System

Abstract

To make it possible to easily design and quickly manufacture what consumer wants in a system of an embodiment.SOLUTION: A system according to an embodiment comprises: a reception unit; a generation unit; a design unit; and a process unit. The reception unit inputs a prompt. The generation unit analyzes the prompt input by the reception unit and generates a 3D preview. The design unit designs a detailed shape on the basis of the 3D preview generated by the generation unit. The process unit makes a layout of a manufacturing line on the basis of a detailed shape designed by the design unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback that the process for consumers to specifically design and manufacture what they want is complex, time-consuming and labor-intensive.

[0005] The system according to the embodiment aims to enable consumers to easily design what they want and quickly manufacture it. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a design unit, and a process unit. The reception unit inputs a prompt. The generation unit analyzes the prompt input by the reception unit and generates a 3D preview. The design unit designs a detailed shape based on the 3D preview generated by the generation unit. The process unit lays out a manufacturing line based on the detailed shape designed by the design unit. Effect of the Invention

[0007] The system according to the embodiment allows consumers to easily design and quickly manufacture what they want. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) In the customized product manufacturing system according to the embodiment of the present invention, when a consumer inputs what he or she wants as a prompt (shape, color, material, etc.), a 3D preview is automatically output, and when the system is executed, the design AI designs the detailed shape based on the 3D data, the process AI autonomously lays out the manufacturing line, and the finished product is shipped to the home in a few days. This system allows consumers to easily customize what they want and get it in a short period of time. For example, when a consumer inputs a prompt such as "a red plastic chair," the generation AI analyzes the input prompt and generates a 3D preview. The generated 3D preview is displayed to the consumer and confirmed. When the consumer checks the 3D preview and instructs execution, the design AI designs the detailed shape based on the 3D data. The design AI creates a detailed design drawing based on the prompt. For example, the dimensions and structure of the chair are designed in detail. Next, the process AI autonomously lays out the manufacturing line based on the design drawing created by the design AI. The process AI plans the optimal manufacturing process and builds the manufacturing line. For example, it plans the order in which each part of the chair is manufactured and assembled. Finally, the completed product is shipped to the consumer's home. The product arrives in the consumer's hands within a few days. For example, a red plastic chair is delivered to the consumer's home. This makes it possible for consumers to easily customize what they want and get it in a short time.

[0029] The customized product manufacturing system according to the embodiment includes a reception unit, a generation unit, a design unit, and a process unit. The reception unit inputs a prompt such as the shape, color, and material of the product that the consumer wants. The prompt input by the consumer includes, but is not limited to, for example, a text format, a voice input, and an image input. The reception unit enables the consumer to input a prompt such as "a red plastic chair". The generation unit uses a generation AI to analyze the prompt input by the reception unit and generate a 3D preview. The generated 3D preview is, for example, a 3D preview of a red plastic chair. The generation unit uses a generation AI to generate a 3D preview based on the prompt. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the prompt and generate a 3D preview. The generation unit can also use a multimodal generation AI to generate a 3D preview based on the prompt. The design unit uses a design AI to design a detailed shape based on the 3D preview generated by the generation unit. The design unit designs the dimensions and structure of the chair in detail based on the 3D preview generated by the generation AI. The design department uses a design AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. The process department uses a process AI to layout a manufacturing line based on the detailed shape designed by the design department. The process department plans an optimal manufacturing process based on, for example, the design drawing created by the design AI, and builds a manufacturing line. The process department uses a process AI to layout a manufacturing line. For example, the process AI plans the order in which each part of a chair is manufactured and assembled. In this way, the customized product manufacturing system according to the embodiment allows consumers to easily customize what they want and obtain it in a short period of time. Some or all of the above-mentioned processing in the process department may be performed, for example, using AI, or may be performed without using AI. For example, the process department can layout a manufacturing line using an AI model that inputs a detailed shape designed by the design department and outputs a layout of a manufacturing line.

[0030] The reception unit inputs prompts such as the shape, color, and material of the thing the consumer wants. The prompts input by the consumer include, but are not limited to, text format, voice input, image input, and the like. The reception unit allows the consumer to input a prompt such as, for example, "a red plastic chair." Specifically, the reception unit provides an interface that supports various input formats and is designed to allow the consumer to operate it intuitively. In the case of text input, the consumer can input detailed specifications using a keyboard, and in the case of voice input, the consumer's request is converted into text using voice recognition technology. In the case of image input, the consumer uploads a reference image, and the system performs analysis based on the image. This allows the consumer to specifically convey his or her image, and the system can accurately understand the request. Furthermore, the reception unit can analyze the prompt input by the consumer in real time and request additional information as necessary. For example, if the consumer inputs "a red chair," the system will ask an additional question such as "What material would you like?" to collect more detailed specifications. This allows the reception unit to accurately grasp the consumer's request and prepare input data for the generation unit, which is the next step.

[0031] The generation unit uses the generation AI to analyze the prompt input by the reception unit and generate a 3D preview. The generated 3D preview is, for example, a 3D preview of a red plastic chair. The generation unit uses the generation AI to generate a 3D preview based on the prompt. For example, the generation AI analyzes the prompt using a text generation AI (for example, LLM) and generates a 3D preview. The generation unit can also generate a 3D preview based on the prompt using a multimodal generation AI. Specifically, the generation AI analyzes text, image, and voice data input by the consumer and generates a 3D model based on the data. For example, the text generation AI analyzes the prompt "red plastic chair" input by the consumer and generates a 3D model based on the specifications. The multimodal generation AI integrates and analyzes multiple data formats such as text, image, and voice to generate a more accurate 3D preview. The generated 3D preview is displayed in an interactive viewer so that the consumer can check it, and the consumer can rotate the model and zoom in and out to check the details. In addition, the generator also has a function that allows consumers to provide feedback on the 3D preview. For example, if a consumer inputs a request such as "I want the backrest to be a little higher," the generator AI will update the 3D preview to reflect that feedback. This allows the generator to flexibly respond to consumer requests and finalize the final product image.

[0032] The design department uses the design AI to design the detailed shape based on the 3D preview generated by the generation department. For example, the design department designs the dimensions and structure of the chair in detail based on the 3D preview generated by the generation AI. The design department uses the design AI to create detailed blueprints. For example, the design AI creates detailed blueprints using CAD data. Specifically, the design AI analyzes the 3D preview provided by the generation department and performs detailed design taking into account the dimensions, shape, and material properties of each part of the product. The design AI can automatically generate precise blueprints in cooperation with CAD software. For example, it calculates the strength of the chair's legs and selects appropriate materials and dimensions. In addition, the design AI performs optimal design taking into account the product's assembly and manufacturing processes. For example, it devises a design for the joints so that each part of the chair can be assembled efficiently. In addition, the design department also has the function of reflecting consumer feedback and modifying the design. For example, if a consumer inputs a request such as "I want the seat to be a little wider," the design AI updates the blueprint to reflect that request. This allows the design department to flexibly respond to consumer demands and finalize the final product design. The design department can work closely with the production department to create designs that accurately reflect consumer demands, thereby improving product quality and customer satisfaction.

[0033] The process department uses the process AI to lay out the manufacturing line based on the detailed shape designed by the design department. For example, the process department plans the optimal manufacturing process based on the design drawing created by the design AI and builds the manufacturing line. The process department uses the process AI to lay out the manufacturing line. For example, the process AI plans the order in which each part of the chair will be manufactured and assembled. Specifically, the process AI analyzes the detailed design drawing provided by the design department and optimizes the manufacturing process for each part of the product. The process AI optimizes the layout of the manufacturing equipment and the work procedure to build an efficient manufacturing line. For example, it plans the order in which each part of the chair, such as the legs, seat, and backrest, will be manufactured and how they will be assembled. In addition, the process AI simulates the manufacturing process and verifies the optimal manufacturing procedure. This maximizes the efficiency of the manufacturing line and improves the quality of the product. In addition, the process department has a real-time monitoring and feedback function for the manufacturing process. For example, it detects abnormalities and problems that occur in the manufacturing line and responds quickly to ensure the stability and reliability of the manufacturing process. The process department works closely with the design department to realize an optimal manufacturing process based on design drawings, thereby quickly providing high-quality products that meet consumer demands. As a result, the customized product manufacturing system according to the embodiment allows consumers to easily customize what they want and obtain it in a short period of time.

[0034] The generation unit can generate a 3D preview based on the prompt. The generation unit generates the 3D preview based on the prompt using, for example, a generation AI. The generation unit generates the 3D preview based on the prompt using the generation AI. For example, the generation AI analyzes the prompt using a text generation AI (for example, LLM) to generate the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. The generation unit generates the 3D preview based on the prompt using the generation AI. For example, the generation AI analyzes the prompt using a text generation AI (for example, LLM) to generate the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. This allows the consumer to visually confirm the 3D preview by generating the 3D preview based on the prompt.

[0035] The design department can create a detailed design drawing based on the 3D preview generated by the generation AI. The design department creates a detailed design drawing based on the 3D preview generated by the generation AI, for example. The design department uses the generation AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. The design department uses the generation AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. The design department uses the generation AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. This enables accurate design by creating a detailed design drawing based on the 3D preview generated by the generation AI.

[0036] The process department can plan an efficient manufacturing process and build a manufacturing line based on the design drawings created by the design AI. The process department, for example, plans an efficient manufacturing process and builds a manufacturing line based on the design drawings created by the design AI. The process department uses the design AI to layout the manufacturing line. For example, the process AI plans in what order each part of a chair will be manufactured and assembled. The process department uses the design AI to layout the manufacturing line. For example, the process AI plans in what order each part of a chair will be manufactured and assembled. The process department uses the design AI to layout the manufacturing line. For example, the process AI plans in what order each part of a chair will be manufactured and assembled. This makes it possible to plan an efficient manufacturing process and build a manufacturing line based on the design drawings created by the design AI, thereby making it possible to achieve efficient manufacturing.

[0037] The processing unit can ship the product after production is complete to the consumer's home. The processing unit, for example, ships the product after production is complete to the consumer's home. The processing unit ships the product after production is complete to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing unit ships the product after production is complete to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing unit ships the product after production is complete to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. In this way, by shipping the product after production is complete to the consumer's home, the consumer can receive the product quickly.

[0038] The reception unit can input a prompt for the shape, color, and material of the thing the consumer wants. The reception unit, for example, inputs a prompt for the shape, color, and material of the thing the consumer wants. The reception unit inputs a prompt for the shape, color, and material of the thing the consumer wants. For example, it enables the consumer to input a prompt such as "a red plastic chair". The reception unit inputs a prompt for the shape, color, and material of the thing the consumer wants. For example, it enables the consumer to input a prompt such as "a red plastic chair". The reception unit inputs a prompt for the shape, color, and material of the thing the consumer wants. For example, it enables the consumer to input a prompt such as "a red plastic chair". This allows the consumer to order a customized product by inputting a prompt such as the shape, color, and material of the thing they want.

[0039] The reception unit can analyze the past prompt input history and suggest an efficient prompt input method to the user. For example, the reception unit automatically displays shapes, colors, and materials that the user frequently inputs in the past as candidates. The reception unit preferentially suggests input methods (such as voice and text) that the user has used in the past. The reception unit predicts and suggests prompts to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest an optimal prompt input method to the user by analyzing the past prompt input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the past prompt input history to a generation AI and cause the generation AI to suggest an optimal prompt input method.

[0040] The reception unit can present input candidates based on the user's current project or field of interest when inputting a prompt. The reception unit displays, for example, shapes, colors, and materials related to a project currently being carried out by the user as candidates. The reception unit proposes a related prompt based on the user's field of interest. The reception unit proposes an optimal prompt based on a project in which the user has shown interest in the past. This makes it possible to present input candidates based on the user's current project or field of interest, thereby making the user's input more efficient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest to the generation AI, and cause the generation AI to present related input candidates.

[0041] The reception unit can preferentially present highly relevant input candidates based on the geographical location information of the user when inputting a prompt. For example, when the user is in a specific area, the reception unit displays shapes, colors, and materials related to the area as candidates. The reception unit proposes an optimal prompt based on the user's current location. The reception unit proposes related prompts based on places the user has visited in the past. This makes the user's input more efficient by presenting highly relevant input candidates based on the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant input candidates.

[0042] The reception unit can analyze the user's social media activity at the time of inputting a prompt and present related input candidates. The reception unit displays related shapes, colors, and materials as candidates based on, for example, the content shared by the user on social media. The reception unit suggests optimal prompts based on the user's social media interests. The reception unit suggests related prompts based on accounts the user follows on social media. In this way, related input candidates can be presented by analyzing the user's social media activities. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to present related input candidates.

[0043] The generating unit can adjust the level of detail of the 3D preview based on the importance of the prompt during generation. For example, when the generating unit is based on an important prompt, the generating unit generates a detailed 3D preview. When the generating unit is based on a general prompt, the generating unit generates a simplified 3D preview. When the generating unit is based on a specific prompt, the generating unit generates a 3D preview with a required level of detail. This allows the level of detail of the 3D preview to be adjusted based on the importance of the prompt, thereby providing a preview according to the needs of the user. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit may input the importance data of the prompt to the generating AI and cause the generating AI to adjust the level of detail of the 3D preview.

[0044] The generation unit can apply different generation algorithms depending on the category of the prompt during generation. For example, the generation unit applies a generation algorithm dedicated to furniture to a prompt in the furniture category. The generation unit applies a generation algorithm dedicated to clothing to a prompt in the clothing category. The generation unit applies a generation algorithm dedicated to electronic devices to a prompt in the electronic device category. In this way, an appropriate 3D preview is generated by applying different generation algorithms depending on the category of the prompt. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the prompt to the generation AI and cause the generation AI to apply the generation algorithm.

[0045] The generation unit can determine the priority of the 3D previews based on the submission time of the prompts at the time of generation. For example, the generation unit generates a 3D preview with priority for a prompt submitted early. For an urgent prompt, the generation unit generates a 3D preview with the highest priority. For normal prompts, the generation unit generates a 3D preview in the order of submission. This enables efficient preview generation by determining the priority of the 3D previews based on the submission time of the prompts. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the prompts to the generation AI and cause the generation AI to execute the determination of the priority of the 3D previews.

[0046] The generation unit can adjust the order of the 3D previews based on the relevance of the prompts at the time of generation. For example, the generation unit generates 3D previews preferentially for prompts with high relevance. For prompts with medium relevance, the generation unit generates 3D previews in the normal order. For prompts with low relevance, the generation unit generates 3D previews later. This allows efficient preview generation by adjusting the order of the 3D previews based on the relevance of the prompts. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input relevance data of the prompts to the generation AI and cause the generation AI to adjust the order of the 3D previews.

[0047] The design unit can adjust the level of detail of the design based on the importance of the 3D preview during design. For example, when the design is based on an important 3D preview, the design unit performs a detailed design. When the design is based on a general 3D preview, the design unit performs a simplified design. When the design is based on a specific 3D preview, the design unit performs a design with a required level of detail. This allows efficient design by adjusting the level of detail of the design based on the importance of the 3D preview. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input the importance data of the 3D preview to the generation AI and cause the generation AI to adjust the level of detail of the design.

[0048] The design unit can apply different design algorithms depending on the category of the prompt during design. For example, the design unit applies a furniture-specific design algorithm to a prompt in the furniture category. The design unit applies a clothing-specific design algorithm to a prompt in the clothing category. The design unit applies an electronic device-specific design algorithm to a prompt in the electronic device category. This enables appropriate design by applying different design algorithms depending on the category of the prompt. Some or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input category data of the prompt to the generation AI and cause the generation AI to apply the design algorithm.

[0049] The design department can determine the design priority based on the submission timing of the prompt during design. For example, the design department gives priority to design for prompts submitted early. For urgent prompts, the design department gives top priority to design. For normal prompts, the design department performs design in the order of submission. In this way, efficient design is possible by determining the design priority based on the submission timing of the prompt. A part or all of the above-mentioned processing in the design department may be performed using, for example, an AI, or may be performed without using an AI. For example, the design department can input prompt submission timing data to the generation AI and have the generation AI execute the determination of the design priority.

[0050] The design unit can adjust the order of design based on the relevance of the prompts during design. For example, the design unit prioritizes design for prompts with high relevance. The design unit performs design in the normal order for prompts with medium relevance. The design unit postpones design for prompts with low relevance. This allows efficient design by adjusting the order of design based on the relevance of the prompts. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input relevance data of the prompts to the generation AI and cause the generation AI to adjust the order of design.

[0051] The process department can adjust the level of detail of the manufacturing process based on the importance of the design drawing when laying out the manufacturing line. For example, when based on an important design drawing, the process department plans a detailed manufacturing process. When based on a general design drawing, the process department plans a simplified manufacturing process. When based on a specific design drawing, the process department plans a manufacturing process with the required level of detail. This allows efficient manufacturing by adjusting the level of detail of the manufacturing process based on the importance of the design drawing. A part or all of the above-mentioned processing in the process department may be performed using, for example, AI, or may be performed without using AI. For example, the process department can input the importance data of the design drawing to the generation AI and cause the generation AI to adjust the level of detail of the manufacturing process.

[0052] The process unit can apply different layout algorithms depending on the category of the blueprint when laying out the manufacturing line. For example, the process unit applies a layout algorithm dedicated to furniture to a blueprint in the furniture category. The process unit applies a layout algorithm dedicated to clothing to a blueprint in the clothing category. The process unit applies a layout algorithm dedicated to electronic devices to a blueprint in the electronic device category. This enables efficient manufacturing by applying different layout algorithms depending on the category of the blueprint. Some or all of the above-mentioned processing in the process unit may be performed using, for example, AI, or may be performed without using AI. For example, the process unit can input category data of the blueprint to the generation AI and cause the generation AI to apply the layout algorithm.

[0053] The process department can determine the priority of the manufacturing process based on the submission time of the design drawing when laying out the manufacturing line. For example, the process department plans the manufacturing process with priority for the design drawing submitted early. For urgent design drawings, the process department plans the manufacturing process with the highest priority. For normal design drawings, the process department plans the manufacturing process in the order of submission. This enables efficient manufacturing by determining the priority of the manufacturing process based on the submission time of the design drawing. A part or all of the above-mentioned processing in the process department may be performed using, for example, AI, or may be performed without using AI. For example, the process department can input the submission time data of the design drawing to the generation AI and have the generation AI execute the determination of the priority of the manufacturing process.

[0054] The process department can adjust the order of manufacturing processes based on the relevance of the design drawings when laying out the manufacturing line. For example, the process department plans the manufacturing process with priority for a design drawing with high relevance. For a design drawing with medium relevance, the process department plans the manufacturing process in a normal order. For a design drawing with low relevance, the process department plans the manufacturing process on a later date. This enables efficient manufacturing by adjusting the order of manufacturing processes based on the relevance of the design drawings. A part or all of the above-mentioned processing in the process department may be performed using, for example, AI, or may be performed without using AI. For example, the process department can input the relevance data of the design drawing to the generation AI and cause the generation AI to adjust the order of the manufacturing process.

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

[0056] The reception unit can analyze the user's past purchase history and automatically suggest related prompts. For example, similar prompts can be presented based on the shape, color, and material of products the user has purchased in the past. Furthermore, it can extract features of products that the user has given high ratings to in the past and suggest new prompts based on those features. It can also analyze the usage of products the user has purchased in the past and preferentially display prompts related to frequently used products. This makes it possible to utilize the user's past purchase history to provide more personalized prompt input.

[0057] The generation unit can suggest prompts based on the user's current weather information. For example, it can suggest highly waterproof materials on rainy days and highly breathable materials on sunny days. It can also suggest appropriate shapes and colors depending on the season. It can also analyze climate data in the user's location over the long term and present prompts for products suitable for that area. This allows for prompt suggestions that take into account the user's current weather information, improving user convenience.

[0058] The design department can propose optimal designs based on the user's health data. For example, if a user has back pain, it can propose a chair design that is gentle on the back. Furthermore, it can automatically adjust optimal dimensions and shapes based on the user's height and weight. It can also analyze the user's exercise data and propose designs that reduce fatigue even when sitting for long periods of time. This allows for personalized designs that utilize the user's health data.

[0059] The process department can monitor the quality of the product in real time during the manufacturing process and adjust the manufacturing process if necessary. For example, if there is an error in the product's dimensions or shape, it can automatically correct it. Also, if a defect occurs in the material of the product, it can continue production using an alternative material. In addition, it is possible to perform quality checks at each stage of the production line and issue an alert immediately if a problem occurs. This strengthens quality control during the manufacturing process and improves product quality.

[0060] The process department can optimize energy consumption during the manufacturing process. For example, it can monitor energy consumption in real time at each stage of the manufacturing line and select a highly energy-efficient process. It can also adjust the energy supply according to the operating status of the manufacturing line to reduce wasteful energy consumption. It can also actively use renewable energy to reduce the environmental load. This allows for the optimization of energy consumption and the realization of an environmentally friendly manufacturing process.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The reception unit inputs a prompt such as the shape, color, material, etc. of the thing the consumer wants. The prompt input by the consumer may be in the form of text, voice input, image input, etc. For example, the consumer may input a prompt such as "a red plastic chair." Step 2: The generation unit uses a generation AI to analyze the prompt input by the reception unit and generate a 3D preview. For example, the generation AI analyzes the prompt using a text generation AI (e.g., LLM) or a multimodal generation AI and generates a 3D preview. The generated 3D preview may display, for example, a 3D preview of a red plastic chair. Step 3: The design department uses design AI to design the detailed shape based on the 3D preview generated by the generation department. For example, based on the 3D preview generated by the generation AI, the dimensions and structure of the chair are designed in detail, and a detailed design drawing is created using CAD data. Step 4: The process department uses the process AI to lay out the production line based on the detailed shape designed by the design department. For example, based on the design drawings created by the design AI, the process department plans the optimal manufacturing process and builds the production line. The process AI plans the order in which each part of the chair should be manufactured and assembled.

[0063] (Example 2) In the customized product manufacturing system according to the embodiment of the present invention, when a consumer inputs what he or she wants as a prompt (shape, color, material, etc.), a 3D preview is automatically output, and when the system is executed, the design AI designs the detailed shape based on the 3D data, the process AI autonomously lays out the manufacturing line, and the finished product is shipped to the home in a few days. This system allows consumers to easily customize what they want and get it in a short period of time. For example, when a consumer inputs a prompt such as "a red plastic chair," the generation AI analyzes the input prompt and generates a 3D preview. The generated 3D preview is displayed to the consumer and confirmed. When the consumer checks the 3D preview and instructs execution, the design AI designs the detailed shape based on the 3D data. The design AI creates a detailed design drawing based on the prompt. For example, the dimensions and structure of the chair are designed in detail. Next, the process AI autonomously lays out the manufacturing line based on the design drawing created by the design AI. The process AI plans the optimal manufacturing process and builds the manufacturing line. For example, it plans the order in which each part of the chair is manufactured and assembled. Finally, the completed product is shipped to the consumer's home. The product arrives in the consumer's hands within a few days. For example, a red plastic chair is delivered to the consumer's home. This makes it possible for consumers to easily customize what they want and get it in a short time.

[0064] The customized product manufacturing system according to the embodiment includes a reception unit, a generation unit, a design unit, and a process unit. The reception unit inputs a prompt such as the shape, color, and material of the product that the consumer wants. The prompt input by the consumer includes, but is not limited to, for example, a text format, a voice input, and an image input. The reception unit enables the consumer to input a prompt such as "a red plastic chair". The generation unit uses a generation AI to analyze the prompt input by the reception unit and generate a 3D preview. The generated 3D preview is, for example, a 3D preview of a red plastic chair. The generation unit uses a generation AI to generate a 3D preview based on the prompt. For example, the generation AI uses a text generation AI (for example, LLM) to analyze the prompt and generate a 3D preview. The generation unit can also use a multimodal generation AI to generate a 3D preview based on the prompt. The design unit uses a design AI to design a detailed shape based on the 3D preview generated by the generation unit. The design unit designs the dimensions and structure of the chair in detail based on the 3D preview generated by the generation AI. The design department uses a design AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. The process department uses a process AI to layout a manufacturing line based on the detailed shape designed by the design department. The process department plans an optimal manufacturing process based on, for example, the design drawing created by the design AI, and builds a manufacturing line. The process department uses a process AI to layout a manufacturing line. For example, the process AI plans the order in which each part of a chair is manufactured and assembled. In this way, the customized product manufacturing system according to the embodiment allows consumers to easily customize what they want and obtain it in a short period of time. Some or all of the above-mentioned processing in the process department may be performed, for example, using AI, or may be performed without using AI. For example, the process department can layout a manufacturing line using an AI model that inputs a detailed shape designed by the design department and outputs a layout of a manufacturing line.

[0065] The reception unit inputs prompts such as the shape, color, and material of the thing the consumer wants. The prompts input by the consumer include, but are not limited to, text format, voice input, image input, and the like. The reception unit allows the consumer to input a prompt such as, for example, "a red plastic chair." Specifically, the reception unit provides an interface that supports various input formats and is designed to allow the consumer to operate it intuitively. In the case of text input, the consumer can input detailed specifications using a keyboard, and in the case of voice input, the consumer's request is converted into text using voice recognition technology. In the case of image input, the consumer uploads a reference image, and the system performs analysis based on the image. This allows the consumer to specifically convey his or her image, and the system can accurately understand the request. Furthermore, the reception unit can analyze the prompt input by the consumer in real time and request additional information as necessary. For example, if the consumer inputs "a red chair," the system will ask an additional question such as "What material would you like?" to collect more detailed specifications. This allows the reception unit to accurately grasp the consumer's request and prepare input data for the generation unit, which is the next step.

[0066] The generation unit uses the generation AI to analyze the prompt input by the reception unit and generate a 3D preview. The generated 3D preview is, for example, a 3D preview of a red plastic chair. The generation unit uses the generation AI to generate a 3D preview based on the prompt. For example, the generation AI analyzes the prompt using a text generation AI (for example, LLM) and generates a 3D preview. The generation unit can also generate a 3D preview based on the prompt using a multimodal generation AI. Specifically, the generation AI analyzes text, image, and voice data input by the consumer and generates a 3D model based on the data. For example, the text generation AI analyzes the prompt "red plastic chair" input by the consumer and generates a 3D model based on the specifications. The multimodal generation AI integrates and analyzes multiple data formats such as text, image, and voice to generate a more accurate 3D preview. The generated 3D preview is displayed in an interactive viewer so that the consumer can check it, and the consumer can rotate the model and zoom in and out to check the details. In addition, the generator also has a function that allows consumers to provide feedback on the 3D preview. For example, if a consumer inputs a request such as "I want the backrest to be a little higher," the generator AI will update the 3D preview to reflect that feedback. This allows the generator to flexibly respond to consumer requests and finalize the final product image.

[0067] The design department uses the design AI to design the detailed shape based on the 3D preview generated by the generation department. For example, the design department designs the dimensions and structure of the chair in detail based on the 3D preview generated by the generation AI. The design department uses the design AI to create detailed blueprints. For example, the design AI creates detailed blueprints using CAD data. Specifically, the design AI analyzes the 3D preview provided by the generation department and performs detailed design taking into account the dimensions, shape, and material properties of each part of the product. The design AI can automatically generate precise blueprints in cooperation with CAD software. For example, it calculates the strength of the chair's legs and selects appropriate materials and dimensions. In addition, the design AI performs optimal design taking into account the product's assembly and manufacturing processes. For example, it devises a design for the joints so that each part of the chair can be assembled efficiently. In addition, the design department also has the function of reflecting consumer feedback and modifying the design. For example, if a consumer inputs a request such as "I want the seat to be a little wider," the design AI updates the blueprint to reflect that request. This allows the design department to flexibly respond to consumer demands and finalize the final product design. The design department can work closely with the production department to create designs that accurately reflect consumer demands, thereby improving product quality and customer satisfaction.

[0068] The process department uses the process AI to lay out the manufacturing line based on the detailed shape designed by the design department. For example, the process department plans the optimal manufacturing process based on the design drawing created by the design AI and builds the manufacturing line. The process department uses the process AI to lay out the manufacturing line. For example, the process AI plans the order in which each part of the chair will be manufactured and assembled. Specifically, the process AI analyzes the detailed design drawing provided by the design department and optimizes the manufacturing process for each part of the product. The process AI optimizes the layout of the manufacturing equipment and the work procedure to build an efficient manufacturing line. For example, it plans the order in which each part of the chair, such as the legs, seat, and backrest, will be manufactured and how they will be assembled. In addition, the process AI simulates the manufacturing process and verifies the optimal manufacturing procedure. This maximizes the efficiency of the manufacturing line and improves the quality of the product. In addition, the process department has a real-time monitoring and feedback function for the manufacturing process. For example, it detects abnormalities and problems that occur in the manufacturing line and responds quickly to ensure the stability and reliability of the manufacturing process. The process department works closely with the design department to realize an optimal manufacturing process based on design drawings, thereby quickly providing high-quality products that meet consumer demands. As a result, the customized product manufacturing system according to the embodiment allows consumers to easily customize what they want and obtain it in a short period of time.

[0069] The generation unit can generate a 3D preview based on the prompt. The generation unit generates the 3D preview based on the prompt using, for example, a generation AI. The generation unit generates the 3D preview based on the prompt using the generation AI. For example, the generation AI analyzes the prompt using a text generation AI (for example, LLM) to generate the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. The generation unit generates the 3D preview based on the prompt using the generation AI. For example, the generation AI analyzes the prompt using a text generation AI (for example, LLM) to generate the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. This allows the consumer to visually confirm the 3D preview by generating the 3D preview based on the prompt.

[0070] The design department can create a detailed design drawing based on the 3D preview generated by the generation AI. The design department creates a detailed design drawing based on the 3D preview generated by the generation AI, for example. The design department uses the generation AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. The design department uses the generation AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. The design department uses the generation AI to create a detailed design drawing. For example, the design AI creates a detailed design drawing using CAD data. This enables accurate design by creating a detailed design drawing based on the 3D preview generated by the generation AI.

[0071] The process department can plan an efficient manufacturing process and build a manufacturing line based on the design drawings created by the design AI. The process department, for example, plans an efficient manufacturing process and builds a manufacturing line based on the design drawings created by the design AI. The process department uses the design AI to layout the manufacturing line. For example, the process AI plans in what order each part of a chair will be manufactured and assembled. The process department uses the design AI to layout the manufacturing line. For example, the process AI plans in what order each part of a chair will be manufactured and assembled. The process department uses the design AI to layout the manufacturing line. For example, the process AI plans in what order each part of a chair will be manufactured and assembled. This makes it possible to plan an efficient manufacturing process and build a manufacturing line based on the design drawings created by the design AI, thereby making it possible to achieve efficient manufacturing.

[0072] The processing unit can ship the product after production is complete to the consumer's home. The processing unit, for example, ships the product after production is complete to the consumer's home. The processing unit ships the product after production is complete to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing unit ships the product after production is complete to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing unit ships the product after production is complete to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. In this way, by shipping the product after production is complete to the consumer's home, the consumer can receive the product quickly.

[0073] The reception unit can input a prompt for the shape, color, and material of the thing the consumer wants. The reception unit, for example, inputs a prompt for the shape, color, and material of the thing the consumer wants. The reception unit inputs a prompt for the shape, color, and material of the thing the consumer wants. For example, it enables the consumer to input a prompt such as "a red plastic chair". The reception unit inputs a prompt for the shape, color, and material of the thing the consumer wants. For example, it enables the consumer to input a prompt such as "a red plastic chair". The reception unit inputs a prompt for the shape, color, and material of the thing the consumer wants. For example, it enables the consumer to input a prompt such as "a red plastic chair". This allows the consumer to order a customized product by inputting a prompt such as the shape, color, and material of the thing they want.

[0074] The reception unit can estimate the user's emotion and customize the input interface of the prompt based on the estimated user's emotion. For example, when the user is stressed, the reception unit provides a simple and intuitive interface and minimizes input procedures. When the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. When the user is in a hurry, the reception unit prioritizes voice input and enables the prompt to be input quickly. This improves user convenience by customizing the input interface of the prompt according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The reception unit can analyze the past prompt input history and suggest an efficient prompt input method to the user. For example, the reception unit automatically displays shapes, colors, and materials that the user frequently inputs in the past as candidates. The reception unit preferentially suggests input methods (such as voice and text) that the user has used in the past. The reception unit predicts and suggests prompts to be used in a specific time period from the user's past input history. In this way, the reception unit can suggest an optimal prompt input method to the user by analyzing the past prompt input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the past prompt input history to a generation AI and cause the generation AI to suggest an optimal prompt input method.

[0076] The reception unit can present input candidates based on the user's current project or field of interest when inputting a prompt. The reception unit displays, for example, shapes, colors, and materials related to a project currently being carried out by the user as candidates. The reception unit proposes a related prompt based on the user's field of interest. The reception unit proposes an optimal prompt based on a project in which the user has shown interest in the past. This makes it possible to present input candidates based on the user's current project or field of interest, thereby making the user's input more efficient. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input data on the user's current project or field of interest to the generation AI, and cause the generation AI to present related input candidates.

[0077] The reception unit can estimate the user's emotion and determine the priority of the prompt input based on the estimated user's emotion. For example, when the user is stressed, the reception unit allows important prompts to be input preferentially. When the user is relaxed, the reception unit allows detailed prompt inputs to be input preferentially. When the user is in a hurry, the reception unit allows the most important prompts to be input first. This improves the user's convenience by determining the priority of the prompt input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The reception unit can preferentially present highly relevant input candidates based on the geographical location information of the user when inputting a prompt. For example, when the user is in a specific area, the reception unit displays shapes, colors, and materials related to the area as candidates. The reception unit proposes an optimal prompt based on the user's current location. The reception unit proposes related prompts based on places the user has visited in the past. This makes the user's input more efficient by presenting highly relevant input candidates based on the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to present highly relevant input candidates.

[0079] The reception unit can analyze the user's social media activity at the time of inputting a prompt and present related input candidates. The reception unit displays related shapes, colors, and materials as candidates based on, for example, the content shared by the user on social media. The reception unit suggests optimal prompts based on the user's social media interests. The reception unit suggests related prompts based on accounts the user follows on social media. In this way, related input candidates can be presented by analyzing the user's social media activities. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media activity data to the generation AI and cause the generation AI to present related input candidates.

[0080] The generation unit can estimate the user's emotion and adjust the display method of the 3D preview based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a 3D preview that proceeds at a leisurely pace. When the user is in a hurry, the generation unit generates a 3D preview that emphasizes the shortest route. When the user is excited, the generation unit generates a 3D preview with a visually stimulating effect added. This improves the user's convenience by adjusting the display method of the 3D preview according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The generating unit can adjust the level of detail of the 3D preview based on the importance of the prompt during generation. For example, when the generating unit is based on an important prompt, the generating unit generates a detailed 3D preview. When the generating unit is based on a general prompt, the generating unit generates a simplified 3D preview. When the generating unit is based on a specific prompt, the generating unit generates a 3D preview with a required level of detail. This allows the level of detail of the 3D preview to be adjusted based on the importance of the prompt, thereby providing a preview according to the needs of the user. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit may input the importance data of the prompt to the generating AI and cause the generating AI to adjust the level of detail of the 3D preview.

[0082] The generation unit can apply different generation algorithms depending on the category of the prompt during generation. For example, the generation unit applies a generation algorithm dedicated to furniture to a prompt in the furniture category. The generation unit applies a generation algorithm dedicated to clothing to a prompt in the clothing category. The generation unit applies a generation algorithm dedicated to electronic devices to a prompt in the electronic device category. In this way, an appropriate 3D preview is generated by applying different generation algorithms depending on the category of the prompt. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data of the prompt to the generation AI and cause the generation AI to apply the generation algorithm.

[0083] The generation unit can estimate the user's emotion and adjust the length of the 3D preview based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short 3D preview that is to the point. When the user is relaxed, the generation unit generates a longer 3D preview that includes detailed explanations. When the user is excited, the generation unit generates a 3D preview with a visually stimulating effect. This improves the user's convenience by adjusting the length of the 3D preview according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The generation unit can determine the priority of the 3D previews based on the submission time of the prompts at the time of generation. For example, the generation unit generates a 3D preview with priority for a prompt submitted early. For an urgent prompt, the generation unit generates a 3D preview with the highest priority. For normal prompts, the generation unit generates a 3D preview in the order of submission. This enables efficient preview generation by determining the priority of the 3D previews based on the submission time of the prompts. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the prompts to the generation AI and cause the generation AI to execute the determination of the priority of the 3D previews.

[0085] The generation unit can adjust the order of the 3D previews based on the relevance of the prompts at the time of generation. For example, the generation unit generates 3D previews preferentially for prompts with high relevance. For prompts with medium relevance, the generation unit generates 3D previews in the normal order. For prompts with low relevance, the generation unit generates 3D previews later. This allows efficient preview generation by adjusting the order of the 3D previews based on the relevance of the prompts. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input relevance data of the prompts to the generation AI and cause the generation AI to adjust the order of the 3D previews.

[0086] The design unit can estimate the user's emotion and adjust the detailed design method based on the estimated user's emotion. For example, when the user is relaxed, the design unit performs a detailed design. When the user is in a hurry, the design unit performs a simplified design. When the user is excited, the design unit performs a visually attractive design. This allows a design according to the user's needs to be achieved by adjusting the detailed design method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0087] The design unit can adjust the level of detail of the design based on the importance of the 3D preview during design. For example, when the design is based on an important 3D preview, the design unit performs a detailed design. When the design is based on a general 3D preview, the design unit performs a simplified design. When the design is based on a specific 3D preview, the design unit performs a design with a required level of detail. This allows efficient design by adjusting the level of detail of the design based on the importance of the 3D preview. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input the importance data of the 3D preview to the generation AI and cause the generation AI to adjust the level of detail of the design.

[0088] The design unit can apply different design algorithms depending on the category of the prompt during design. For example, the design unit applies a furniture-specific design algorithm to a prompt in the furniture category. The design unit applies a clothing-specific design algorithm to a prompt in the clothing category. The design unit applies an electronic device-specific design algorithm to a prompt in the electronic device category. This enables appropriate design by applying different design algorithms depending on the category of the prompt. Some or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input category data of the prompt to the generation AI and cause the generation AI to apply the design algorithm.

[0089] The design unit can estimate the user's emotions and determine the priority of the design based on the estimated user's emotions. For example, when the user is stressed, the design unit prioritizes important designs. When the user is relaxed, the design unit prioritizes detailed designs. When the user is in a hurry, the design unit performs the most important designs first. This enables a design according to the user's needs by determining the design priority according to the user's emotions. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0090] The design department can determine the design priority based on the submission timing of the prompt during design. For example, the design department gives priority to design for prompts submitted early. For urgent prompts, the design department gives top priority to design. For normal prompts, the design department performs design in the order of submission. In this way, efficient design is possible by determining the design priority based on the submission timing of the prompt. A part or all of the above-mentioned processing in the design department may be performed using, for example, an AI, or may be performed without using an AI. For example, the design department can input prompt submission timing data to the generation AI and have the generation AI execute the determination of the design priority.

[0091] The design unit can adjust the order of design based on the relevance of the prompts during design. For example, the design unit prioritizes design for prompts with high relevance. The design unit performs design in the normal order for prompts with medium relevance. The design unit postpones design for prompts with low relevance. This allows efficient design by adjusting the order of design based on the relevance of the prompts. A part or all of the above-mentioned processing in the design unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the design unit can input relevance data of the prompts to the generation AI and cause the generation AI to adjust the order of design.

[0092] The process unit can estimate the user's emotion and adjust the layout of the production line based on the estimated user's emotion. For example, when the user is relaxed, the process unit builds an efficient production line. When the user is in a hurry, the process unit builds a quick production line. When the user is excited, the process unit builds a visually attractive production line. This allows efficient production by adjusting the layout of the production line according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the process unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the process unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0093] The process department can adjust the level of detail of the manufacturing process based on the importance of the design drawing when laying out the manufacturing line. For example, when based on an important design drawing, the process department plans a detailed manufacturing process. When based on a general design drawing, the process department plans a simplified manufacturing process. When based on a specific design drawing, the process department plans a manufacturing process with the required level of detail. This allows efficient manufacturing by adjusting the level of detail of the manufacturing process based on the importance of the design drawing. A part or all of the above-mentioned processing in the process department may be performed using, for example, AI, or may be performed without using AI. For example, the process department can input the importance data of the design drawing to the generation AI and cause the generation AI to adjust the level of detail of the manufacturing process.

[0094] The process unit can apply different layout algorithms depending on the category of the blueprint when laying out the manufacturing line. For example, the process unit applies a layout algorithm dedicated to furniture to a blueprint in the furniture category. The process unit applies a layout algorithm dedicated to clothing to a blueprint in the clothing category. The process unit applies a layout algorithm dedicated to electronic devices to a blueprint in the electronic device category. This enables efficient manufacturing by applying different layout algorithms depending on the category of the blueprint. Some or all of the above-mentioned processing in the process unit may be performed using, for example, AI, or may be performed without using AI. For example, the process unit can input category data of the blueprint to the generation AI and cause the generation AI to apply the layout algorithm.

[0095] The process unit can estimate the user's emotion and determine the priority of the production line based on the estimated user's emotion. For example, when the user is stressed, the process unit prioritizes important production processes. When the user is relaxed, the process unit prioritizes detailed production processes. When the user is in a hurry, the process unit performs the most important production process first. This enables efficient production by determining the priority of the production line according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the process unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the process unit can input the user's emotion data to the generation AI and have the generation AI execute emotion estimation.

[0096] The process department can determine the priority of the manufacturing process based on the submission time of the design drawing when laying out the manufacturing line. For example, the process department plans the manufacturing process with priority for the design drawing submitted early. For urgent design drawings, the process department plans the manufacturing process with the highest priority. For normal design drawings, the process department plans the manufacturing process in the order of submission. This enables efficient manufacturing by determining the priority of the manufacturing process based on the submission time of the design drawing. A part or all of the above-mentioned processing in the process department may be performed using, for example, AI, or may be performed without using AI. For example, the process department can input the submission time data of the design drawing to the generation AI and have the generation AI execute the determination of the priority of the manufacturing process.

[0097] The process department can adjust the order of manufacturing processes based on the relevance of the design drawings when laying out the manufacturing line. For example, the process department plans the manufacturing process with priority for a design drawing with high relevance. For a design drawing with medium relevance, the process department plans the manufacturing process in a normal order. For a design drawing with low relevance, the process department plans the manufacturing process on a later date. This enables efficient manufacturing by adjusting the order of manufacturing processes based on the relevance of the design drawings. A part or all of the above-mentioned processing in the process department may be performed using, for example, AI, or may be performed without using AI. For example, the process department can input the relevance data of the design drawing to the generation AI and cause the generation AI to adjust the order of the manufacturing process.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The reception unit can analyze the user's past purchase history and automatically suggest related prompts. For example, similar prompts can be presented based on the shape, color, and material of products the user has purchased in the past. Furthermore, it can extract features of products that the user has given high ratings to in the past and suggest new prompts based on those features. It can also analyze the usage of products the user has purchased in the past and preferentially display prompts related to frequently used products. This makes it possible to utilize the user's past purchase history to provide more personalized prompt input.

[0100] The generation unit can suggest prompts based on the user's current weather information. For example, it can suggest highly waterproof materials on rainy days and highly breathable materials on sunny days. It can also suggest appropriate shapes and colors depending on the season. It can also analyze climate data in the user's location over the long term and present prompts for products suitable for that area. This allows for prompt suggestions that take into account the user's current weather information, improving user convenience.

[0101] The design department can propose optimal designs based on the user's health data. For example, if a user has back pain, it can propose a chair design that is gentle on the back. Furthermore, it can automatically adjust optimal dimensions and shapes based on the user's height and weight. It can also analyze the user's exercise data and propose designs that reduce fatigue even when sitting for long periods of time. This allows for personalized designs that utilize the user's health data.

[0102] The process department can monitor the quality of the product in real time during the manufacturing process and adjust the manufacturing process if necessary. For example, if there is an error in the product's dimensions or shape, it can automatically correct it. Also, if a defect occurs in the material of the product, it can continue production using an alternative material. In addition, it is possible to perform quality checks at each stage of the production line and issue an alert immediately if a problem occurs. This strengthens quality control during the manufacturing process and improves product quality.

[0103] The process department can optimize energy consumption during the manufacturing process. For example, it can monitor energy consumption in real time at each stage of the manufacturing line and select a highly energy-efficient process. It can also adjust the energy supply according to the operating status of the manufacturing line to reduce wasteful energy consumption. It can also actively use renewable energy to reduce the environmental load. This allows for the optimization of energy consumption and the realization of an environmentally friendly manufacturing process.

[0104] The reception unit can estimate the user's emotion and suggest a prompt input method based on the estimated user's emotion. For example, if the user is stressed, a simple and intuitive input method can be suggested. If the user is relaxed, detailed customization options can be provided. Also, if the user is in a hurry, voice input can be suggested with priority. This provides an optimal input method according to the user's emotion, improving user convenience.

[0105] The generation unit can estimate the user's emotion and adjust the color tone of the 3D preview based on the estimated emotion of the user. For example, if the user is relaxed, a 3D preview with soft color tones is generated. If the user is excited, a 3D preview with vivid color tones is generated. Also, if the user is stressed, a 3D preview with calm color tones can be generated. This allows visual adjustments according to the user's emotion, improving the user's satisfaction.

[0106] The design unit can estimate the user's emotions and adjust the progress of the design based on the estimated user's emotions. For example, if the user is in a hurry, the design progress can be quickly advanced. If the user is relaxed, a detailed design process can be provided. Also, if the user is stressed, the design process can be simplified and the result can be provided quickly. This provides a design process according to the user's emotions, improving user convenience.

[0107] The process unit can estimate the user's emotions and adjust the speed of the manufacturing process based on the estimated user's emotions. For example, if the user is in a hurry, the manufacturing process can be accelerated. If the user is relaxed, manufacturing can be performed at a normal speed. Also, if the user is stressed, the manufacturing process can be optimized to provide the product quickly. This provides a manufacturing process that corresponds to the user's emotions, improving user satisfaction.

[0108] The process unit can estimate the user's emotion and adjust the delivery method of the product based on the estimated user's emotion. For example, if the user is in a hurry, a quick delivery option can be provided. If the user is relaxed, a normal delivery option can be provided. Also, if the user is stressed, a trackable delivery option can be provided to provide peace of mind. This provides an optimal delivery method according to the user's emotion, improving the user's satisfaction.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The reception unit inputs a prompt such as the shape, color, material, etc. of the thing the consumer wants. The prompt input by the consumer may be in the form of text, voice input, image input, etc. For example, the consumer may input a prompt such as "a red plastic chair." Step 2: The generation unit uses a generation AI to analyze the prompt input by the reception unit and generate a 3D preview. For example, the generation AI analyzes the prompt using a text generation AI (e.g., LLM) or a multimodal generation AI and generates a 3D preview. The generated 3D preview may display, for example, a 3D preview of a red plastic chair. Step 3: The design department uses design AI to design the detailed shape based on the 3D preview generated by the generation department. For example, based on the 3D preview generated by the generation AI, the dimensions and structure of the chair are designed in detail, and a detailed design drawing is created using CAD data. Step 4: The process department uses the process AI to lay out the production line based on the detailed shape designed by the design department. For example, based on the design drawings created by the design AI, the process department plans the optimal manufacturing process and builds the production line. The process AI plans the order in which each part of the chair should be manufactured and assembled.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements including the above-mentioned reception unit, generation unit, design unit, and process unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and enables the consumer to input a prompt. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D preview using a generation AI. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and designs a detailed shape using a design AI. The process unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lays out a manufacturing line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements including the above-mentioned reception unit, generation unit, design unit, and process unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and enables the consumer to voice-input a prompt. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D preview using a generation AI. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and designs a detailed shape using a design AI. The process unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lays out a manufacturing line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements including the above-mentioned reception unit, generation unit, design unit, and process unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and enables the consumer to input a prompt by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D preview using a generation AI. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and designs a detailed shape using a design AI. The process unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lays out a manufacturing line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] Each of the multiple elements including the above-mentioned reception unit, generation unit, design unit, and process unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and enables the consumer to input a prompt by voice. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a 3D preview using a generation AI. The design unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and designs a detailed shape using a design AI. The process unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and lays out a manufacturing line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) a reception unit for inputting a prompt; a generating unit that analyzes the prompt input by the receiving unit and generates a 3D preview; A design unit that designs a detailed shape based on the 3D preview generated by the generation unit; A process unit that lays out a manufacturing line based on the detailed shape designed by the design unit. A system characterized by: (Appendix 2) The generation unit is Generate 3D preview based on prompt 2. The system of claim 1, (Appendix 3) The design unit Create detailed blueprints based on 3D previews generated by generative AI 2. The system of claim 1, (Appendix 4) The process section includes: Based on the design drawings created by design AI, an efficient manufacturing process is planned and a manufacturing line is constructed. 2. The system of claim 1, (Appendix 5) The process section includes: Ship completed products to consumers' homes 2. The system of claim 1, (Appendix 6) The reception unit is Enter prompts for the shape, color, and material of what the consumer wants 2. The system of claim 1, (Appendix 7) The reception unit is To provide a method for estimating a user's emotion and customizing a prompt input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyzes the prompt input history and suggests efficient ways to input prompts to users 2. The system of claim 1, (Appendix 9) The reception unit is As you type, prompts provide suggestions based on your current projects and interests 2. The system of claim 1, (Appendix 10) The reception unit is To provide a method for estimating a user's emotion and prioritizing prompt inputs based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When typing prompts, the system prioritizes relevant suggestions based on the user's geographic location 2. The system of claim 1, (Appendix 12) The reception unit is Analyzes the user's social media activity and offers relevant suggestions as you type 2. The system of claim 1, (Appendix 13) The generation unit is Provide a method to estimate a user's emotion and adjust the display method of the 3D preview based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The generation unit is At generation time, adjust the detail of the 3D preview based on the importance of the prompt 2. The system of claim 1, (Appendix 15) The generation unit is During generation, apply different generation algorithms depending on the category of the prompt 2. The system of claim 1, (Appendix 16) The generation unit is Provide a method to estimate a user's emotion and adjust the length of the 3D preview based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The generation unit is At generation time, prioritize 3D previews based on when the prompt was submitted 2. The system of claim 1, (Appendix 18) The generation unit is At generation time, adjust the order of 3D previews based on prompt relevance 2. The system of claim 1, (Appendix 19) The design unit To provide a method for estimating a user's emotion and adjusting a detailed design method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 20) The design unit As you design, adjust the level of design detail based on how important the 3D preview is 2. The system of claim 1, (Appendix 21) The design unit At design time, apply different design algorithms depending on the category of the prompt 2. The system of claim 1, (Appendix 22) The design unit To provide a method for estimating user emotions and determining design priorities based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The design unit During design, prioritize your design based on when prompts are submitted 2. The system of claim 1, (Appendix 24) The design unit At design time, adjust the design order based on prompt relevance 2. The system of claim 1, (Appendix 25) The process section includes: To provide a method for estimating a user's emotion and adjusting a layout of a production line based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The process section includes: When laying out a manufacturing line, adjust the level of detail in the manufacturing process based on the importance of the design drawings 2. The system of claim 1, (Appendix 27) The process section includes: Apply different layout algorithms depending on the design category when laying out a manufacturing line 2. The system of claim 1, (Appendix 28) The process section includes: To provide a method for estimating a user's emotion and determining a priority order of a production line based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The process section includes: When laying out a manufacturing line, prioritize manufacturing processes based on when design drawings are submitted 2. The system of claim 1, (Appendix 30) The process section includes: When laying out a manufacturing line, adjust the sequence of manufacturing processes based on blueprint associations 2. The system of claim 1, [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit for inputting a prompt; a generating unit that analyzes the prompt input by the receiving unit and generates a 3D preview; A design unit that designs a detailed shape based on the 3D preview generated by the generation unit; A process unit that lays out a manufacturing line based on the detailed shape designed by the design unit. A system characterized by:

2. The design unit Create detailed design drawings based on 3D previews generated by generative AI 2. The system of claim 1 .

3. The process section includes: Based on design drawings created by design AI, an efficient manufacturing process is planned and a manufacturing line is constructed.

2. The system of claim 1 .

4. The process section includes: Ship completed products to consumers' homes 2. The system of claim 1 .

5. The reception unit is Enter prompts for the shape, color, and material of what the consumer wants 2. The system of claim 1 .

6. The reception unit is To provide a method for estimating a user's emotion and customizing a prompt input interface based on the estimated user's emotion.

2. The system of claim 1 .

7. The reception unit is Analyzes the prompt input history and suggests efficient ways to input prompts to users 2. The system of claim 1 .

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