system

Abstract

The system according to this embodiment aims to efficiently create 3D models based on the buyer's requests and provide the product. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a confirmation unit, and a creation unit. The reception unit receives the buyer's request. The proposal unit analyzes the request received by the reception unit and proposes specific specifications. The confirmation unit checks the 3D model proposed by the proposal unit and requests revisions. The creation unit creates the product based on the 3D model revised by the confirmation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since the process of recognizing detailed specifications based on the demands of purchasers and creating 3D models is carried out between individuals, there is a problem that it is not efficient.

[0005] The system according to the embodiment aims to efficiently create a 3D model based on the demands of purchasers and provide products.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a confirmation unit, and a creation unit. The reception unit receives requests from the buyer. The proposal unit analyzes the requests received by the reception unit and proposes specific specifications. The confirmation unit checks the 3D model proposed by the proposal unit and requests revisions. The creation unit creates the product based on the 3D model revised by the confirmation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently create 3D models based on the buyer's requests and provide the product. [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 labeled 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 applied 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) This service is a system that matches people who own home 3D printers with people who want to create their own products. It uses a generation AI to support detailed specification matching and 3D model creation. Specifically, it consists of the following steps: First, the buyer inputs a request such as "I want a product like this." For example, they might input "I want an accessory stand of a specific shape." This request is input into the generation AI. Next, the generation AI analyzes the input request and proposes specific specifications. The generation AI generates a 3D model based on the request and proposes it to the buyer. For example, it proposes specifications such as the shape, size, and material of an accessory stand. The generated 3D model is shared between the buyer and the 3D printer owner. The buyer reviews the proposed 3D model and requests modifications as needed. The 3D printer owner creates the product based on the modified 3D model. This system allows buyers to easily create their own products, and solves the problem of 3D printer owners not having anything to create. In addition, by using the generation AI, detailed specification matching is carried out smoothly, and products can be created efficiently. For example, if a buyer inputs "I want an accessory stand of a specific shape," the AI ​​analyzes the request and proposes specific specifications. The buyer then reviews the proposed 3D model and requests modifications as needed. Based on the modified 3D model, a 3D printer owner creates the product. In this way, a system is realized that efficiently matches buyers and 3D printer owners, allowing them to create their own unique products. This enables efficient matching of people who own home 3D printers with those who want to create their own products, and provides support for detailed specification adjustments and 3D model creation.

[0029] The matching system according to this embodiment comprises a reception unit, a proposal unit, a confirmation unit, and a creation unit. The reception unit receives requests from buyers. These requests include, but are not limited to, specific requests such as wanting an accessory stand of a particular shape. The reception unit can receive requests in various formats, such as text, audio, or image. The proposal unit uses a generation AI to analyze the requests received by the reception unit and propose specific specifications. The proposal unit generates a 3D model based on the requests and proposes it to the buyer. For example, the generation AI proposes specifications such as the shape, size, and material of the accessory stand based on the requests. The proposal unit can also use the generation AI to propose specific specifications based on the requests. The confirmation unit checks the 3D model proposed by the proposal unit and requests modifications as necessary. The confirmation unit can check the 3D model using methods such as visual confirmation, functional confirmation, and user testing. The confirmation unit can also check the proposed 3D model and request modifications as necessary. The creation unit creates the product based on the 3D model modified by the confirmation unit. The creation unit, for example, creates products using a 3D printer. The creation unit can also create products based on a modified 3D model. As a result, the matching system according to this embodiment can efficiently receive, analyze, propose, confirm, and create products based on the buyer's requests.

[0030] The reception desk receives requests from buyers. These requests include, but are not limited to, specific requests such as wanting an accessory stand of a particular shape. The reception desk can accept requests in various formats, such as text, voice, and image. Specifically, in text format, buyers can input detailed specifications and design requests for the product they want in written form. In voice format, buyers can verbally communicate their requests via a microphone, which are then converted to text using speech recognition technology. In image format, buyers can upload reference images or sketches to communicate visual requests. Furthermore, the reception desk has the functionality to centrally manage and store these requests in a database. This ensures that all buyer requests are recorded without fail and are smoothly carried over to subsequent processing. The reception desk also provides a user-friendly interface to facilitate smooth interaction with buyers, and is designed to make request input easy and intuitive. For example, the input form displays guidelines and samples to support buyers in entering their requests without confusion. This allows the reception department to efficiently receive diverse requests from customers and quickly move on to the next step.

[0031] The proposal department uses a generative AI to analyze requests received by the reception department and propose specific specifications. For example, the proposal department generates a 3D model based on the request and proposes it to the buyer. Specifically, the generative AI uses natural language processing technology to analyze text-based requests and understand the buyer's intent. For voice-based requests, it uses speech recognition technology to convert them to text and then performs the same analysis. For image-based requests, it uses image recognition technology to extract features from uploaded images and understand the buyer's desired design. Based on these analysis results, the generative AI proposes specific specifications such as the shape, size, and material of the accessory stand. For example, if a buyer requests a "wooden accessory stand that is 20 cm tall," the generative AI will generate a 3D model of a 20 cm tall wooden stand based on that request and propose it to the buyer. The generative AI can also refer to past data and trends to propose the optimal design and functions for the buyer. The proposal department provides an interface that allows buyers to visually confirm the generated 3D model, enabling them to intuitively understand the proposal. This allows the proposal department to quickly and accurately propose specific specifications based on the buyer's requests, thereby increasing buyer satisfaction.

[0032] The verification department reviews the 3D model proposed by the proposal department and requests revisions as needed. The verification department can review the 3D model using methods such as visual verification, functional verification, and user testing. Specifically, visual verification provides an interface that allows the buyer to rotate the 3D model 360 degrees and view it from all angles, checking for any problems with the design or shape. Functional verification simulates whether the dimensions, materials, and structure of the 3D model can withstand actual use and evaluates its strength and durability. User testing involves the buyer or a third party actually using the 3D model to collect feedback on usability and design. Through these verification processes, the buyer can request revisions as many times as needed until they are satisfied. When a revision request is received, the verification department works in cooperation with the proposal department to quickly make the revisions and repeat the verification process. Furthermore, the verification department provides a chat function and feedback form to facilitate smooth communication with the buyer and support the buyer in easily requesting revisions. This allows the verification unit to meticulously check and revise the proposed 3D model until it perfectly matches the buyer's requirements, preparing for the final product creation.

[0033] The production department creates products based on the 3D models corrected by the verification department. For example, the production department uses a 3D printer to create the products. Specifically, the data of the corrected 3D model is input into the 3D printer, and the product is created layer by layer using the specified material. 3D printers utilize high-precision layering technology to create products that faithfully reproduce even the smallest details. The production department configures the 3D printer and selects materials to create the products under optimal conditions. Furthermore, the production department implements inspection processes for quality control to ensure that the finished products are created according to the design. For example, dimensional measurements and visual inspections are performed to check for defects. In addition, the production department is flexible in creating customized products according to the buyer's requests. For example, if a specific color or finish is desired, appropriate painting or processing can be applied. This allows the production department to quickly create high-quality products based on the 3D models corrected by the verification department and deliver them to the buyer. Finally, the production department manages the entire process from packaging the finished products to shipping them to the buyer, ensuring that the buyer receives a product they are satisfied with.

[0034] The proposal department can generate 3D models based on requests. For example, the proposal department can propose specifications such as the shape, size, and material of an accessory stand based on requests. The proposal department can also generate 3D models based on requests using a generation AI. For example, the generation AI can generate a specific 3D model based on requests and propose it to the buyer. This makes it possible to make specific proposals by generating 3D models based on requests. Some or all of the above processes in the proposal department may be performed using a generation AI, or they may be performed without a generation AI. For example, the proposal department inputs requests as input to the generation AI, and the generation AI generates a 3D model.

[0035] The proposal department can use a generative AI to propose specific specifications based on the request. For example, the proposal department can propose specifications such as the shape, size, and material of an accessory stand based on the request. The proposal department can also use a generative AI to propose specific specifications based on the request. For example, the generative AI proposes specific specifications based on the request and presents them to the buyer. This makes it possible to propose specific specifications based on the request by using a generative AI. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without using a generative AI. For example, the proposal department inputs the request as input to the generative AI, and the generative AI proposes specific specifications.

[0036] The verification unit can review the proposed 3D model and request modifications as needed. The verification unit can review the 3D model using methods such as visual review, functional review, and user testing. The verification unit can also review the proposed 3D model and request modifications as needed. For example, the verification unit can visually review the proposed 3D model and request modifications as needed. The verification unit can also review the functionality of the proposed 3D model and request modifications as needed. This allows for the creation of highly accurate products by reviewing the proposed 3D model and requesting modifications as needed. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs the proposed 3D model into the AI, and the AI ​​determines the need for modifications.

[0037] The creation unit can create products based on the modified 3D model. The creation unit can, for example, create products using a 3D printer. The creation unit can also create products based on the modified 3D model. For example, the creation unit inputs the modified 3D model into a 3D printer to create the product. Alternatively, the creation unit can manually create products based on the modified 3D model. This makes it possible to create products that meet the buyer's needs by creating products based on the modified 3D model. Some or all of the above processes in the creation unit may be performed using AI, or not. For example, the creation unit inputs the modified 3D model into AI, and the AI ​​instructs the AI ​​on the steps to create the product.

[0038] The system includes a storage unit for saving the 3D models generated by the proposal unit. The storage unit can save the generated 3D models. For example, the storage unit can save the 3D models in digital format. The storage unit can also save the generated 3D models. For example, the storage unit can save the 3D models to cloud storage. The storage unit can also save the 3D models to local storage. By saving the generated 3D models, they can be referenced and modified later. Some or all of the above-described processes in the storage unit may be performed using AI or not. For example, the storage unit inputs the generated 3D models into the AI, and the AI ​​instructs the AI ​​on the saving procedure.

[0039] The verification unit includes a modification unit to which the modification request is made. The modification unit can modify the proposed 3D model. For example, the modification unit can modify the shape, size, and material of the 3D model. The modification unit can also modify the proposed 3D model. For example, the modification unit can modify the shape of the 3D model. The modification unit can also modify the size of the 3D model. Thus, by including a modification unit, the modification of the proposed 3D model can be performed efficiently. Some or all of the above processing in the modification unit may be performed using AI or not. For example, the modification unit inputs the proposed 3D model into the AI, and the AI ​​instructs the modification procedure.

[0040] The system includes a management unit that manages the progress of the product creation process by the creation unit. The management unit can manage the progress of product creation. For example, the management unit can monitor the progress of product creation. The management unit can also manage the progress of product creation. For example, the management unit can display the progress of product creation in real time. The management unit can also record the progress of product creation and refer to it later. This makes it easier to understand the progress of product creation by managing the progress. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit inputs the progress of product creation into the AI, and the AI ​​instructs the AI ​​on the steps for progress management.

[0041] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requests that will be used during specific time periods based on the user's past request history. In this way, by analyzing past request history, the reception desk can provide the user with the most suitable reception method. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past request history into the AI, and the AI ​​can select the most suitable reception method.

[0042] The reception desk can filter requests based on the user's current projects and areas of interest. For example, the reception desk can prioritize requests related to projects the user is currently working on. The reception desk can also filter and display highly relevant requests based on the user's areas of interest. The reception desk can also filter requests based on areas the user has shown interest in in the past. This allows for priority reception of highly relevant requests by filtering requests based on the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's project information into the AI, and the AI ​​performs the filtering.

[0043] The reception desk can prioritize requests based on the user's geographical location when receiving requests. For example, the reception desk can prioritize requests related to the user's current location. The reception desk can also filter and display region-specific requests based on the user's geographical location. The reception desk can also prioritize requests related to places the user has visited in the past. This allows for addressing region-specific requests by prioritizing requests based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's geographical location into the AI, which then filters out the most relevant requests.

[0044] The reception desk can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can prioritize requests that the user has mentioned on social media. The reception desk can also filter and display requests related to areas of interest based on the user's social media activity. The reception desk can also accept relevant requests based on the activity of accounts that the user follows. This allows for requests based on the user's interests by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which then filters out relevant requests.

[0045] The proposal department can adjust the level of detail of a proposal based on the importance of the request. For example, the proposal department will provide a detailed proposal for high-priority requests. For low-priority requests, it can provide a concise proposal. The proposal department can also adjust the level of detail of a proposal in stages according to the importance of the request. This allows for appropriate proposals by adjusting the level of detail according to the importance of the request. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department inputs the importance of the request into the generation AI, and the generation AI adjusts the level of detail of the proposal.

[0046] The proposal unit can apply different proposal algorithms depending on the category of the request when making a proposal. For example, the proposal unit can apply a design-focused proposal algorithm to accessory-related requests. The proposal unit can also apply a functionality-focused proposal algorithm to furniture-related requests. The proposal unit can also apply a safety-focused proposal algorithm to toy-related requests. By applying a proposal algorithm according to the category of the request, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit inputs the category of the request into the generative AI, and the generative AI applies a proposal algorithm.

[0047] The proposal department can determine the priority of proposals based on when the requests were submitted. For example, the proposal department may prioritize recently submitted requests. The proposal department can also re-examine and submit proposals for older requests. The proposal department can also adjust the priority of proposals in stages based on the submission date. This allows for a quick response to the latest requests by determining the priority of proposals based on the submission date. Some or all of the above processes in the proposal department may be performed using a generation AI, or not. For example, the proposal department inputs the submission dates of requests into the generation AI, and the generation AI determines the priority of proposals.

[0048] The proposal department can adjust the order of proposals based on the relevance of the requests. For example, the proposal department will prioritize proposals for highly relevant requests. For less relevant requests, the proposal department can also provide concise proposals. The proposal department can also adjust the order of proposals in stages according to the relevance of the requests. This allows for prioritizing highly relevant proposals by adjusting the order of proposals based on the relevance of the requests. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department inputs the relevance of the requests into a generative AI, and the generative AI adjusts the order of proposals.

[0049] The verification unit can adjust the level of detail of the verification based on the importance of the proposed 3D model during the verification process. For example, the verification unit can perform a detailed verification for 3D models with high importance. The verification unit can also perform a simplified verification for 3D models with low importance. The verification unit can also adjust the level of detail of the verification in stages according to the importance of the 3D model. This allows for appropriate verification by adjusting the level of detail of the verification according to the importance of the 3D model. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit inputs the importance of the 3D model into the AI, and the AI ​​adjusts the level of detail of the verification.

[0050] The verification unit can apply different verification algorithms depending on the category of the proposed 3D model during verification. For example, the verification unit can apply a design-focused verification algorithm to accessory-related 3D models. It can also apply a functionality-focused verification algorithm to furniture-related 3D models. It can also apply a safety-focused verification algorithm to toy-related 3D models. This allows for more appropriate verification by applying a verification algorithm appropriate to the category of the 3D model. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit inputs the category of the 3D model into the AI, and the AI ​​applies a verification algorithm.

[0051] The verification unit can adjust the order of verification based on the submission date of the proposed 3D models. For example, the verification unit prioritizes the verification of recently submitted 3D models. The verification unit can also re-verify older 3D models and adjust their priority. The verification unit can also adjust the order of verification in stages based on the submission date. This allows for quick response to the latest 3D models by adjusting the order of verification based on the submission date of the 3D models. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs the submission date of the 3D models into the AI, and the AI ​​adjusts the order of verification.

[0052] The verification unit can adjust the order of verification based on the relevance of the proposed 3D models during the verification process. For example, the verification unit prioritizes verification of highly relevant 3D models. The verification unit can also perform a brief verification of less relevant 3D models. The verification unit can also adjust the order of verification in stages according to the relevance of the 3D models. This allows for priority verification of highly relevant 3D models by adjusting the order of verification based on the relevance of the 3D models. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit inputs the relevance of the 3D models into the AI, and the AI ​​adjusts the order of verification.

[0053] The creation unit can adjust the level of detail during creation based on the importance of the modified 3D model. For example, the creation unit can provide detailed creation instructions for high-importance 3D models. For low-importance 3D models, the creation unit can also provide concise creation instructions. The creation unit can also adjust the level of detail in stages according to the importance of the 3D model. This allows for appropriate creation by adjusting the level of detail according to the importance of the 3D model. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit inputs the importance of the 3D model into the AI, and the AI ​​adjusts the level of detail.

[0054] The creation unit can apply different creation algorithms depending on the category of the modified 3D model during creation. For example, the creation unit can apply a design-focused creation algorithm to accessory-related 3D models. It can also apply a functionality-focused creation algorithm to furniture-related 3D models. It can also apply a safety-focused creation algorithm to toy-related 3D models. This allows for more appropriate creation by applying a creation algorithm appropriate to the category of the 3D model. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit inputs the category of the 3D model into the AI, and the AI ​​applies a creation algorithm.

[0055] The creation unit can adjust the creation order based on the submission dates of revised 3D models during the creation process. For example, the creation unit can prioritize the creation of recently submitted 3D models. The creation unit can also re-examine older 3D models and adjust their priority. The creation unit can also adjust the creation order in stages based on the submission dates. This allows for quick response to the latest 3D models by adjusting the creation order based on the submission dates of the 3D models. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit inputs the submission dates of the 3D models into the AI, and the AI ​​adjusts the creation order.

[0056] The creation unit can adjust the creation order based on the relationships between modified 3D models during creation. For example, the creation unit can prioritize the creation of highly relevant 3D models. The creation unit can also perform a simplified creation for less relevant 3D models. The creation unit can also adjust the creation order stepwise according to the relationships between the 3D models. This allows for the prioritization of highly relevant 3D models by adjusting the creation order based on the relationships between the 3D models. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit inputs the relationships between the 3D models into the AI, and the AI ​​adjusts the creation order.

[0057] The storage unit can manage the versions of the 3D models being saved, allowing for comparison with past versions. For example, the storage unit can save each version of the 3D model with a timestamp, enabling comparison with past versions. The storage unit can also record the change history of the 3D model and visually display which parts have been changed. The storage unit can also automatically detect differences between versions of the 3D model and notify the user. This makes it easier to compare with past versions by managing the versions of the 3D model. Some or all of the above processes in the storage unit may be performed using AI or not. For example, the storage unit can input the 3D model version control into AI, and the AI ​​can perform the version control.

[0058] The storage unit can automatically generate metadata for 3D models during saving, improving searchability. For example, when saving a 3D model, the storage unit automatically generates metadata such as the model's shape, size, and materials. Based on the 3D model's metadata, the storage unit can also provide a search function, making it easier for users to find models. The storage unit can also tag the 3D model's metadata and group related models. This improves searchability by automatically generating the 3D model's metadata. Some or all of the above processes in the storage unit may be performed using AI or not. For example, the storage unit inputs the 3D model's metadata generation into the AI, and the AI ​​generates the metadata.

[0059] The storage unit can support multiple formats for saving 3D models, ensuring compatibility between different software. For example, the storage unit can save 3D models in multiple formats such as STL, OBJ, and PLY, ensuring compatibility between different software. The storage unit can also allow the user to select their preferred format when saving a 3D model. The storage unit can also provide a 3D model format conversion function, making conversion between different formats easy. This ensures compatibility between different software by supporting multiple formats. Some or all of the above processing in the storage unit may be performed using AI, or not. For example, the storage unit inputs the 3D model format conversion into AI, and the AI ​​performs the format conversion.

[0060] The saving unit can save related documents and images of the 3D model together with the 3D model when saving, making them easier to refer to. For example, the saving unit can save related design drawings and specifications together with the 3D model. The saving unit can also save rendered images of the model together with the 3D model, allowing for visual verification. The saving unit can also save related comments and feedback together with the 3D model, making them easier to refer to. This makes them easier to refer to by saving related documents and images together. Some or all of the above processes in the saving unit may be performed using AI or not. For example, the saving unit inputs related documents and images of the 3D model into the AI, and the AI ​​instructs the AI ​​on the saving procedure.

[0061] The modification unit can adjust the level of detail of the modification based on the importance of the proposed 3D model during the modification process. For example, the modification unit can provide detailed modification procedures for highly important 3D models. For less important 3D models, the modification unit can also provide concise modification procedures. The modification unit can also adjust the level of detail of the modification in stages according to the importance of the 3D model. This allows for appropriate modification by adjusting the level of detail of the modification according to the importance of the 3D model. Some or all of the above processes in the modification unit may be performed using AI or not. For example, the modification unit inputs the importance of the 3D model into the AI, and the AI ​​adjusts the level of detail of the modification.

[0062] The revision unit can adjust the order of revisions based on the submission dates of the proposed 3D models. For example, the revision unit prioritizes revisions to recently submitted 3D models. The revision unit can also re-examine older 3D models and adjust their priorities. The revision unit can also adjust the order of revisions in stages based on the submission dates. This allows for quick response to the latest 3D models by adjusting the order of revisions based on the submission dates of the 3D models. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit inputs the submission dates of the 3D models into the AI, and the AI ​​adjusts the order of revisions.

[0063] The management department can select the optimal management method by referring to past progress data when managing progress. For example, the management department can propose the optimal progress management method based on past progress data. The management department can also extract problems from past progress data and propose improvement measures. The management department can also analyze past progress data and select an efficient progress management method. In this way, the optimal progress management method can be selected by referring to past progress data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs past progress data into the AI, and the AI ​​selects the optimal management method.

[0064] The management department can select the optimal management method when managing progress, taking into account the user's device information. For example, if the user is using a smartphone, the management department can provide a progress management method that is adapted to the screen size. If the user is using a tablet, the management department can also provide a progress management method optimized for a larger screen. If the user is using a smartwatch, the management department can also provide a simple and highly visible progress management method. By selecting the optimal progress management method based on the user's device information, user-friendly progress management is provided. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs the user's device information into the AI, and the AI ​​selects the optimal management method.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The proposal department can analyze the user's past request history and make optimal suggestions. For example, it can suggest similar 3D models based on requests the user has frequently entered in the past. It can also customize suggestions by considering materials and designs the user has used in the past. Furthermore, it can predict requests that the user will use at a specific time of day based on their past request history and make suggestions accordingly. In this way, analyzing past request history makes it possible to make optimal suggestions for the user. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department inputs the user's past request history into a generative AI, and the generative AI makes the optimal suggestion.

[0067] The suggestion unit can make highly relevant suggestions based on the user's geographical location information. For example, it can suggest designs and materials related to the user's current location. It can also suggest designs and cultures specific to a particular region based on the user's geographical location information. Furthermore, it can suggest designs related to places the user has visited in the past. This enables highly relevant suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's geographical location information into a generative AI, and the generative AI makes highly relevant suggestions.

[0068] The suggestion department can analyze a user's social media activity and make relevant suggestions. For example, it can make suggestions based on designs and materials mentioned by the user on social media. It can also make suggestions related to areas of interest based on the user's social media activity. Furthermore, it can make relevant suggestions based on the activity of accounts the user follows. This makes it possible to make suggestions based on the user's interests by analyzing social media activity. Some or all of the above processing in the suggestion department may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion department inputs the user's social media data into a generative AI, and the generative AI makes relevant suggestions.

[0069] The proposal department can adjust the level of detail in a proposal based on the importance of the request. For example, it can provide a detailed proposal for high-priority requests and a concise proposal for low-priority requests. Furthermore, it can adjust the level of detail in a proposal stepwise according to the importance of the request. This allows for proposals that are appropriate to the importance of the request, ensuring that suitable proposals are made. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department inputs the importance of the request into the generation AI, and the generation AI adjusts the level of detail in the proposal.

[0070] The proposal unit can apply different proposal algorithms depending on the category of the request. For example, a design-focused proposal algorithm can be applied to accessory-related requests. A functionality-focused proposal algorithm can be applied to furniture-related requests. Furthermore, a safety-focused proposal algorithm can be applied to toy-related requests. This allows for the application of a proposal algorithm appropriate to the category of the request, enabling more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit inputs the category of the request into the generative AI, and the generative AI applies a proposal algorithm.

[0071] The proposal department can determine the priority of proposals based on when the requests were submitted. For example, it can prioritize recently submitted requests. It can also re-examine older requests and submit proposals again. Furthermore, it can adjust the priority of proposals in stages based on the submission date. This ensures that proposals are prioritized based on the submission date and that the latest requests are addressed quickly. Some or all of the above processes in the proposal department may be performed using a generation AI, or not. For example, the proposal department inputs the submission dates of requests into the generation AI, and the generation AI determines the priority of proposals.

[0072] The proposal department can adjust the order of proposals based on the relevance of the requests. For example, it can prioritize proposals for highly relevant requests. It can also provide concise proposals for less relevant requests. Furthermore, it can adjust the order of proposals in stages according to the relevance of the requests. This adjusts the order of proposals based on the relevance of the requests, prioritizing highly relevant proposals. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department inputs the relevance of the requests into a generation AI, and the generation AI adjusts the order of proposals.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The reception desk receives requests from buyers. These requests may include specific requests such as wanting an accessory stand of a particular shape. The reception desk can accept requests in various formats, including text, audio, and image. Step 2: The proposal department uses a generation AI to analyze the requests received by the reception department and propose specific specifications. The proposal department generates a 3D model based on the requests and proposes it to the buyer. For example, the generation AI proposes specifications such as the shape, size, and material of the accessory stand based on the requests. Step 3: The verification team reviews the 3D model proposed by the proposal team and requests modifications as needed. The verification team can review the 3D model using methods such as visual verification, functional verification, and user testing. Step 4: The creation unit creates the product based on the 3D model corrected by the verification unit. The creation unit uses a 3D printer to create the product.

[0075] (Example of form 2) This service is a system that matches people who own home 3D printers with people who want to create their own products. It uses a generation AI to support detailed specification matching and 3D model creation. Specifically, it consists of the following steps: First, the buyer inputs a request such as "I want a product like this." For example, they might input "I want an accessory stand of a specific shape." This request is input into the generation AI. Next, the generation AI analyzes the input request and proposes specific specifications. The generation AI generates a 3D model based on the request and proposes it to the buyer. For example, it proposes specifications such as the shape, size, and material of an accessory stand. The generated 3D model is shared between the buyer and the 3D printer owner. The buyer reviews the proposed 3D model and requests modifications as needed. The 3D printer owner creates the product based on the modified 3D model. This system allows buyers to easily create their own products, and solves the problem of 3D printer owners not having anything to create. In addition, by using the generation AI, detailed specification matching is carried out smoothly, and products can be created efficiently. For example, if a buyer inputs "I want an accessory stand of a specific shape," the AI ​​analyzes the request and proposes specific specifications. The buyer then reviews the proposed 3D model and requests modifications as needed. Based on the modified 3D model, a 3D printer owner creates the product. In this way, a system is realized that efficiently matches buyers and 3D printer owners, allowing them to create their own unique products. This enables efficient matching of people who own home 3D printers with those who want to create their own products, and provides support for detailed specification adjustments and 3D model creation.

[0076] The matching system according to this embodiment comprises a reception unit, a proposal unit, a confirmation unit, and a creation unit. The reception unit receives requests from buyers. These requests include, but are not limited to, specific requests such as wanting an accessory stand of a particular shape. The reception unit can receive requests in various formats, such as text, audio, or image. The proposal unit uses a generation AI to analyze the requests received by the reception unit and propose specific specifications. The proposal unit generates a 3D model based on the requests and proposes it to the buyer. For example, the generation AI proposes specifications such as the shape, size, and material of the accessory stand based on the requests. The proposal unit can also use the generation AI to propose specific specifications based on the requests. The confirmation unit checks the 3D model proposed by the proposal unit and requests modifications as necessary. The confirmation unit can check the 3D model using methods such as visual confirmation, functional confirmation, and user testing. The confirmation unit can also check the proposed 3D model and request modifications as necessary. The creation unit creates the product based on the 3D model modified by the confirmation unit. The creation unit, for example, creates products using a 3D printer. The creation unit can also create products based on a modified 3D model. As a result, the matching system according to this embodiment can efficiently receive, analyze, propose, confirm, and create products based on the buyer's requests.

[0077] The reception desk receives requests from buyers. These requests include, but are not limited to, specific requests such as wanting an accessory stand of a particular shape. The reception desk can accept requests in various formats, such as text, voice, and image. Specifically, in text format, buyers can input detailed specifications and design requests for the product they want in written form. In voice format, buyers can verbally communicate their requests via a microphone, which are then converted to text using speech recognition technology. In image format, buyers can upload reference images or sketches to communicate visual requests. Furthermore, the reception desk has the functionality to centrally manage and store these requests in a database. This ensures that all buyer requests are recorded without fail and are smoothly carried over to subsequent processing. The reception desk also provides a user-friendly interface to facilitate smooth interaction with buyers, and is designed to make request input easy and intuitive. For example, the input form displays guidelines and samples to support buyers in entering their requests without confusion. This allows the reception department to efficiently receive diverse requests from customers and quickly move on to the next step.

[0078] The proposal department uses a generative AI to analyze requests received by the reception department and propose specific specifications. For example, the proposal department generates a 3D model based on the request and proposes it to the buyer. Specifically, the generative AI uses natural language processing technology to analyze text-based requests and understand the buyer's intent. For voice-based requests, it uses speech recognition technology to convert them to text and then performs the same analysis. For image-based requests, it uses image recognition technology to extract features from uploaded images and understand the buyer's desired design. Based on these analysis results, the generative AI proposes specific specifications such as the shape, size, and material of the accessory stand. For example, if a buyer requests a "wooden accessory stand that is 20 cm tall," the generative AI will generate a 3D model of a 20 cm tall wooden stand based on that request and propose it to the buyer. The generative AI can also refer to past data and trends to propose the optimal design and functions for the buyer. The proposal department provides an interface that allows buyers to visually confirm the generated 3D model, enabling them to intuitively understand the proposal. This allows the proposal department to quickly and accurately propose specific specifications based on the buyer's requests, thereby increasing buyer satisfaction.

[0079] The verification department reviews the 3D model proposed by the proposal department and requests revisions as needed. The verification department can review the 3D model using methods such as visual verification, functional verification, and user testing. Specifically, visual verification provides an interface that allows the buyer to rotate the 3D model 360 degrees and view it from all angles, checking for any problems with the design or shape. Functional verification simulates whether the dimensions, materials, and structure of the 3D model can withstand actual use and evaluates its strength and durability. User testing involves the buyer or a third party actually using the 3D model to collect feedback on usability and design. Through these verification processes, the buyer can request revisions as many times as needed until they are satisfied. When a revision request is received, the verification department works in cooperation with the proposal department to quickly make the revisions and repeat the verification process. Furthermore, the verification department provides a chat function and feedback form to facilitate smooth communication with the buyer and support the buyer in easily requesting revisions. This allows the verification unit to meticulously check and revise the proposed 3D model until it perfectly matches the buyer's requirements, preparing for the final product creation.

[0080] The production department creates products based on the 3D models corrected by the verification department. For example, the production department uses a 3D printer to create the products. Specifically, the data of the corrected 3D model is input into the 3D printer, and the product is created layer by layer using the specified material. 3D printers utilize high-precision layering technology to create products that faithfully reproduce even the smallest details. The production department configures the 3D printer and selects materials to create the products under optimal conditions. Furthermore, the production department implements inspection processes for quality control to ensure that the finished products are created according to the design. For example, dimensional measurements and visual inspections are performed to check for defects. In addition, the production department is flexible in creating customized products according to the buyer's requests. For example, if a specific color or finish is desired, appropriate painting or processing can be applied. This allows the production department to quickly create high-quality products based on the 3D models corrected by the verification department and deliver them to the buyer. Finally, the production department manages the entire process from packaging the finished products to shipping them to the buyer, ensuring that the buyer receives a product they are satisfied with.

[0081] The proposal department can generate 3D models based on requests. For example, the proposal department can propose specifications such as the shape, size, and material of an accessory stand based on requests. The proposal department can also generate 3D models based on requests using a generation AI. For example, the generation AI can generate a specific 3D model based on requests and propose it to the buyer. This makes it possible to make specific proposals by generating 3D models based on requests. Some or all of the above processes in the proposal department may be performed using a generation AI, or they may be performed without a generation AI. For example, the proposal department inputs requests as input to the generation AI, and the generation AI generates a 3D model.

[0082] The proposal department can use a generative AI to propose specific specifications based on the request. For example, the proposal department can propose specifications such as the shape, size, and material of an accessory stand based on the request. The proposal department can also use a generative AI to propose specific specifications based on the request. For example, the generative AI proposes specific specifications based on the request and presents them to the buyer. This makes it possible to propose specific specifications based on the request by using a generative AI. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without using a generative AI. For example, the proposal department inputs the request as input to the generative AI, and the generative AI proposes specific specifications.

[0083] The verification unit can review the proposed 3D model and request modifications as needed. The verification unit can review the 3D model using methods such as visual review, functional review, and user testing. The verification unit can also review the proposed 3D model and request modifications as needed. For example, the verification unit can visually review the proposed 3D model and request modifications as needed. The verification unit can also review the functionality of the proposed 3D model and request modifications as needed. This allows for the creation of highly accurate products by reviewing the proposed 3D model and requesting modifications as needed. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs the proposed 3D model into the AI, and the AI ​​determines the need for modifications.

[0084] The creation unit can create products based on the modified 3D model. The creation unit can, for example, create products using a 3D printer. The creation unit can also create products based on the modified 3D model. For example, the creation unit inputs the modified 3D model into a 3D printer to create the product. Alternatively, the creation unit can manually create products based on the modified 3D model. This makes it possible to create products that meet the buyer's needs by creating products based on the modified 3D model. Some or all of the above processes in the creation unit may be performed using AI, or not. For example, the creation unit inputs the modified 3D model into AI, and the AI ​​instructs the AI ​​on the steps to create the product.

[0085] The system includes a storage unit for saving the 3D models generated by the proposal unit. The storage unit can save the generated 3D models. For example, the storage unit can save the 3D models in digital format. The storage unit can also save the generated 3D models. For example, the storage unit can save the 3D models to cloud storage. The storage unit can also save the 3D models to local storage. By saving the generated 3D models, they can be referenced and modified later. Some or all of the above-described processes in the storage unit may be performed using AI or not. For example, the storage unit inputs the generated 3D models into the AI, and the AI ​​instructs the AI ​​on the saving procedure.

[0086] The verification unit includes a modification unit to which the modification request is made. The modification unit can modify the proposed 3D model. For example, the modification unit can modify the shape, size, and material of the 3D model. The modification unit can also modify the proposed 3D model. For example, the modification unit can modify the shape of the 3D model. The modification unit can also modify the size of the 3D model. Thus, by including a modification unit, the modification of the proposed 3D model can be performed efficiently. Some or all of the above processing in the modification unit may be performed using AI or not. For example, the modification unit inputs the proposed 3D model into the AI, and the AI ​​instructs the modification procedure.

[0087] The system includes a management unit that manages the progress of the product creation process by the creation unit. The management unit can manage the progress of product creation. For example, the management unit can monitor the progress of product creation. The management unit can also manage the progress of product creation. For example, the management unit can display the progress of product creation in real time. The management unit can also record the progress of product creation and refer to it later. This makes it easier to understand the progress of product creation by managing the progress. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit inputs the progress of product creation into the AI, and the AI ​​instructs the AI ​​on the steps for progress management.

[0088] The reception desk can estimate the user's emotions and adjust the timing of request acceptance based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick request entry. This improves user convenience by adjusting the timing of request acceptance according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 desk may be performed using AI or not. For example, the reception desk inputs user emotion data into the AI, and the AI ​​adjusts the acceptance timing.

[0089] The reception desk can analyze a user's past request history and select the most suitable reception method. For example, the reception desk can automatically display requests that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest requests that will be used during specific time periods based on the user's past request history. In this way, by analyzing past request history, the reception desk can provide the user with the most suitable reception method. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past request history into the AI, and the AI ​​can select the most suitable reception method.

[0090] The reception desk can filter requests based on the user's current projects and areas of interest. For example, the reception desk can prioritize requests related to projects the user is currently working on. The reception desk can also filter and display highly relevant requests based on the user's areas of interest. The reception desk can also filter requests based on areas the user has shown interest in in the past. This allows for priority reception of highly relevant requests by filtering requests based on the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's project information into the AI, and the AI ​​performs the filtering.

[0091] The reception desk can estimate the user's emotions and determine the priority of requests based on the estimated emotions. For example, if the user is nervous, the reception desk may prioritize high-priority requests. If the user is relaxed, the reception desk may also prioritize detailed requests. If the user is in a hurry, the reception desk may also prioritize requests that require a quick response. This allows important requests to be prioritized by determining the priority of requests according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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 desk may be performed using AI or not. For example, the reception desk inputs user emotion data into the AI, and the AI ​​determines the priority.

[0092] The reception desk can prioritize requests based on the user's geographical location when receiving requests. For example, the reception desk can prioritize requests related to the user's current location. The reception desk can also filter and display region-specific requests based on the user's geographical location. The reception desk can also prioritize requests related to places the user has visited in the past. This allows for addressing region-specific requests by prioritizing requests based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's geographical location into the AI, which then filters out the most relevant requests.

[0093] The reception desk can analyze a user's social media activity when receiving a request and accept relevant requests. For example, the reception desk can prioritize requests that the user has mentioned on social media. The reception desk can also filter and display requests related to areas of interest based on the user's social media activity. The reception desk can also accept relevant requests based on the activity of accounts that the user follows. This allows for requests based on the user's interests by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which then filters out relevant requests.

[0094] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, it can provide concise and to-the-point suggestions. If the user is excited, it can provide visually appealing suggestions. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. 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 processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit inputs user emotion data into a generative AI, and the generative AI adjusts the way it presents its suggestions.

[0095] The proposal department can adjust the level of detail of a proposal based on the importance of the request. For example, the proposal department will provide a detailed proposal for high-priority requests. For low-priority requests, it can provide a concise proposal. The proposal department can also adjust the level of detail of a proposal in stages according to the importance of the request. This allows for appropriate proposals by adjusting the level of detail according to the importance of the request. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department inputs the importance of the request into the generation AI, and the generation AI adjusts the level of detail of the proposal.

[0096] The proposal unit can apply different proposal algorithms depending on the category of the request when making a proposal. For example, the proposal unit can apply a design-focused proposal algorithm to accessory-related requests. The proposal unit can also apply a functionality-focused proposal algorithm to furniture-related requests. The proposal unit can also apply a safety-focused proposal algorithm to toy-related requests. By applying a proposal algorithm according to the category of the request, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit inputs the category of the request into the generative AI, and the generative AI applies a proposal algorithm.

[0097] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide visually stimulating suggestions. By adjusting the length of suggestions according to the user's emotions, it becomes possible to provide suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit inputs user emotion data into a generative AI, and the generative AI adjusts the length of the suggestions.

[0098] The proposal department can determine the priority of proposals based on when the requests were submitted. For example, the proposal department may prioritize recently submitted requests. The proposal department can also re-examine and submit proposals for older requests. The proposal department can also adjust the priority of proposals in stages based on the submission date. This allows for a quick response to the latest requests by determining the priority of proposals based on the submission date. Some or all of the above processes in the proposal department may be performed using a generation AI, or not. For example, the proposal department inputs the submission dates of requests into the generation AI, and the generation AI determines the priority of proposals.

[0099] The proposal department can adjust the order of proposals based on the relevance of the requests. For example, the proposal department will prioritize proposals for highly relevant requests. For less relevant requests, the proposal department can also provide concise proposals. The proposal department can also adjust the order of proposals in stages according to the relevance of the requests. This allows for prioritizing highly relevant proposals by adjusting the order of proposals based on the relevance of the requests. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department inputs the relevance of the requests into a generative AI, and the generative AI adjusts the order of proposals.

[0100] The verification unit can estimate the user's emotions and adjust the verification method based on the estimated emotions. For example, if the user is nervous, the verification unit can provide a simple and highly visible verification method. If the user is relaxed, the verification unit can also provide a verification method that includes detailed information. If the user is in a hurry, the verification unit can also provide a concise verification method. By adjusting the verification method according to the user's emotions, it becomes possible to provide verification that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not using AI. For example, the verification unit inputs the user's emotion data into the AI, and the AI ​​adjusts the verification method.

[0101] The verification unit can adjust the level of detail of the verification based on the importance of the proposed 3D model during the verification process. For example, the verification unit can perform a detailed verification for 3D models with high importance. The verification unit can also perform a simplified verification for 3D models with low importance. The verification unit can also adjust the level of detail of the verification in stages according to the importance of the 3D model. This allows for appropriate verification by adjusting the level of detail of the verification according to the importance of the 3D model. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit inputs the importance of the 3D model into the AI, and the AI ​​adjusts the level of detail of the verification.

[0102] The verification unit can apply different verification algorithms depending on the category of the proposed 3D model during verification. For example, the verification unit can apply a design-focused verification algorithm to accessory-related 3D models. It can also apply a functionality-focused verification algorithm to furniture-related 3D models. It can also apply a safety-focused verification algorithm to toy-related 3D models. This allows for more appropriate verification by applying a verification algorithm appropriate to the category of the 3D model. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit inputs the category of the 3D model into the AI, and the AI ​​applies a verification algorithm.

[0103] The verification unit can estimate the user's emotions and determine the priority of verifications based on the estimated emotions. For example, if the user is nervous, the verification unit may prioritize high-priority verifications. If the user is relaxed, the verification unit may also prioritize detailed verifications. If the user is in a hurry, the verification unit may also prioritize verifications that require a quick response. In this way, important verifications can be prioritized by determining the priority of verifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit inputs user emotion data into the AI, and the AI ​​determines the priority of verifications.

[0104] The verification unit can adjust the order of verification based on the submission date of the proposed 3D models. For example, the verification unit prioritizes the verification of recently submitted 3D models. The verification unit can also re-verify older 3D models and adjust their priority. The verification unit can also adjust the order of verification in stages based on the submission date. This allows for quick response to the latest 3D models by adjusting the order of verification based on the submission date of the 3D models. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs the submission date of the 3D models into the AI, and the AI ​​adjusts the order of verification.

[0105] The verification unit can adjust the order of verification based on the relevance of the proposed 3D models during the verification process. For example, the verification unit prioritizes verification of highly relevant 3D models. The verification unit can also perform a brief verification of less relevant 3D models. The verification unit can also adjust the order of verification in stages according to the relevance of the 3D models. This allows for priority verification of highly relevant 3D models by adjusting the order of verification based on the relevance of the 3D models. Some or all of the above processing in the verification unit may be performed using AI or not. For example, the verification unit inputs the relevance of the 3D models into the AI, and the AI ​​adjusts the order of verification.

[0106] The creation unit can estimate the user's emotions and adjust the creation method based on the estimated emotions. For example, if the user is relaxed, the creation unit can provide detailed creation instructions. If the user is in a hurry, the creation unit can also provide concise and quick creation instructions. If the user is excited, the creation unit can also provide visually appealing creation instructions. This ensures that the user receives appropriate creation instructions by adjusting the creation method according to their emotions. 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 creation unit may be performed using AI or not. For example, the creation unit inputs user emotion data into the AI, and the AI ​​adjusts the creation method.

[0107] The creation unit can adjust the level of detail during creation based on the importance of the modified 3D model. For example, the creation unit can provide detailed creation instructions for high-importance 3D models. For low-importance 3D models, the creation unit can also provide concise creation instructions. The creation unit can also adjust the level of detail in stages according to the importance of the 3D model. This allows for appropriate creation by adjusting the level of detail according to the importance of the 3D model. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit inputs the importance of the 3D model into the AI, and the AI ​​adjusts the level of detail.

[0108] The creation unit can apply different creation algorithms depending on the category of the modified 3D model during creation. For example, the creation unit can apply a design-focused creation algorithm to accessory-related 3D models. It can also apply a functionality-focused creation algorithm to furniture-related 3D models. It can also apply a safety-focused creation algorithm to toy-related 3D models. This allows for more appropriate creation by applying a creation algorithm appropriate to the category of the 3D model. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit inputs the category of the 3D model into the AI, and the AI ​​applies a creation algorithm.

[0109] The creation unit can estimate the user's emotions and determine the priority of creations based on the estimated emotions. For example, if the user is stressed, the creation unit may prioritize high-priority creations. If the user is relaxed, the creation unit may also prioritize detailed creations. If the user is in a hurry, the creation unit may also prioritize creations that require a quick response. This allows important creations to be prioritized by determining the priority of creations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit inputs user emotion data into the AI, and the AI ​​determines the priority of creations.

[0110] The creation unit can adjust the creation order based on the submission dates of revised 3D models during the creation process. For example, the creation unit can prioritize the creation of recently submitted 3D models. The creation unit can also re-examine older 3D models and adjust their priority. The creation unit can also adjust the creation order in stages based on the submission dates. This allows for quick response to the latest 3D models by adjusting the creation order based on the submission dates of the 3D models. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit inputs the submission dates of the 3D models into the AI, and the AI ​​adjusts the creation order.

[0111] The creation unit can adjust the creation order based on the relationships between modified 3D models during creation. For example, the creation unit can prioritize the creation of highly relevant 3D models. The creation unit can also perform a simplified creation for less relevant 3D models. The creation unit can also adjust the creation order stepwise according to the relationships between the 3D models. This allows for the prioritization of highly relevant 3D models by adjusting the creation order based on the relationships between the 3D models. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit inputs the relationships between the 3D models into the AI, and the AI ​​adjusts the creation order.

[0112] The storage unit can manage the versions of the 3D models being saved, allowing for comparison with past versions. For example, the storage unit can save each version of the 3D model with a timestamp, enabling comparison with past versions. The storage unit can also record the change history of the 3D model and visually display which parts have been changed. The storage unit can also automatically detect differences between versions of the 3D model and notify the user. This makes it easier to compare with past versions by managing the versions of the 3D model. Some or all of the above processes in the storage unit may be performed using AI or not. For example, the storage unit can input the 3D model version control into AI, and the AI ​​can perform the version control.

[0113] The storage unit can automatically generate metadata for 3D models during saving, improving searchability. For example, when saving a 3D model, the storage unit automatically generates metadata such as the model's shape, size, and materials. Based on the 3D model's metadata, the storage unit can also provide a search function, making it easier for users to find models. The storage unit can also tag the 3D model's metadata and group related models. This improves searchability by automatically generating the 3D model's metadata. Some or all of the above processes in the storage unit may be performed using AI or not. For example, the storage unit inputs the 3D model's metadata generation into the AI, and the AI ​​generates the metadata.

[0114] The storage unit can support multiple formats for saving 3D models, ensuring compatibility between different software. For example, the storage unit can save 3D models in multiple formats such as STL, OBJ, and PLY, ensuring compatibility between different software. The storage unit can also allow the user to select their preferred format when saving a 3D model. The storage unit can also provide a 3D model format conversion function, making conversion between different formats easy. This ensures compatibility between different software by supporting multiple formats. Some or all of the above processing in the storage unit may be performed using AI, or not. For example, the storage unit inputs the 3D model format conversion into AI, and the AI ​​performs the format conversion.

[0115] The saving unit can save related documents and images of the 3D model together with the 3D model when saving, making them easier to refer to. For example, the saving unit can save related design drawings and specifications together with the 3D model. The saving unit can also save rendered images of the model together with the 3D model, allowing for visual verification. The saving unit can also save related comments and feedback together with the 3D model, making them easier to refer to. This makes them easier to refer to by saving related documents and images together. Some or all of the above processes in the saving unit may be performed using AI or not. For example, the saving unit inputs related documents and images of the 3D model into the AI, and the AI ​​instructs the AI ​​on the saving procedure.

[0116] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. For example, if the user is relaxed, the correction unit can provide detailed correction steps. If the user is in a hurry, the correction unit can also provide concise and quick correction steps. If the user is excited, the correction unit can also provide visually appealing correction steps. This ensures that the user receives appropriate correction steps by adjusting the correction method according to their emotions. 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 correction unit may be performed using AI or not. For example, the correction unit inputs user emotion data into the AI, and the AI ​​adjusts the correction method.

[0117] The modification unit can adjust the level of detail of the modification based on the importance of the proposed 3D model during the modification process. For example, the modification unit can provide detailed modification procedures for highly important 3D models. For less important 3D models, the modification unit can also provide concise modification procedures. The modification unit can also adjust the level of detail of the modification in stages according to the importance of the 3D model. This allows for appropriate modification by adjusting the level of detail of the modification according to the importance of the 3D model. Some or all of the above processes in the modification unit may be performed using AI or not. For example, the modification unit inputs the importance of the 3D model into the AI, and the AI ​​adjusts the level of detail of the modification.

[0118] The editing unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is stressed, the editing unit may prioritize high-priority corrections. If the user is relaxed, the editing unit may also prioritize detailed corrections. If the user is in a hurry, the editing unit may also prioritize corrections that require immediate attention. This allows important corrections to be prioritized by determining the priority of corrections according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI or not. For example, the editing unit inputs user emotion data into the AI, and the AI ​​determines the priority of corrections.

[0119] The revision unit can adjust the order of revisions based on the submission dates of the proposed 3D models. For example, the revision unit prioritizes revisions to recently submitted 3D models. The revision unit can also re-examine older 3D models and adjust their priorities. The revision unit can also adjust the order of revisions in stages based on the submission dates. This allows for quick response to the latest 3D models by adjusting the order of revisions based on the submission dates of the 3D models. Some or all of the above processes in the revision unit may be performed using AI or not. For example, the revision unit inputs the submission dates of the 3D models into the AI, and the AI ​​adjusts the order of revisions.

[0120] The management department can estimate the user's emotions and adjust the progress management method based on the estimated emotions. For example, if the user is stressed, the management department can provide a simple and highly visible progress management method. If the user is relaxed, the management department can also provide a progress management method that includes detailed information. If the user is in a hurry, the management department can also provide a progress management method that gets straight to the point. In this way, by adjusting the progress management method according to the user's emotions, appropriate progress management is provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not using AI. For example, the management department inputs user emotion data into the AI, and the AI ​​adjusts the progress management method.

[0121] The management department can select the optimal management method by referring to past progress data when managing progress. For example, the management department can propose the optimal progress management method based on past progress data. The management department can also extract problems from past progress data and propose improvement measures. The management department can also analyze past progress data and select an efficient progress management method. In this way, the optimal progress management method can be selected by referring to past progress data. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs past progress data into the AI, and the AI ​​selects the optimal management method.

[0122] The management department can estimate the user's emotions and determine the priority of progress management based on the estimated emotions. For example, if the user is stressed, the management department can prioritize high-priority progress management. If the user is relaxed, the management department can also prioritize detailed progress management. If the user is in a hurry, the management department can also prioritize progress management that requires a quick response. This allows important progress management to be prioritized by determining the priority of progress management according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management department may be performed using AI or not. For example, the management department inputs user emotion data into the AI, and the AI ​​determines the priority of progress management.

[0123] The management department can select the optimal management method when managing progress, taking into account the user's device information. For example, if the user is using a smartphone, the management department can provide a progress management method that is adapted to the screen size. If the user is using a tablet, the management department can also provide a progress management method optimized for a larger screen. If the user is using a smartwatch, the management department can also provide a simple and highly visible progress management method. By selecting the optimal progress management method based on the user's device information, user-friendly progress management is provided. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs the user's device information into the AI, and the AI ​​selects the optimal management method.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is excited, the suggestion unit can suggest a visually appealing 3D model. If the user is relaxed, the suggestion unit can provide suggestions that include detailed specifications. Furthermore, if the user is in a hurry, the suggestion unit can provide concise and to-the-point suggestions. This enables suggestions that are tailored to the user's emotions, thereby improving user satisfaction. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using generative AI, or not. For example, the suggestion unit inputs user emotion data into the generative AI, and the generative AI adjusts the content of its suggestions.

[0126] The proposal department can analyze the user's past request history and make optimal suggestions. For example, it can suggest similar 3D models based on requests the user has frequently entered in the past. It can also customize suggestions by considering materials and designs the user has used in the past. Furthermore, it can predict requests that the user will use at a specific time of day based on their past request history and make suggestions accordingly. In this way, analyzing past request history makes it possible to make optimal suggestions for the user. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department inputs the user's past request history into a generative AI, and the generative AI makes the optimal suggestion.

[0127] The suggestion unit can make highly relevant suggestions based on the user's geographical location information. For example, it can suggest designs and materials related to the user's current location. It can also suggest designs and cultures specific to a particular region based on the user's geographical location information. Furthermore, it can suggest designs related to places the user has visited in the past. This enables highly relevant suggestions based on the user's geographical location information. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit inputs the user's geographical location information into a generative AI, and the generative AI makes highly relevant suggestions.

[0128] The suggestion department can analyze a user's social media activity and make relevant suggestions. For example, it can make suggestions based on designs and materials mentioned by the user on social media. It can also make suggestions related to areas of interest based on the user's social media activity. Furthermore, it can make relevant suggestions based on the activity of accounts the user follows. This makes it possible to make suggestions based on the user's interests by analyzing social media activity. Some or all of the above processing in the suggestion department may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion department inputs the user's social media data into a generative AI, and the generative AI makes relevant suggestions.

[0129] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can offer simple and visually appealing suggestions. If the user is relaxed, it can offer suggestions that include detailed specifications. Furthermore, if the user is in a hurry, the suggestion unit can offer suggestions that require immediate attention. This allows for suggestion timing that matches the user's emotions, improving user satisfaction. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit inputs user emotion data into the generative AI, which then adjusts the timing of suggestions.

[0130] The proposal department can adjust the level of detail in a proposal based on the importance of the request. For example, it can provide a detailed proposal for high-priority requests and a concise proposal for low-priority requests. Furthermore, it can adjust the level of detail in a proposal stepwise according to the importance of the request. This allows for proposals that are appropriate to the importance of the request, ensuring that suitable proposals are made. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department inputs the importance of the request into the generation AI, and the generation AI adjusts the level of detail in the proposal.

[0131] The proposal unit can apply different proposal algorithms depending on the category of the request. For example, a design-focused proposal algorithm can be applied to accessory-related requests. A functionality-focused proposal algorithm can be applied to furniture-related requests. Furthermore, a safety-focused proposal algorithm can be applied to toy-related requests. This allows for the application of a proposal algorithm appropriate to the category of the request, enabling more appropriate proposals. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit inputs the category of the request into the generative AI, and the generative AI applies a proposal algorithm.

[0132] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, it can provide a short, concise suggestion. If the user is relaxed, it can provide a longer suggestion with detailed explanations. Furthermore, if the user is excited, it can provide a visually stimulating suggestion. This adjusts the length of the suggestion according to the user's emotions, enabling the provision of appropriate suggestions for the user. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit inputs user emotion data into the generative AI, and the generative AI adjusts the length of the suggestion.

[0133] The proposal department can determine the priority of proposals based on when the requests were submitted. For example, it can prioritize recently submitted requests. It can also re-examine older requests and submit proposals again. Furthermore, it can adjust the priority of proposals in stages based on the submission date. This ensures that proposals are prioritized based on the submission date and that the latest requests are addressed quickly. Some or all of the above processes in the proposal department may be performed using a generation AI, or not. For example, the proposal department inputs the submission dates of requests into the generation AI, and the generation AI determines the priority of proposals.

[0134] The proposal department can adjust the order of proposals based on the relevance of the requests. For example, it can prioritize proposals for highly relevant requests. It can also provide concise proposals for less relevant requests. Furthermore, it can adjust the order of proposals in stages according to the relevance of the requests. This adjusts the order of proposals based on the relevance of the requests, prioritizing highly relevant proposals. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department inputs the relevance of the requests into a generation AI, and the generation AI adjusts the order of proposals.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The reception desk receives requests from buyers. These requests may include specific requests such as wanting an accessory stand of a particular shape. The reception desk can accept requests in various formats, including text, audio, and image. Step 2: The proposal department uses a generation AI to analyze the requests received by the reception department and propose specific specifications. The proposal department generates a 3D model based on the requests and proposes it to the buyer. For example, the generation AI proposes specifications such as the shape, size, and material of the accessory stand based on the requests. Step 3: The verification team reviews the 3D model proposed by the proposal team and requests modifications as needed. The verification team can review the 3D model using methods such as visual verification, functional verification, and user testing. Step 4: The creation unit creates the product based on the 3D model corrected by the verification unit. The creation unit uses a 3D printer to create the product.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, proposal unit, confirmation unit, creation unit, storage unit, modification unit, and management unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives the buyer's request using the reception device 38 of the smart device 14. The proposal unit analyzes the request using generated AI by the specific processing unit 290 of the data processing unit 12 and proposes specific specifications. The confirmation unit checks the proposed 3D model using the display 40A of the smart device 14 and requests modifications as necessary. The creation unit controls the 3D printer using the control unit 46A of the smart device 14 to create the product. The storage unit saves the generated 3D model in the database 24 of the data processing unit 12. The modification unit modifies the 3D model using the specific processing unit 290 of the data processing unit 12. The management unit manages the progress of product creation using the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, proposal unit, confirmation unit, creation unit, storage unit, modification unit, and management unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives the buyer's request using the microphone 238 of the smart glasses 214. The proposal unit analyzes the request using generated AI by the specific processing unit 290 of the data processing unit 12 and proposes specific specifications. The confirmation unit checks the proposed 3D model using the display of the smart glasses 214 and requests modifications as necessary. The creation unit controls the 3D printer using the control unit 46A of the smart glasses 214 to create the product. The storage unit saves the generated 3D model in the database 24 of the data processing unit 12. The modification unit modifies the 3D model using the specific processing unit 290 of the data processing unit 12. The management unit manages the progress of product creation using the control unit 46A of the smart glasses 214. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, proposal unit, confirmation unit, creation unit, storage unit, modification unit, and management unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives the buyer's request using the microphone 238 of the headset terminal 314. The proposal unit analyzes the request using generated AI by the specific processing unit 290 of the data processing unit 12 and proposes specific specifications. The confirmation unit checks the proposed 3D model using the display 343 of the headset terminal 314 and requests modifications as necessary. The creation unit controls the 3D printer using the control unit 46A of the headset terminal 314 to create the product. The storage unit saves the generated 3D model in the database 24 of the data processing unit 12. The modification unit modifies the 3D model using the specific processing unit 290 of the data processing unit 12. The management unit manages the progress of product creation using the control unit 46A of the headset terminal 314. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the reception unit, proposal unit, confirmation unit, creation unit, storage unit, modification unit, and management unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives the buyer's request using the microphone 238 of the robot 414. The proposal unit analyzes the request using generated AI by the specific processing unit 290 of the data processing unit 12 and proposes specific specifications. The confirmation unit checks the proposed 3D model using the display of the robot 414 and requests modifications as necessary. The creation unit controls the 3D printer using the control unit 46A of the robot 414 to create the product. The storage unit saves the generated 3D model in the database 24 of the data processing unit 12. The modification unit modifies the 3D model using the specific processing unit 290 of the data processing unit 12. The management unit manages the progress of product creation using the control unit 46A of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A reception desk that handles customer requests, The proposal department analyzes the requests received by the aforementioned reception department and proposes specific specifications. The verification unit reviews the 3D model proposed by the aforementioned proposal unit and requests modifications. The system includes a creation unit that creates a product based on the 3D model corrected by the verification unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Generate a 3D model based on your requirements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, The AI ​​generates specific specifications based on the requirements. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned verification unit is Review the proposed 3D model and request modifications as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Create products based on the revised 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a storage unit for saving the 3D models generated by the proposal unit. The system described in Appendix 1, characterized by the features described herein. (Note 7) The verification unit includes a correction unit to which corrections are requested. The system described in Appendix 1, characterized by the features described herein. (Note 8) The production department has a management department that manages the progress of the product creation process. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of requests based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requests, 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 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of requests to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When receiving requests, we prioritize requests that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving a request, the system analyzes the user's social media activity and accepts relevant requests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the request. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the request. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When submitting proposals, we will prioritize them based on when the requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the requests. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned verification unit is We estimate the user's emotions and adjust the confirmation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned verification unit is During the review process, adjust the level of detail based on the importance of the proposed 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned verification unit is During verification, different verification algorithms are applied depending on the category of the proposed 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned verification unit is The system estimates the user's emotions and determines the priority of confirmations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned verification unit is During the review process, the order of review will be adjusted based on the submission timing of the proposed 3D models. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is During the review process, the order of review will be adjusted based on the relevance of the proposed 3D models. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creation unit, We estimate the user's emotions and adjust the creation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned creation unit, During creation, adjust the level of detail based on the importance of the modified 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned creation unit, During creation, different creation algorithms are applied depending on the category of the modified 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned creation unit, It estimates user sentiment and determines creation priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned creation unit, During creation, adjust the creation order based on when the revised 3D model is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned creation unit, During creation, adjust the creation order based on the relevance of the modified 3D models. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned storage unit is Version control is implemented for saved 3D models, allowing comparison with previous versions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned storage unit is When saving, metadata for 3D models is automatically generated, improving searchability. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned storage unit is Supports multiple formats for saving 3D models, ensuring compatibility across different software. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned storage unit is When saving, related documents and images of the 3D model are saved together for easier reference. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned modification section is, It estimates the user's emotions and adjusts the correction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned modification section is, When making revisions, adjust the level of detail of the revisions based on the importance of the proposed 3D model. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned modification section is, It estimates user sentiment and determines the priority of modifications based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned modification section is, During the revision process, the order of revisions will be adjusted based on the submission timing of the proposed 3D models. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned management department, We estimate the user's emotions and adjust the progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned management department, When managing progress, refer to past progress data to select the most suitable management method. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned management department, Estimate user emotions and prioritize progress management based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned management department, When managing progress, select the optimal management method while considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0209] 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 handles customer requests, The proposal department analyzes the requests received by the aforementioned reception department and proposes specific specifications. The verification unit reviews the 3D model proposed by the aforementioned proposal unit and requests modifications. The system includes a creation unit that creates a product based on the 3D model corrected by the verification unit. A system characterized by the following features.

2. The aforementioned proposal section is, Generate a 3D model based on your requirements. The system according to feature 1.

3. The aforementioned proposal section is, The AI ​​generates specific specifications based on the requests. The system according to feature 1.

4. The aforementioned verification unit is Review the proposed 3D model and request modifications as needed. The system according to feature 1.

5. The aforementioned creation unit, Create products based on the revised 3D model. The system according to feature 1.

6. The proposed unit includes a storage unit for storing the 3D model generated by the proposed unit. The system according to feature 1.

7. The aforementioned verification unit includes a correction unit that requests corrections. The system according to feature 1.

8. The aforementioned creation unit includes a management unit that manages the progress of product creation. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of requests based on those emotions. The system according to feature 1.

10. The aforementioned reception unit is Analyze the user's past request history and select the most suitable method of receiving requests. The system according to feature 1.

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