processing in the providing unit may also be performed using a generating AI. Step 4: The integration unit interacts with the 3D printer based on the information provided by the supply unit. For example, it interacts with the 3D printer in the form of data transmission methods and print job management methods. It sends the generated 3D model to the 3D printer, instructs the 3D printer to output the object, manages print jobs, and monitors the 3D printer's operating status. Processing in the integration unit may also be performed using generation AI.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, collaboration unit, update unit, and interface unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives user instructions using voice recognition technology or a text input interface. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings or 3D models based on user instructions. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the generated information to the user. The collaboration unit is implemented by the specific processing unit 290 of the data processing unit 12 and outputs objects in cooperation with a 3D printer. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs database updates and maintenance. The interface unit is implemented by the control unit 46A of the smart device 14 and provides an interface that the user can easily operate. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, cooperation unit, update unit, and interface unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives user instructions using voice recognition technology and a text input interface. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings and 3D models based on user instructions. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the generated information to the user. The cooperation unit is implemented by the specific processing unit 290 of the data processing unit 12 and outputs objects in cooperation with a 3D printer. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs database updates and maintenance. The interface unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface that the user can easily operate. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, cooperation unit, update unit, and interface unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives user instructions using voice recognition technology and a text input interface. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings and 3D models based on user instructions. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the generated information to the user. The cooperation unit is implemented by the specific processing unit 290 of the data processing unit 12 and outputs objects in cooperation with a 3D printer. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs database updates and maintenance. The interface unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface that the user can easily operate. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, cooperation unit, update unit, and interface unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives user instructions using voice recognition technology or a text input interface. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates design drawings or 3D models based on user instructions. The provision unit is implemented by the control unit 46A of the robot 414 and provides the generated information to the user. The cooperation unit is implemented by the specific processing unit 290 of the data processing unit 12 and outputs objects in cooperation with a 3D printer. The update unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs database updates and maintenance. The interface unit is implemented by the control unit 46A of the robot 414 and provides an interface that the user can easily operate. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A reception desk that takes user instructions, A generation unit that generates design drawings and 3D models based on instructions received by the aforementioned reception unit, A providing unit that provides the information generated by the generation unit, The system includes a cooperation unit that interacts with a 3D printer based on the information provided by the aforementioned provisioning unit. A system characterized by the following features. (Note 2) The generating unit is The design plans are created based on the size of the lumber the user purchases. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Perform 3D modeling based on the user's image. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provide a list of necessary screws and tools. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned linkage unit is, Outputting objects in conjunction with a 3D printer. The system described in Appendix 1, characterized by the features described herein. (Note 6) It also includes an update unit for updating and maintaining the database. The system described in Appendix 1, characterized by the features described herein. (Note 7) It further includes an interface section that provides an interface that users can easily operate. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of receiving instructions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past instruction history and select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and determines the priority of instructions to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving instructions, the system prioritizes receiving instructions that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving instructions, the system analyzes the user's social media activity and accepts relevant instructions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the representation of design drawings and 3D models based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When creating design drawings, adjust the level of detail in the design based on the quality and characteristics of the timber. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When 3D modeling, different modeling algorithms are applied depending on the user's vision. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the information provided is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing information, different delivery algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the way it interacts with the 3D printer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, During integration, the level of detail in the integration is adjusted based on the performance and characteristics of the 3D printer. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, During integration, different integration algorithms are applied depending on the progress of the user's project. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of 3D printer integration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, During integration, the integration order is adjusted based on the location of the 3D printer. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, During integration, the integration procedure is adjusted based on the user's related projects. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned update unit is It estimates the user's emotions and adjusts the timing of database updates based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned update unit is During updates, the update algorithm is optimized by referring to past update history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned update unit is It estimates user sentiment and adjusts the update frequency based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned update unit is During updates, the updated data is weighted based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 30) The interface unit is It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The interface unit is When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The interface unit is It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The interface unit is When displaying the interface, the optimal display method is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that takes user instructions, A generation unit that generates design drawings and 3D models based on instructions received by the reception unit, A providing unit that provides the information generated by the generation unit, The system includes a cooperation unit that interacts with a 3D printer based on the information provided by the aforementioned provisioning unit. A system characterized by the following features.

2. The generating unit is The design plans are created based on the size of the lumber the user purchases. The system according to feature 1.

3. The generating unit is Perform 3D modeling based on the user's image. The system according to feature 1.

4. The aforementioned supply unit is, Provide a list of necessary screws and tools. The system according to feature 1.

5. The aforementioned linkage unit is, Outputting objects in conjunction with a 3D printer. The system according to feature 1.

6. It also includes an update unit for updating and maintaining the database. The system according to feature 1.

7. It further includes an interface section that provides an interface that users can easily operate. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of receiving instructions based on the estimated user emotions. The system according to feature 1.

9. The aforementioned reception unit is Analyze the user's past instruction history and select the optimal reception method. The system according to feature 1.

10. The aforementioned reception unit is When receiving instructions, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

11. The aforementioned reception unit is It estimates the user's emotions and determines the priority of instructions to accept based on the estimated user emotions. The system according to feature 1.

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