system

Abstract

The system according to this embodiment aims to easily turn a user's idea into a concrete product. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a proposal unit, and a delivery unit. The reception unit receives the user's idea as input. The generation unit analyzes the idea input by the reception unit and generates design data for a 3D printer. The proposal unit proposes the optimal manufacturing facility based on the design data generated by the generation unit. The delivery unit delivers the product manufactured at the manufacturing facility proposed by the proposal 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 prior art, there is a problem that the process of turning a user's idea into a specific product is complicated and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to easily turn a user's idea into a specific product.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a delivery unit. The reception unit receives the user's idea as input. The generation unit analyzes the idea input by the reception unit and generates design data for a 3D printer. The proposal unit proposes the optimal manufacturing facility based on the design data generated by the generation unit. The delivery unit delivers the product manufactured at the manufacturing facility proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can easily turn a user's idea into a concrete 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 也, are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The AI ​​data generation and creation service for 3D printers according to an embodiment of the present invention is a service that converts a user's idea into design data for 3D printers and provides comprehensive support from manufacturing to delivery. The AI ​​data generation and creation service for 3D printers utilizes a generating AI to convert a user's idea into design data for 3D printers and provides comprehensive support from manufacturing to delivery. This service can meet a variety of needs, such as children's drawings, self-help tools for disability welfare, furniture repair, metal parts, and artwork. For example, a user inputs an idea in the form of text, illustrations, or photographs. For instance, a child's drawing, an idea for a self-help tool for disability welfare, or a photograph of parts needed for furniture repair. This information is input into the generating AI. Next, the generating AI analyzes the input information and generates design data for 3D printers. The generating AI understands the intent from the input text, illustrations, and photographs and proposes appropriate size, materials, and design drawings. For example, it can convert a child's drawing into a 3D object to generate data for a 3D printer, or generate design data for parts needed for furniture repair. Based on the generated design data, the AI ​​proposes the most suitable manufacturer. The AI-generated system selects the most suitable manufacturer from a registered list based on conditions such as material, size, and quantity, and proposes it to the user. For example, it might suggest a manufacturer specializing in metal parts or one suitable for creating artwork. Furthermore, it handles everything from production to delivery. Once the user selects a suggested manufacturer, production begins, and the finished product is delivered to the user. This allows users to realize their ideas without needing special skills or knowledge. This system can meet a variety of needs, including children's drawings, self-help tools for people with disabilities, furniture repair, metal parts, and artwork. For example, a child's drawing can be transformed into a three-dimensional keepsake, or a self-help tool for people with disabilities can be customized to individual needs. It's also possible to easily create parts needed for furniture repair or commission the production of metal parts. Moreover, by commissioning the creation of artwork, users can obtain original pieces.This service supports users with specific needs in realizing their ideas using 3D printers, and is expected to improve their quality of life. For example, it will be a useful service for people who have difficulties in their daily lives and need self-help tools, parents who want to fulfill their children's wishes, and people who want to reduce costs by repairing household furniture and miscellaneous goods. As a result, the AI ​​data generation and creation service for 3D printers can convert users' ideas into design data for 3D printers and handle everything from manufacturing to delivery.

[0029] The AI ​​data generation and creation service for 3D printers according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a delivery unit. The reception unit receives the user's ideas as input. The user's ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception unit can receive ideas in the form of, for example, text, illustrations, and photographs. The generation unit uses a generation AI to analyze the ideas input by the reception unit and generate design data for a 3D printer. The generation unit, for example, interprets the intent from the input text, illustrations, and photographs and proposes appropriate size, materials, and design drawings. The generation unit can, for example, convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can also generate design data for parts necessary for furniture repair. The generation unit can, for example, generate data to create a 3D model of a child's drawing to preserve as a memento. The generation unit can also generate design data for parts necessary for furniture repair. The proposal unit proposes the optimal manufacturing facility based on the design data generated by the generation unit. The proposal department selects the most suitable manufacturer from among the registered manufacturers based on conditions such as material, size, and quantity. The proposal department proposes, for example, a manufacturer that excels at manufacturing metal parts or a manufacturer suitable for producing works of art. The delivery department delivers the products manufactured by the manufacturers proposed by the proposal department. The delivery department delivers, for example, the products manufactured by the manufacturers to the users. The delivery department can deliver, for example, the products manufactured by the manufacturers quickly. As a result, the AI ​​data generation and creation service for 3D printers according to this embodiment can convert the user's ideas into design data for 3D printers and handle everything from manufacturing to delivery in a consistent manner.

[0030] The reception desk receives user ideas. User ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. Ideas can be entered in various formats, such as text, illustrations, and photographs. Specifically, users can enter their ideas through a dedicated web interface or mobile app. The web interface allows users to enter ideas into text boxes or upload image files. The mobile app allows users to directly take hand-drawn illustrations or photographs using the camera function and upload them. Furthermore, users can use the voice input function to verbally describe their ideas, which are then converted into text using speech recognition technology. This allows users to easily enter ideas in various formats, and the reception desk centrally manages this data. The reception desk automatically categorizes the entered ideas and assigns appropriate tags to ensure smooth subsequent processing. For example, business ideas are tagged "Business," technical ideas "Technology," and creative ideas "Creative." This allows the reception desk to meet the diverse needs of users and efficiently collect and manage ideas.

[0031] The generation unit uses generation AI to analyze ideas entered by the reception unit and generate design data for 3D printers. For example, the generation unit extracts intent from entered text, illustrations, and photographs, and proposes appropriate size, materials, and design drawings. Specifically, the generation AI uses natural language processing technology to analyze text data and understand the user's intent. For example, if the text "I want to make a chair with a new design" is entered, the generation AI extracts the keyword "chair" and, considering the requirement of "new design," generates a unique design. It also uses image recognition technology to analyze illustrations and photographs and extract shapes and features. For example, when creating a 3D model from a child's drawing, the generation AI recognizes the main shapes and colors in the drawing and generates a 3D model based on them. Furthermore, the generation AI proposes appropriate materials and sizes according to the user's requirements. For example, when generating design data for parts needed to repair furniture, it considers the materials and dimensions of the original furniture to create the optimal design. Through these processes, the generation unit can quickly generate high-quality 3D printer data that meets the diverse needs of users.

[0032] The Proposal Department proposes the most suitable manufacturing plant based on the design data generated by the Production Department. For example, the Proposal Department selects the most suitable manufacturing plant from among the registered plants based on conditions such as material, size, and quantity. Specifically, the Proposal Department refers to the information of manufacturing plants registered in the database and selects the most suitable plant considering each plant's area of ​​expertise, equipment, and past performance. For example, it may propose a plant that excels at manufacturing metal parts or a plant suitable for producing works of art. The Proposal Department compares multiple plants according to the user's requirements and presents the best option. For example, if mass production is required, it will propose a large-scale plant, and if small-batch production or custom-made products are required, it will propose a small-scale specialized plant. The Proposal Department can also refer to plant evaluations and reviews to select a highly reliable plant. As a result, the Proposal Department can quickly and accurately propose the most suitable plant to the user, achieving increased efficiency in the manufacturing process and improved quality.

[0033] The Delivery Department delivers products manufactured at factories proposed by the Proposal Department. For example, the Delivery Department delivers products manufactured at factories to users. Specifically, the Delivery Department receives products from factories and has established a logistics network to deliver them quickly to the user's specified address. The Delivery Department selects the optimal delivery method considering the size and weight of the product, the distance to the delivery destination, etc. For example, small products are delivered by courier, while large products are delivered by specialized delivery companies. In addition, the Delivery Department uses appropriate packaging and protective materials to ensure the safe delivery of products so that they arrive at the user's location without damage. Furthermore, the Delivery Department has implemented a system to track the delivery status in real time, allowing users to always check the location of their products. This enables the Delivery Department to achieve fast and safe delivery of products and improve user satisfaction.

[0034] The generation unit can interpret the intent from the input text, illustrations, and photographs and propose appropriate size, material, and design plans. For example, the generation unit can convert a child's drawing into a 3D object to generate data for a 3D printer. The generation unit can also generate design data for parts needed to repair furniture. For example, the generation unit can generate data to create a 3D model of a child's drawing to keep as a memento. The generation unit can also generate design data for parts needed to repair furniture. This allows the generation unit to generate appropriate design data based on user input. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the input text, illustrations, and photographs into a generation AI, which can interpret the intent and propose appropriate size, material, and design plans.

[0035] The proposal unit can select the most suitable manufacturer from among the registered manufacturers based on conditions such as material, size, and quantity. For example, the proposal unit can select the most suitable manufacturer from among the registered manufacturers based on conditions such as material, size, and quantity. The proposal unit can propose, for example, a manufacturer that excels at manufacturing metal parts or a manufacturer suitable for producing works of art. For example, the proposal unit can select a manufacturer that excels at manufacturing metal parts and propose it to the user. The proposal unit can also select a manufacturer suitable for producing works of art and propose it to the user. In this way, the proposal unit can select the most suitable manufacturer and propose it to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information on registered manufacturers into AI, and the AI ​​can select the most suitable manufacturer based on conditions such as material, size, and quantity.

[0036] The delivery department can deliver products manufactured at the factory to users. The delivery department can, for example, deliver products manufactured at the factory to users. The delivery department can, for example, deliver products manufactured at the factory quickly. The delivery department can, for example, deliver products manufactured at the factory quickly. The delivery department can also deliver products manufactured at the factory safely. In this way, the delivery department can deliver manufactured products to users. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input delivery information of products manufactured at the factory into AI, and the AI ​​can select the optimal delivery method.

[0037] The generation unit can convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can, for example, convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can, for example, generate data to create a 3D model of a child's drawing to keep as a memento. The generation unit can also convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can, for example, generate data to create a 3D model of a child's drawing to keep as a memento. The generation unit can also convert a child's drawing into a 3D object to generate data for a 3D printer. Thus, the generation unit can create a 3D model of a child's drawing and generate data for a 3D printer. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input a child's drawing into a generation AI, and the generation AI can convert it into a 3D object and generate data for a 3D printer.

[0038] The generation unit can generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can also generate design data for parts necessary for furniture repair. The generation unit can also generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can also generate design data for parts necessary for furniture repair. In this way, the generation unit can generate design data for parts necessary for furniture repair. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input a photograph of the parts necessary for furniture repair into the generation AI, and the generation AI can generate design data.

[0039] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the format of ideas (text, illustrations, photos) that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the format of ideas to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0040] The reception desk can provide input guides based on the user's current projects and areas of interest when inputting ideas. For example, when a user inputs ideas related to an ongoing project, the reception desk can provide relevant guidelines and samples. The reception desk can also suggest methods for inputting relevant ideas based on the user's areas of interest. For example, if a user is interested in a particular field, the reception desk can provide input guides specific to that field. This allows the reception desk to improve input efficiency by providing input guides tailored to the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's project information into AI, and the AI ​​can provide relevant input guides.

[0041] The input unit can provide highly relevant input guidance when an idea is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the input unit can provide input guidance for ideas related to that region. The input unit can also suggest region-specific materials and designs based on the user's location. For example, if the user is traveling, the input unit can provide input guidance for ideas related to their travel destination. This allows the input unit to improve input accuracy by providing highly relevant input guidance based on the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location into AI, which can then provide highly relevant input guidance.

[0042] The reception desk can analyze the user's social media activity when an idea is entered and suggest relevant ideas. For example, the reception desk can provide relevant input guides based on ideas the user has shared on social media. For example, the reception desk can also suggest ideas related to areas of interest based on the user's social media activity. For example, the reception desk can analyze the content of posts from accounts the user follows on social media and suggest relevant ideas. In this way, the reception desk can provide ideas tailored to the user's interests by suggesting relevant ideas based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can suggest relevant ideas.

[0043] The generation unit can adjust the level of detail of the design data based on the importance of the idea during generation. For example, the generation unit generates detailed design data for important ideas. For example, the generation unit can also generate design data with a standard level of detail for general ideas. For example, the generation unit generates concise design data for simple ideas. In this way, the generation unit can generate appropriate design data by adjusting the level of detail of the design data based on the importance of the idea. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the idea into the generation AI, and the generation AI can adjust the level of detail of the design data.

[0044] The generation unit can apply different generation algorithms depending on the category of the idea during generation. For example, in the case of a work of art, the generation unit can apply a visually appealing algorithm. For example, in the case of a metal part, the generation unit can also apply a precise algorithm. For example, in the case of furniture repair, the generation unit can apply an algorithm that prioritizes durability. In this way, the generation unit can generate appropriate design data by applying a generation algorithm appropriate to the category of the idea. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the idea into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0045] The generation unit can determine the priority of design data based on the timing of idea submission during generation. For example, the generation unit will prioritize the generation of design data for urgent ideas. For example, the generation unit can also generate design data with a standard priority for typical ideas. For example, the generation unit will postpone the generation of design data for long-term ideas. This allows the generation unit to generate design data in the appropriate order by determining the priority of design data based on the timing of idea submission. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the timing of idea submission into the generation AI, and the generation AI can determine the priority of design data.

[0046] The generation unit can adjust the order of design data based on the relevance of ideas during generation. For example, the generation unit will prioritize generating design data for highly relevant ideas. For example, the generation unit can also postpone generating design data for less relevant ideas. For example, if multiple ideas are related, the generation unit will generate the design data in order of relevance. In this way, the generation unit can generate design data in order of relevance by adjusting the order of design data based on the relevance of ideas. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of ideas into a generation AI, and the generation AI can adjust the order of the design data.

[0047] The proposal department can adjust the level of detail of a proposal based on the importance of the manufacturing plant. For example, the proposal department will provide a detailed proposal for a critical manufacturing plant. For example, the proposal department may provide a proposal with a standard level of detail for a general manufacturing plant. For example, the proposal department will provide a concise proposal for a simple manufacturing plant. In this way, the proposal department can provide appropriate proposals by adjusting the level of detail based on the importance of the manufacturing plant. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the importance of the manufacturing plant into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0048] The proposal unit can apply different proposal algorithms depending on the manufacturer's category when making a proposal. For example, if it is an art factory, the proposal unit will make visually appealing proposals. If it is a metal parts factory, the proposal unit can also make precise proposals. If it is a furniture repair factory, the proposal unit will make proposals that prioritize durability. In this way, the proposal unit can make appropriate proposals by applying a proposal algorithm appropriate to the manufacturer's category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the manufacturer's category into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0049] The proposal department can determine the priority of proposals based on the submission timing of each factory. For example, the proposal department may prioritize proposals from factories with urgent needs. For example, the proposal department may also submit proposals with standard priority for factories with normal needs. For example, the proposal department may postpone proposals from factories with long-term needs. This allows the proposal department to submit proposals in the appropriate order by determining the priority of proposals based on the submission timing of each factory. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of each factory into the AI, which can then determine the priority of the proposals.

[0050] The proposal unit can adjust the order of proposals based on the relationships between the manufacturing plants. For example, the proposal unit will prioritize proposals to highly relevant plants. For example, the proposal unit may postpone proposals to less relevant plants. For example, if multiple plants are related, the proposal unit will propose to them in order of relevance. In this way, the proposal unit can make proposals in order of relevance by adjusting the order of proposals based on the relationships between the plants. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relationships between plants into the AI, and the AI ​​can adjust the order of proposals.

[0051] The delivery department can analyze the user's past delivery history to select the optimal delivery method during delivery. For example, the delivery department can propose the optimal delivery method based on the delivery method the user has used in the past. For example, the delivery department can also propose a fast delivery method based on the user's past delivery history. For example, the delivery department can analyze the user's past delivery history and propose the most efficient delivery method. In this way, the delivery department can perform efficient deliveries by selecting the optimal delivery method based on the user's past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's past delivery history into AI, and the AI ​​can propose the optimal delivery method.

[0052] The delivery department can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is at home, the delivery department will select regular courier service. If the user is out, the delivery department can also use convenience store pickup or a delivery locker. If the user is traveling, the delivery department will deliver to a hotel or designated location at the travel destination. In this way, the delivery department can provide appropriate delivery by customizing the delivery method according to the user's living situation. Some or all of the above processing in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input the user's living situation into the AI, and the AI ​​can suggest the most suitable delivery method.

[0053] The delivery department can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in an urban area, the delivery department may select an expedited courier service. If the user is in a suburban area, the delivery department may select a region-specific delivery service. If the user is overseas, the delivery department may select an international delivery service. This allows the delivery department to perform efficient deliveries by selecting the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's geographical location information into AI, which can then suggest the optimal delivery method.

[0054] The delivery department can analyze the user's social media activity during delivery and suggest a delivery method. For example, the delivery department can suggest the optimal delivery method based on location information shared by the user on social media. For example, the delivery department can suggest delivery options that the user is interested in based on their social media activity. For example, the delivery department can suggest the optimal delivery method based on the delivery services the user follows on social media. This allows the delivery department to perform efficient deliveries by suggesting the optimal delivery method based on the user's social media activity. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input the user's social media activity into AI, which can then suggest the optimal delivery method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past input history and suggest the most suitable input method. For example, it can automatically display as suggestions the format of ideas (text, illustrations, photos) that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the format of ideas that the user will use at a specific time of day based on their past input history. In this way, the reception desk can suggest the most suitable input method based on the user's past input history.

[0057] The proposal department can adjust the level of detail in a proposal based on the importance of the manufacturing plant. For example, a detailed proposal can be made for a critical manufacturing plant. For a general manufacturing plant, a proposal with a standard level of detail is possible. Furthermore, for a simple manufacturing plant, a concise proposal can be made. In this way, the proposal department can make appropriate proposals by adjusting the level of detail based on the importance of the manufacturing plant.

[0058] The delivery department can analyze a user's past delivery history to select the optimal delivery method. For example, it can suggest the most suitable delivery method based on the delivery method the user has used in the past. It can also suggest a faster delivery method based on the user's past delivery history. Furthermore, it can analyze the user's past delivery history and suggest the most efficient delivery method. As a result, the delivery department can perform efficient deliveries by selecting the optimal delivery method based on the user's past delivery history.

[0059] The generation unit can adjust the level of detail of the design data based on the importance of the idea during generation. For example, for important ideas, it can generate detailed design data. For general ideas, it can also generate design data with a standard level of detail. Furthermore, for simple ideas, it can generate concise design data. In this way, the generation unit can generate appropriate design data by adjusting the level of detail of the design data based on the importance of the idea.

[0060] The proposal department can apply different proposal algorithms depending on the category of the manufacturing facility. For example, an art factory can make visually appealing proposals. A metal parts factory can make precise proposals. Furthermore, a furniture repair factory can make proposals that prioritize durability. In this way, the proposal department can make appropriate proposals by applying proposal algorithms tailored to the category of the manufacturing facility.

[0061] The delivery department can select the most suitable delivery method by considering the user's geographical location during delivery. For example, if the user is in an urban area, it can select a fast courier service. If the user is in a suburban area, it can select a delivery service specific to that region. Furthermore, if the user is overseas, it can select an international delivery service. In this way, the delivery department can perform efficient deliveries by selecting the most suitable delivery method based on the user's geographical location.

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

[0063] Step 1: The reception desk receives the user's idea. User ideas include business ideas, technical ideas, and creative ideas. Ideas can be entered into the reception desk in various formats such as text, illustrations, and photographs. Step 2: The generation unit uses generation AI to analyze the ideas entered by the reception unit and generate design data for a 3D printer. The generation unit understands the intent from the entered text, illustrations, and photos, and proposes appropriate size, materials, and design drawings. For example, it can convert a child's drawing into a 3D model to generate data for a 3D printer, or generate design data for parts needed to repair furniture. Step 3: The Proposal Department proposes the most suitable manufacturing company based on the design data generated by the Generation Department. The Proposal Department selects the most suitable manufacturing company from the registered companies based on conditions such as material, size, and quantity. For example, it may propose a manufacturing company that specializes in the production of metal parts or a manufacturing company suitable for the production of art pieces. Step 4: The delivery department delivers the products manufactured at the factory proposed by the proposal department. The delivery department can quickly deliver the products manufactured at the factory to the user.

[0064] (Example of form 2) The AI ​​data generation and creation service for 3D printers according to an embodiment of the present invention is a service that converts a user's idea into design data for 3D printers and provides comprehensive support from manufacturing to delivery. The AI ​​data generation and creation service for 3D printers utilizes a generating AI to convert a user's idea into design data for 3D printers and provides comprehensive support from manufacturing to delivery. This service can meet a variety of needs, such as children's drawings, self-help tools for disability welfare, furniture repair, metal parts, and artwork. For example, a user inputs an idea in the form of text, illustrations, or photographs. For instance, a child's drawing, an idea for a self-help tool for disability welfare, or a photograph of parts needed for furniture repair. This information is input into the generating AI. Next, the generating AI analyzes the input information and generates design data for 3D printers. The generating AI understands the intent from the input text, illustrations, and photographs and proposes appropriate size, materials, and design drawings. For example, it can convert a child's drawing into a 3D object to generate data for a 3D printer, or generate design data for parts needed for furniture repair. Based on the generated design data, the AI ​​proposes the most suitable manufacturer. The AI-generated system selects the most suitable manufacturer from a registered list based on conditions such as material, size, and quantity, and proposes it to the user. For example, it might suggest a manufacturer specializing in metal parts or one suitable for creating artwork. Furthermore, it handles everything from production to delivery. Once the user selects a suggested manufacturer, production begins, and the finished product is delivered to the user. This allows users to realize their ideas without needing special skills or knowledge. This system can meet a variety of needs, including children's drawings, self-help tools for people with disabilities, furniture repair, metal parts, and artwork. For example, a child's drawing can be transformed into a three-dimensional keepsake, or a self-help tool for people with disabilities can be customized to individual needs. It's also possible to easily create parts needed for furniture repair or commission the production of metal parts. Moreover, by commissioning the creation of artwork, users can obtain original pieces.This service supports users with specific needs in realizing their ideas using 3D printers, and is expected to improve their quality of life. For example, it will be a useful service for people who have difficulties in their daily lives and need self-help tools, parents who want to fulfill their children's wishes, and people who want to reduce costs by repairing household furniture and miscellaneous goods. As a result, the AI ​​data generation and creation service for 3D printers can convert users' ideas into design data for 3D printers and handle everything from manufacturing to delivery.

[0065] The AI ​​data generation and creation service for 3D printers according to this embodiment comprises a reception unit, a generation unit, a proposal unit, and a delivery unit. The reception unit receives the user's ideas as input. The user's ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. The reception unit can receive ideas in the form of, for example, text, illustrations, and photographs. The generation unit uses a generation AI to analyze the ideas input by the reception unit and generate design data for a 3D printer. The generation unit, for example, interprets the intent from the input text, illustrations, and photographs and proposes appropriate size, materials, and design drawings. The generation unit can, for example, convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can also generate design data for parts necessary for furniture repair. The generation unit can, for example, generate data to create a 3D model of a child's drawing to preserve as a memento. The generation unit can also generate design data for parts necessary for furniture repair. The proposal unit proposes the optimal manufacturing facility based on the design data generated by the generation unit. The proposal department selects the most suitable manufacturer from among the registered manufacturers based on conditions such as material, size, and quantity. The proposal department proposes, for example, a manufacturer that excels at manufacturing metal parts or a manufacturer suitable for producing works of art. The delivery department delivers the products manufactured by the manufacturers proposed by the proposal department. The delivery department delivers, for example, the products manufactured by the manufacturers to the users. The delivery department can deliver, for example, the products manufactured by the manufacturers quickly. As a result, the AI ​​data generation and creation service for 3D printers according to this embodiment can convert the user's ideas into design data for 3D printers and handle everything from manufacturing to delivery in a consistent manner.

[0066] The reception desk receives user ideas. User ideas include, but are not limited to, business ideas, technical ideas, and creative ideas. Ideas can be entered in various formats, such as text, illustrations, and photographs. Specifically, users can enter their ideas through a dedicated web interface or mobile app. The web interface allows users to enter ideas into text boxes or upload image files. The mobile app allows users to directly take hand-drawn illustrations or photographs using the camera function and upload them. Furthermore, users can use the voice input function to verbally describe their ideas, which are then converted into text using speech recognition technology. This allows users to easily enter ideas in various formats, and the reception desk centrally manages this data. The reception desk automatically categorizes the entered ideas and assigns appropriate tags to ensure smooth subsequent processing. For example, business ideas are tagged "Business," technical ideas "Technology," and creative ideas "Creative." This allows the reception desk to meet the diverse needs of users and efficiently collect and manage ideas.

[0067] The generation unit uses generation AI to analyze ideas entered by the reception unit and generate design data for 3D printers. For example, the generation unit extracts intent from entered text, illustrations, and photographs, and proposes appropriate size, materials, and design drawings. Specifically, the generation AI uses natural language processing technology to analyze text data and understand the user's intent. For example, if the text "I want to make a chair with a new design" is entered, the generation AI extracts the keyword "chair" and, considering the requirement of "new design," generates a unique design. It also uses image recognition technology to analyze illustrations and photographs and extract shapes and features. For example, when creating a 3D model from a child's drawing, the generation AI recognizes the main shapes and colors in the drawing and generates a 3D model based on them. Furthermore, the generation AI proposes appropriate materials and sizes according to the user's requirements. For example, when generating design data for parts needed to repair furniture, it considers the materials and dimensions of the original furniture to create the optimal design. Through these processes, the generation unit can quickly generate high-quality 3D printer data that meets the diverse needs of users.

[0068] The Proposal Department proposes the most suitable manufacturing plant based on the design data generated by the Production Department. For example, the Proposal Department selects the most suitable manufacturing plant from among the registered plants based on conditions such as material, size, and quantity. Specifically, the Proposal Department refers to the information of manufacturing plants registered in the database and selects the most suitable plant considering each plant's area of ​​expertise, equipment, and past performance. For example, it may propose a plant that excels at manufacturing metal parts or a plant suitable for producing works of art. The Proposal Department compares multiple plants according to the user's requirements and presents the best option. For example, if mass production is required, it will propose a large-scale plant, and if small-batch production or custom-made products are required, it will propose a small-scale specialized plant. The Proposal Department can also refer to plant evaluations and reviews to select a highly reliable plant. As a result, the Proposal Department can quickly and accurately propose the most suitable plant to the user, achieving increased efficiency in the manufacturing process and improved quality.

[0069] The Delivery Department delivers products manufactured at factories proposed by the Proposal Department. For example, the Delivery Department delivers products manufactured at factories to users. Specifically, the Delivery Department receives products from factories and has established a logistics network to deliver them quickly to the user's specified address. The Delivery Department selects the optimal delivery method considering the size and weight of the product, the distance to the delivery destination, etc. For example, small products are delivered by courier, while large products are delivered by specialized delivery companies. In addition, the Delivery Department uses appropriate packaging and protective materials to ensure the safe delivery of products so that they arrive at the user's location without damage. Furthermore, the Delivery Department has implemented a system to track the delivery status in real time, allowing users to always check the location of their products. This enables the Delivery Department to achieve fast and safe delivery of products and improve user satisfaction.

[0070] The generation unit can interpret the intent from the input text, illustrations, and photographs and propose appropriate size, material, and design plans. For example, the generation unit can convert a child's drawing into a 3D object to generate data for a 3D printer. The generation unit can also generate design data for parts needed to repair furniture. For example, the generation unit can generate data to create a 3D model of a child's drawing to keep as a memento. The generation unit can also generate design data for parts needed to repair furniture. This allows the generation unit to generate appropriate design data based on user input. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the input text, illustrations, and photographs into a generation AI, which can interpret the intent and propose appropriate size, material, and design plans.

[0071] The proposal unit can select the most suitable manufacturer from among the registered manufacturers based on conditions such as material, size, and quantity. For example, the proposal unit can select the most suitable manufacturer from among the registered manufacturers based on conditions such as material, size, and quantity. The proposal unit can propose, for example, a manufacturer that excels at manufacturing metal parts or a manufacturer suitable for producing works of art. For example, the proposal unit can select a manufacturer that excels at manufacturing metal parts and propose it to the user. The proposal unit can also select a manufacturer suitable for producing works of art and propose it to the user. In this way, the proposal unit can select the most suitable manufacturer and propose it to the user. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input information on registered manufacturers into AI, and the AI ​​can select the most suitable manufacturer based on conditions such as material, size, and quantity.

[0072] The delivery department can deliver products manufactured at the factory to users. The delivery department can, for example, deliver products manufactured at the factory to users. The delivery department can, for example, deliver products manufactured at the factory quickly. The delivery department can, for example, deliver products manufactured at the factory quickly. The delivery department can also deliver products manufactured at the factory safely. In this way, the delivery department can deliver manufactured products to users. Some or all of the above processes in the delivery department may be performed using AI, for example, or not using AI. For example, the delivery department can input delivery information of products manufactured at the factory into AI, and the AI ​​can select the optimal delivery method.

[0073] The generation unit can convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can, for example, convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can, for example, generate data to create a 3D model of a child's drawing to keep as a memento. The generation unit can also convert a child's drawing into a 3D object and generate data for a 3D printer. The generation unit can, for example, generate data to create a 3D model of a child's drawing to keep as a memento. The generation unit can also convert a child's drawing into a 3D object to generate data for a 3D printer. Thus, the generation unit can create a 3D model of a child's drawing and generate data for a 3D printer. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the generation unit can input a child's drawing into a generation AI, and the generation AI can convert it into a 3D object and generate data for a 3D printer.

[0074] The generation unit can generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can also generate design data for parts necessary for furniture repair. The generation unit can also generate design data for parts necessary for furniture repair. The generation unit can generate design data for parts necessary for furniture repair. The generation unit can also generate design data for parts necessary for furniture repair. In this way, the generation unit can generate design data for parts necessary for furniture repair. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input a photograph of the parts necessary for furniture repair into the generation AI, and the generation AI can generate design data.

[0075] The reception desk can estimate the user's emotions and customize the idea input interface 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, for example, the reception desk can provide detailed input options and suggest a customizable input method. If the user is in a hurry, for example, the reception desk can prioritize voice input to allow for quick idea input. In this way, the reception desk can provide a more appropriate input environment by customizing the input interface 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 using AI. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions and customize the interface.

[0076] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display as suggestions the format of ideas (text, illustrations, photos) that the user has frequently entered in the past. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest the format of ideas to be used during a specific time period based on the user's past input history. This allows the reception desk to suggest the optimal input method based on the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into AI, and the AI ​​can suggest the optimal input method.

[0077] The reception desk can provide input guides based on the user's current projects and areas of interest when inputting ideas. For example, when a user inputs ideas related to an ongoing project, the reception desk can provide relevant guidelines and samples. The reception desk can also suggest methods for inputting relevant ideas based on the user's areas of interest. For example, if a user is interested in a particular field, the reception desk can provide input guides specific to that field. This allows the reception desk to improve input efficiency by providing input guides tailored to the user's projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's project information into AI, and the AI ​​can provide relevant input guides.

[0078] The reception unit can estimate the user's emotions and determine the priority of input ideas based on the estimated user emotions. For example, if the user is excited, the reception unit will prioritize processing that idea. If the user is relaxed, the reception unit can process ideas with normal priority. If the user is stressed, the reception unit will process ideas quickly. This allows the reception unit to prioritize important ideas by determining the priority of ideas based on 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 processing described above in the reception unit may be performed using AI or not using AI. For example, the reception unit can input user facial expression data into a generative AI, which can estimate emotions and determine the priority of ideas.

[0079] The input unit can provide highly relevant input guidance when an idea is entered, taking into account the user's geographical location. For example, if the user is in a specific region, the input unit can provide input guidance for ideas related to that region. The input unit can also suggest region-specific materials and designs based on the user's location. For example, if the user is traveling, the input unit can provide input guidance for ideas related to their travel destination. This allows the input unit to improve input accuracy by providing highly relevant input guidance based on the user's geographical location. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input the user's geographical location into AI, which can then provide highly relevant input guidance.

[0080] The reception desk can analyze the user's social media activity when an idea is entered and suggest relevant ideas. For example, the reception desk can provide relevant input guides based on ideas the user has shared on social media. For example, the reception desk can also suggest ideas related to areas of interest based on the user's social media activity. For example, the reception desk can analyze the content of posts from accounts the user follows on social media and suggest relevant ideas. In this way, the reception desk can provide ideas tailored to the user's interests by suggesting relevant ideas based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can suggest relevant ideas.

[0081] The generation unit can estimate the user's emotions and adjust the way the generated design data is represented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate detailed design data. For example, if the user is in a hurry, the generation unit can also generate concise design data. For example, if the user is excited, the generation unit can generate visually appealing design data. In this way, the generation unit can generate more appropriate design data by adjusting the way the design data is represented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation 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 generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user facial expression data into a generation AI, which can estimate emotions and adjust the way the design data is represented.

[0082] The generation unit can adjust the level of detail of the design data based on the importance of the idea during generation. For example, the generation unit generates detailed design data for important ideas. For example, the generation unit can also generate design data with a standard level of detail for general ideas. For example, the generation unit generates concise design data for simple ideas. In this way, the generation unit can generate appropriate design data by adjusting the level of detail of the design data based on the importance of the idea. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the importance of the idea into the generation AI, and the generation AI can adjust the level of detail of the design data.

[0083] The generation unit can apply different generation algorithms depending on the category of the idea during generation. For example, in the case of a work of art, the generation unit can apply a visually appealing algorithm. For example, in the case of a metal part, the generation unit can also apply a precise algorithm. For example, in the case of furniture repair, the generation unit can apply an algorithm that prioritizes durability. In this way, the generation unit can generate appropriate design data by applying a generation algorithm appropriate to the category of the idea. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the category of the idea into the generation AI, and the generation AI can apply an appropriate generation algorithm.

[0084] The generation unit can estimate the user's emotions and adjust the length of the design data it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise design data. If the user is relaxed, for example, the generation unit can also generate longer design data that includes detailed explanations. If the user is excited, for example, the generation unit can generate design data with visually stimulating effects. In this way, the generation unit can generate appropriate design data by adjusting the length of the design data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or not using a generation AI. For example, the generation unit can input user facial expression data into a generation AI, which can estimate emotions and adjust the length of the design data.

[0085] The generation unit can determine the priority of design data based on the timing of idea submission during generation. For example, the generation unit will prioritize the generation of design data for urgent ideas. For example, the generation unit can also generate design data with a standard priority for typical ideas. For example, the generation unit will postpone the generation of design data for long-term ideas. This allows the generation unit to generate design data in the appropriate order by determining the priority of design data based on the timing of idea submission. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the timing of idea submission into the generation AI, and the generation AI can determine the priority of design data.

[0086] The generation unit can adjust the order of design data based on the relevance of ideas during generation. For example, the generation unit will prioritize generating design data for highly relevant ideas. For example, the generation unit can also postpone generating design data for less relevant ideas. For example, if multiple ideas are related, the generation unit will generate the design data in order of relevance. In this way, the generation unit can generate design data in order of relevance by adjusting the order of design data based on the relevance of ideas. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of ideas into a generation AI, and the generation AI can adjust the order of the design data.

[0087] 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, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. This allows the suggestion unit to provide more appropriate suggestions by adjusting the way it presents them according to the user's 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-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI, which can then estimate emotions and adjust the way it presents its suggestions.

[0088] The proposal department can adjust the level of detail of a proposal based on the importance of the manufacturing plant. For example, the proposal department will provide a detailed proposal for a critical manufacturing plant. For example, the proposal department may provide a proposal with a standard level of detail for a general manufacturing plant. For example, the proposal department will provide a concise proposal for a simple manufacturing plant. In this way, the proposal department can provide appropriate proposals by adjusting the level of detail based on the importance of the manufacturing plant. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the importance of the manufacturing plant into the AI, and the AI ​​can adjust the level of detail of the proposal.

[0089] The proposal unit can apply different proposal algorithms depending on the manufacturer's category when making a proposal. For example, if it is an art factory, the proposal unit will make visually appealing proposals. If it is a metal parts factory, the proposal unit can also make precise proposals. If it is a furniture repair factory, the proposal unit will make proposals that prioritize durability. In this way, the proposal unit can make appropriate proposals by applying a proposal algorithm appropriate to the manufacturer's category. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the manufacturer's category into the AI, and the AI ​​can apply an appropriate proposal algorithm.

[0090] 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, the suggestion unit can provide a short, concise suggestion. If the user is relaxed, the suggestion unit can provide a longer suggestion with detailed explanations. If the user is excited, the suggestion unit can provide a suggestion with visually stimulating effects. In this way, the suggestion unit can provide appropriate suggestions by adjusting the length of the suggestion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the length of the suggestion.

[0091] The proposal department can determine the priority of proposals based on the submission timing of each factory. For example, the proposal department may prioritize proposals from factories with urgent needs. For example, the proposal department may also submit proposals with standard priority for factories with normal needs. For example, the proposal department may postpone proposals from factories with long-term needs. This allows the proposal department to submit proposals in the appropriate order by determining the priority of proposals based on the submission timing of each factory. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the submission timing of each factory into the AI, which can then determine the priority of the proposals.

[0092] The proposal unit can adjust the order of proposals based on the relationships between the manufacturing plants. For example, the proposal unit will prioritize proposals to highly relevant plants. For example, the proposal unit may postpone proposals to less relevant plants. For example, if multiple plants are related, the proposal unit will propose to them in order of relevance. In this way, the proposal unit can make proposals in order of relevance by adjusting the order of proposals based on the relationships between the plants. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relationships between plants into the AI, and the AI ​​can adjust the order of proposals.

[0093] The delivery unit can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is relaxed, the delivery unit will select a standard delivery method. If the user is in a hurry, the delivery unit may select an expedited delivery method. If the user is excited, the delivery unit may add special packaging or a message. In this way, the delivery unit can provide appropriate delivery by adjusting the delivery method 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 delivery unit may be performed using AI or not using AI. For example, the delivery unit can input user facial expression data into a generative AI, which can estimate emotions and adjust the delivery method.

[0094] The delivery department can analyze the user's past delivery history to select the optimal delivery method during delivery. For example, the delivery department can propose the optimal delivery method based on the delivery method the user has used in the past. For example, the delivery department can also propose a fast delivery method based on the user's past delivery history. For example, the delivery department can analyze the user's past delivery history and propose the most efficient delivery method. In this way, the delivery department can perform efficient deliveries by selecting the optimal delivery method based on the user's past delivery history. Some or all of the above processes in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's past delivery history into AI, and the AI ​​can propose the optimal delivery method.

[0095] The delivery department can customize the delivery method based on the user's current living situation at the time of delivery. For example, if the user is at home, the delivery department will select regular courier service. If the user is out, the delivery department can also use convenience store pickup or a delivery locker. If the user is traveling, the delivery department will deliver to a hotel or designated location at the travel destination. In this way, the delivery department can provide appropriate delivery by customizing the delivery method according to the user's living situation. Some or all of the above processing in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input the user's living situation into the AI, and the AI ​​can suggest the most suitable delivery method.

[0096] The delivery unit can estimate the user's emotions and determine delivery priorities based on those emotions. For example, if the user is in a hurry, the delivery unit will prioritize the delivery. If the user is relaxed, the delivery unit may also prioritize the delivery at the normal priority level. If the user is excited, the delivery unit may offer special delivery options. This allows the delivery unit to prioritize important deliveries by determining delivery priorities based on the user's 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 delivery unit may be performed using AI or not. For example, the delivery unit can input user facial expression data into a generative AI, which can estimate emotions and determine delivery priorities.

[0097] The delivery department can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in an urban area, the delivery department may select an expedited courier service. If the user is in a suburban area, the delivery department may select a region-specific delivery service. If the user is overseas, the delivery department may select an international delivery service. This allows the delivery department to perform efficient deliveries by selecting the optimal delivery method based on the user's geographical location information. Some or all of the above processing in the delivery department may be performed using AI, for example, or without AI. For example, the delivery department can input the user's geographical location information into AI, which can then suggest the optimal delivery method.

[0098] The delivery department can analyze the user's social media activity during delivery and suggest a delivery method. For example, the delivery department can suggest the optimal delivery method based on location information shared by the user on social media. For example, the delivery department can suggest delivery options that the user is interested in based on their social media activity. For example, the delivery department can suggest the optimal delivery method based on the delivery services the user follows on social media. This allows the delivery department to perform efficient deliveries by suggesting the optimal delivery method based on the user's social media activity. Some or all of the above processes in the delivery department may be performed using AI, for example, or not. For example, the delivery department can input the user's social media activity into AI, which can then suggest the optimal delivery method.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can estimate the user's emotions and customize the idea input interface based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick idea entry. In this way, the reception desk can provide a more appropriate input environment by customizing the input interface according to the user's emotions.

[0101] The generation unit can estimate the user's emotions and adjust the way the design data is represented based on those estimated emotions. For example, if the user is relaxed, it can generate detailed design data. If the user is in a hurry, it can generate concise design data. Furthermore, if the user is excited, it can generate visually appealing design data. In this way, the generation unit can generate more appropriate design data by adjusting the way the design data is represented according to the user's emotions.

[0102] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, it can offer detailed suggestions. If the user is in a hurry, it can offer concise suggestions. Furthermore, if the user is excited, it can offer visually appealing suggestions. In this way, the suggestion function can provide more appropriate suggestions by adjusting the way it presents suggestions according to the user's emotions.

[0103] The delivery department can estimate the user's emotions and adjust the delivery method based on those estimates. For example, if the user is relaxed, a standard delivery method can be selected. If the user is in a hurry, an expedited delivery method can be chosen. Furthermore, if the user is excited, special packaging or a message can be added to the delivery. In this way, the delivery department can provide appropriate delivery by adjusting the delivery method according to the user's emotions.

[0104] The reception desk can analyze a user's past input history and suggest the most suitable input method. For example, it can automatically display as suggestions the format of ideas (text, illustrations, photos) that the user has frequently entered in the past. It can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest the format of ideas that the user will use at a specific time of day based on their past input history. In this way, the reception desk can suggest the most suitable input method based on the user's past input history.

[0105] The proposal department can adjust the level of detail in a proposal based on the importance of the manufacturing plant. For example, a detailed proposal can be made for a critical manufacturing plant. For a general manufacturing plant, a proposal with a standard level of detail is possible. Furthermore, for a simple manufacturing plant, a concise proposal can be made. In this way, the proposal department can make appropriate proposals by adjusting the level of detail based on the importance of the manufacturing plant.

[0106] The delivery department can analyze a user's past delivery history to select the optimal delivery method. For example, it can suggest the most suitable delivery method based on the delivery method the user has used in the past. It can also suggest a faster delivery method based on the user's past delivery history. Furthermore, it can analyze the user's past delivery history and suggest the most efficient delivery method. As a result, the delivery department can perform efficient deliveries by selecting the optimal delivery method based on the user's past delivery history.

[0107] The generation unit can adjust the level of detail of the design data based on the importance of the idea during generation. For example, for important ideas, it can generate detailed design data. For general ideas, it can also generate design data with a standard level of detail. Furthermore, for simple ideas, it can generate concise design data. In this way, the generation unit can generate appropriate design data by adjusting the level of detail of the design data based on the importance of the idea.

[0108] The proposal department can apply different proposal algorithms depending on the category of the manufacturing facility. For example, an art factory can make visually appealing proposals. A metal parts factory can make precise proposals. Furthermore, a furniture repair factory can make proposals that prioritize durability. In this way, the proposal department can make appropriate proposals by applying proposal algorithms tailored to the category of the manufacturing facility.

[0109] The delivery department can select the most suitable delivery method by considering the user's geographical location during delivery. For example, if the user is in an urban area, it can select a fast courier service. If the user is in a suburban area, it can select a delivery service specific to that region. Furthermore, if the user is overseas, it can select an international delivery service. In this way, the delivery department can perform efficient deliveries by selecting the most suitable delivery method based on the user's geographical location.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives the user's idea. User ideas include business ideas, technical ideas, and creative ideas. Ideas can be entered into the reception desk in various formats such as text, illustrations, and photographs. Step 2: The generation unit uses generation AI to analyze the ideas entered by the reception unit and generate design data for a 3D printer. The generation unit understands the intent from the entered text, illustrations, and photos, and proposes appropriate size, materials, and design drawings. For example, it can convert a child's drawing into a 3D model to generate data for a 3D printer, or generate design data for parts needed to repair furniture. Step 3: The Proposal Department proposes the most suitable manufacturing company based on the design data generated by the Generation Department. The Proposal Department selects the most suitable manufacturing company from the registered companies based on conditions such as material, size, and quantity. For example, it may propose a manufacturing company that specializes in the production of metal parts or a manufacturing company suitable for the production of art pieces. Step 4: The delivery department delivers the products manufactured at the factory proposed by the proposal department. The delivery department can quickly deliver the products manufactured at the factory to the user.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and delivery 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 is implemented by the reception device 38 of the smart device 14, which inputs the user's ideas in the form of text, illustrations, photographs, etc. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input ideas using generation AI and generates design data for a 3D printer. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the optimal manufacturing plant based on the generated design data. The delivery unit delivers the products manufactured at the manufacturing plant to the user via, for example, the communication I / F 44 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 various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and delivery unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and takes the user's idea as voice input. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze the input idea and generate design data for a 3D printer. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal manufacturing plant based on the generated design data. The delivery unit delivers the product manufactured at the manufacturing plant to the user, for example, via the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and delivery 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 is implemented by the microphone 238 of the headset terminal 314 and takes the user's ideas as voice input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generation AI to analyze the input ideas and generate design data for a 3D printer. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal manufacturing plant based on the generated design data. The delivery unit delivers the products manufactured at the manufacturing plant to the user via, for example, the communication I / F 44 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 can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the reception unit, generation unit, proposal unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, which takes the user's ideas as voice input. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the input ideas using generation AI and generates design data for a 3D printer. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the optimal manufacturing plant based on the generated design data. The delivery unit delivers the products manufactured at the manufacturing plant to the user via, for example, the communication I / F 44 of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A reception area where users input their ideas, A generation unit analyzes the ideas input by the reception unit and generates design data for a 3D printer, A proposal unit that proposes the optimal manufacturing plant based on the design data generated by the generation unit, The system includes a delivery unit that delivers products manufactured at the factory proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The generating unit is We interpret the intent from the entered text, illustrations, and photos, and propose appropriate sizes, materials, and design plans. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, From the registered manufacturers, the most suitable manufacturer is selected based on conditions such as material, size, and quantity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned delivery department, Products manufactured at the factory are delivered to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Converting children's drawings into 3D shapes to generate data for 3D printing. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate design data for parts needed to repair furniture. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and customizes the idea input interface based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering ideas, the system provides input guidance based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the input ideas based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input ideas, the system provides relevant input guides that take into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When you submit an idea, the system analyzes your social media activity and suggests relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates user emotions and adjusts how design data is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail in the design data is adjusted based on the importance of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the design data generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, design data is prioritized based on when the idea was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the order of design data is adjusted based on the relevance of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the manufacturing plant. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When submitting a proposal, different proposal algorithms are applied depending on the manufacturer's category. The system described in Appendix 1, characterized by the features described herein. (Note 22) 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 23) The aforementioned proposal section is, When submitting proposals, the priority of proposals will be determined based on the submission timing of the manufacturers. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the manufacturing plants. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned delivery department, The system estimates the user's emotions and adjusts the delivery method based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned delivery department, During delivery, the system analyzes the user's past delivery history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned delivery department, During delivery, the delivery method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned delivery department, The system estimates the user's emotions and determines delivery priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned delivery department, During delivery, the system selects the optimal delivery method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned delivery department, During delivery, we analyze the user's social media activity and suggest delivery methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 area where users input their ideas, A generation unit analyzes the ideas input by the reception unit and generates design data for a 3D printer, A proposal unit that proposes the optimal manufacturing plant based on the design data generated by the generation unit, The system includes a delivery unit that delivers products manufactured at the factory proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The generating unit is We interpret the intent from the entered text, illustrations, and photos, and propose appropriate sizes, materials, and design plans. The system according to feature 1.

3. The aforementioned proposal section is, From the registered manufacturers, the most suitable manufacturer is selected based on conditions such as material, size, and quantity. The system according to feature 1.

4. The aforementioned delivery department, Products manufactured at the factory are delivered to the user. The system according to feature 1.

5. The generating unit is Converting children's drawings into 3D shapes to generate data for 3D printing. The system according to feature 1.

6. The generating unit is Generate design data for parts needed to repair furniture. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and customizes the idea input interface based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

9. The aforementioned reception unit is When entering ideas, the system provides input guidance based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes the input ideas based on the estimated user emotions. The system according to feature 1.

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