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 an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

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

[0005] The system according to the embodiment aims to enable consumers to easily design and quickly manufacture what they want. [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 production line based on the detailed shape designed by the design unit. [Effects of the Invention]

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

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) In a customized product manufacturing system according to an embodiment of the present invention, consumers input their desired product as prompts (e.g., shape, color, material), which automatically outputs a 3D preview. When 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 delivered to their home within a few days. This system allows consumers to easily customize what they want and obtain it quickly. 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 for confirmation. When the consumer confirms the 3D preview and issues a command to execute, the design AI designs the detailed shape based on the 3D data. The design AI creates detailed design drawings based on the prompts. For example, it designs the chair's dimensions and structure in detail. Next, the process AI autonomously lays out the manufacturing line based on the design drawings created by the design AI. The process AI then plans the optimal manufacturing process and constructs the manufacturing line. For example, it plans the order in which each part of the chair should be manufactured and assembled. Finally, the completed product is shipped to the consumer's home. The product will arrive in the consumer's hands within a few days. For example, a red plastic chair will be delivered to the consumer's home. This makes it possible for consumers to easily customize what they want and get it in a short amount of time.

[0029] A customized product manufacturing system according to an embodiment includes a reception unit, a generation unit, a design unit, and a process unit. The reception unit inputs prompts, such as the shape, color, and material of a desired item, from a consumer. The prompts input by the consumer include, but are not limited to, text, voice input, and image input. The reception unit allows 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, for example, displays a 3D preview of a red plastic chair. The generation unit generates the 3D preview based on the prompt using the generation AI. For example, the generation AI analyzes the prompt and generates the 3D preview using a text generation AI (e.g., LLM). The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. 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 chair's dimensions and structure in detail based on the 3D preview generated by the generation AI. The design department uses a design AI to create detailed design drawings. For example, the design AI creates detailed design drawings using CAD data. The processing department uses the process AI to layout a production line based on the detailed shapes designed by the design department. For example, the processing department plans an optimal manufacturing process based on the design drawings created by the design AI and builds a production line. The processing department uses the process AI to layout the production line. For example, the process AI plans the order in which each part of a chair should be manufactured and assembled. This allows the customized product manufacturing system according to the embodiment to enable consumers to easily customize what they want and obtain it in a short period of time. Some or all of the above-described processing in the processing department may be performed, for example, using AI, or may be performed without using AI. For example, the processing department can layout a production line using an AI model that inputs the detailed shapes designed by the design department and outputs the layout of the production line.

[0030] The reception unit inputs prompts such as the shape, color, and material of the item desired by the consumer. Examples of prompts input by the consumer include, but are not limited to, text, voice input, and image input. The reception unit allows the consumer to input a prompt such as "a red plastic chair." Specifically, the reception unit provides an interface compatible with various input formats and is designed to be intuitive for consumers. For text input, the consumer can enter detailed specifications using a keyboard. For voice input, the system uses voice recognition technology to convert the consumer's request into text. For image input, the consumer uploads a reference image, and the system analyzes the image. This allows the consumer to specifically communicate their image and accurately understand their request. Furthermore, the reception unit can analyze the prompt input by the consumer in real time and request additional information as needed. For example, if a consumer inputs "a red chair," the system can ask an additional question such as "What material would you like it made of?" 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, the next step.

[0031] The generation unit uses a generation AI to analyze the prompt entered by the reception unit and generate a 3D preview. The generated 3D preview, for example, displays a 3D preview of a red plastic chair. The generation unit uses the generation AI to generate the 3D preview based on the prompt. For example, the generation AI analyzes the prompt using a text generation AI (e.g., LLM) and generates the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. Specifically, the generation AI analyzes text, image, and audio data entered by the consumer and generates a 3D model based on that data. For example, the text generation AI analyzes the prompt "red plastic chair" entered 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, images, and audio, to generate a more accurate 3D preview. The generated 3D preview is displayed in an interactive viewer for the consumer to view, allowing the consumer to rotate the model and zoom in and out to view details. 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'd like the backrest to be a little higher," the generator AI will reflect that feedback and update the 3D preview. This allows the generator to flexibly respond to consumer requests and finalize the final product image.

[0032] The design department uses design AI to design the detailed shape based on the 3D preview generated by the generation department. For example, the design department designs the chair's dimensions and structure in detail based on the 3D preview generated by the generation AI. The design department then uses design AI to create detailed blueprints. For example, the design AI uses CAD data to create detailed blueprints. Specifically, the design AI analyzes the 3D preview provided by the generation department and creates detailed designs by taking into account the dimensions, shape, and material properties of each product part. The design AI can automatically generate precise blueprints in conjunction with CAD software. For example, it calculates the strength of a chair's legs and selects appropriate materials and dimensions. The design AI also takes into account the product's assembly and manufacturing processes to create an optimal design. For example, it designs the joints so that each part of the chair can be assembled efficiently. Furthermore, the design department also has the ability to modify designs based on consumer feedback. For example, if a consumer inputs a request such as "I wish the seat was 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. By working closely with the production department, the design department can create designs that accurately reflect consumer demands, thereby improving product quality and customer satisfaction.

[0033] The process department uses process AI to lay out the production line based on the detailed shapes designed by the design department. For example, the process department plans the optimal manufacturing process based on the design drawings created by the design AI and builds the production line. The process department also uses process AI to lay out the production line. For example, the process AI plans the order in which each chair part should be manufactured and assembled. Specifically, the process AI analyzes the detailed design drawings provided by the design department and optimizes the manufacturing process for each product part. The process AI optimizes the layout of manufacturing equipment and work procedures to build an efficient production line. For example, it plans the order in which each part of a chair, such as the legs, seat, and backrest, should be manufactured and assembled. The process AI also simulates the manufacturing process and verifies the optimal manufacturing procedure. This maximizes the efficiency of the production line and improves product quality. Furthermore, the process department is equipped with real-time monitoring and feedback functions for the manufacturing process. For example, it can detect abnormalities and problems that occur on the production line and respond quickly to them, ensuring the stability and reliability of the manufacturing process. The process department works closely with the design department to realize the optimal manufacturing process based on the design drawings, thereby enabling the rapid provision of 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, for example, uses a generation AI to generate the 3D preview based on the prompt. The generation unit, for example, uses a generation AI to generate the 3D preview based on the prompt. For example, the generation AI analyzes the prompt using a text generation AI (e.g., 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 can also generate the 3D preview based on the prompt using a generation AI. For example, the generation AI analyzes the prompt using a text generation AI (e.g., LLM) to generate the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. In this way, generating a 3D preview based on the prompt allows a consumer to visually confirm it.

[0035] The design department can create a detailed design drawing based on the 3D preview generated by the generation AI. The design department, for example, creates a detailed design drawing based on the 3D preview generated by the generation AI. 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 production line based on the design drawings created by the design AI. The process department can plan an efficient manufacturing process and build a production line based on the design drawings created by the design AI. For example, the process AI plans the order in which each part of a chair will be manufactured and assembled. The process department can use the design AI to layout the production line. For example, the process AI plans the order in which each part of a chair will be manufactured and assembled. The process department can use the design AI to layout the production line. For example, the process AI plans the order in which each part of a chair will be manufactured and assembled. In this way, efficient manufacturing is possible by planning an efficient manufacturing process and building a production line based on the design drawings created by the design AI.

[0037] The processing department can ship the completed manufactured product to the consumer's home. The processing department, for example, ships the completed manufactured product to the consumer's home. The processing department can ship the completed manufactured product to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing department can ship the completed manufactured product to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing department can ship the completed manufactured product to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. By shipping the completed manufactured product to the consumer's home, the consumer can receive the product quickly.

[0038] The reception unit allows the consumer to input a prompt for the shape, color, and material of the product they want. The reception unit, for example, inputs a prompt for the shape, color, and material of the product they want. The reception unit inputs a prompt for the shape, color, and material of the product they want. For example, the reception unit allows 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 product they want. For example, the reception unit allows 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 product they want. For example, the reception unit allows 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 for the shape, color, and material of the product they want.

[0039] The reception unit can analyze 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 has frequently input in the past as candidates. The reception unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit predicts and suggests prompts to be used in specific time periods based on the user's past input history. In this way, by analyzing the past prompt input history, it is possible to suggest the optimal prompt input method to the user. 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 past prompt input history to a generation AI and have the generation AI suggest the optimal prompt input method.

[0040] When inputting a prompt, the reception unit can present input candidates based on the user's current project or area of ​​interest. For example, the reception unit displays shapes, colors, and materials related to the user's current project as candidates. The reception unit proposes related prompts based on the user's area of ​​interest. The reception unit proposes optimal prompts based on projects in which the user has previously shown interest. This makes user input more efficient by presenting input candidates based on the user's current project or area of ​​interest. Some or all of the above-described 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 data on the user's current project or area of ​​interest into the generation AI and cause the generation AI to present related input candidates.

[0041] When a user inputs a prompt, the reception unit can prioritize highly relevant input candidates based on the user's geographical location information. For example, if the user is in a specific area, the reception unit displays shapes, colors, and materials related to that area as candidates. The reception unit suggests the most appropriate prompt based on the user's current location. The reception unit suggests related prompts based on places the user has previously visited. This makes user input more efficient by presenting highly relevant input candidates based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to a 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 when entering a prompt and present related input candidates. For example, the reception unit displays related shapes, colors, and materials as candidates based on 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 activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to present related input candidates.

[0043] The generation unit can adjust the level of detail of the 3D preview based on the importance of the prompt during generation. For example, the generation unit generates a detailed 3D preview based on an important prompt. The generation unit generates a simplified 3D preview based on a general prompt. The generation unit generates a 3D preview with a required level of detail based on a specific prompt. By adjusting the level of detail of the 3D preview based on the importance of the prompt, a preview that meets the user's needs is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit may input prompt importance data to the generation AI and cause the generation AI to adjust the level of detail of the 3D preview.

[0044] During generation, the generation unit can apply different generation algorithms depending on the category of the prompt. 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-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs category data of the prompt to the generation AI and causes the generation AI to apply the generation algorithm.

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

[0046] The generation unit can adjust the order of 3D previews based on the relevance of the prompts during generation. For example, the generation unit generates 3D previews with priority 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 enables efficient preview generation by adjusting the order of 3D previews based on the relevance of the prompts. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input prompt relevance data to the generation AI and cause the generation AI to adjust the order of the 3D previews.

[0047] During design, the design department can adjust the level of detail of the design based on the importance of the 3D preview. For example, the design department may create a detailed design based on an important 3D preview. The design department may create a simplified design based on a general 3D preview. The design department may create a design with the required level of detail based on a specific 3D preview. This enables efficient design by adjusting the level of detail of the design based on the importance of the 3D preview. Some or all of the above-described processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department may input the importance data of the 3D preview into the generation AI and have the generation AI adjust the level of detail of the design.

[0048] During design, the design unit can apply different design algorithms depending on the category of the prompt. 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 prompt category. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input prompt category data into a generation AI and have the generation AI apply a design algorithm.

[0049] During design, the design department can determine design priorities based on the timing of prompt submission. For example, the design department prioritizes design for prompts submitted early. The design department prioritizes design for urgent prompts. The design department designs for normal prompts in the order of submission. In this way, efficient design is possible by determining design priorities based on the timing of prompt submission. Some or all of the above-described processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input prompt submission time data into the generation AI and have the generation AI determine the design priorities.

[0050] The design unit can adjust the design order 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. In this way, adjusting the design order based on the relevance of the prompts enables efficient design. Some or all of the above-described processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input prompt relevance data to the generation AI and have the generation AI adjust the design order.

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

[0052] The processing unit can apply different layout algorithms depending on the category of the blueprint when laying out the production line. For example, the processing unit applies a layout algorithm dedicated to furniture to blueprints in the furniture category. The processing unit applies a layout algorithm dedicated to clothing to blueprints in the clothing category. The processing unit applies a layout algorithm dedicated to electronic devices to blueprints 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 processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input blueprint category data into a generation AI and have the generation AI apply a layout algorithm.

[0053] When laying out a production line, the production department can determine the priority of production processes based on the submission date of the design drawings. For example, the production department prioritizes the production process for design drawings that are submitted early. For urgent design drawings, the production department prioritizes the production process for them. For regular design drawings, the production department plans the production process in the order of submission. This enables efficient production by determining the priority of production processes based on the submission date of the design drawings. Some or all of the above-mentioned processing in the production department may be performed using, or without, AI. For example, the production department can input design drawing submission date data into a generation AI and have the generation AI determine the priority of production processes.

[0054] When laying out a production line, the processing department can adjust the order of manufacturing processes based on the relevance of the design drawings. For example, the processing department prioritizes planning of manufacturing processes for design drawings with high relevance. For design drawings with medium relevance, the processing department plans manufacturing processes in the normal order. For design drawings with low relevance, the processing department postpones planning of manufacturing processes. In this way, adjusting the order of manufacturing processes based on the relevance of the design drawings enables efficient manufacturing. Some or all of the above-mentioned processing in the processing department may be performed using, for example, AI, or may be performed without using AI. For example, the processing department can input relevance data of the design drawings into a generation AI and have the generation AI adjust the order of manufacturing processes.

[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, it can present similar prompts based on the shape, color, and material of products the user has previously purchased. It can also extract features of products that the user has previously given high ratings to and suggest new prompts based on these. It can also analyze the usage of products the user has previously purchased and prioritize 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 breathable materials on sunny days. It can also suggest appropriate shapes and colors depending on the season. It can also analyze weather data for the user's location over the long term and present prompts for products suitable for that area. This allows for prompt suggestions that take the user's current weather information into account, improving user convenience.

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

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

[0060] The processing department can optimize energy consumption during the manufacturing process. For example, it can monitor energy consumption at each stage of the manufacturing line in real time and select the most energy-efficient process. It can also adjust energy supply according to the operating status of the manufacturing line to reduce unnecessary energy consumption. It can also actively use renewable energy to reduce environmental impact. This optimizes energy consumption and realizes an environmentally friendly manufacturing process.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit inputs prompts such as the shape, color, and material of the item the consumer wants. The prompts input by the consumer can be in text format, voice input, image input, etc. For example, the consumer can input a prompt such as "a red plastic chair." Step 2: The generation unit uses a generation AI to analyze the prompt entered by the reception unit and generate a 3D preview. For example, the generation AI may analyze the prompt using a text generation AI (e.g., LLM) or a multimodal generation AI and generate 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 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 a customized product manufacturing system according to an embodiment of the present invention, consumers input their desired product as prompts (e.g., shape, color, material), which automatically outputs a 3D preview. When 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 delivered to their home within a few days. This system allows consumers to easily customize what they want and obtain it quickly. 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 for confirmation. When the consumer confirms the 3D preview and issues a command to execute, the design AI designs the detailed shape based on the 3D data. The design AI creates detailed design drawings based on the prompts. For example, it designs the chair's dimensions and structure in detail. Next, the process AI autonomously lays out the manufacturing line based on the design drawings created by the design AI. The process AI then plans the optimal manufacturing process and constructs the manufacturing line. For example, it plans the order in which each part of the chair should be manufactured and assembled. Finally, the completed product is shipped to the consumer's home. The product will arrive in the consumer's hands within a few days. For example, a red plastic chair will be delivered to the consumer's home. This makes it possible for consumers to easily customize what they want and get it in a short amount of time.

[0064] A customized product manufacturing system according to an embodiment includes a reception unit, a generation unit, a design unit, and a process unit. The reception unit inputs prompts, such as the shape, color, and material of a desired item, from a consumer. The prompts input by the consumer include, but are not limited to, text, voice input, and image input. The reception unit allows 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, for example, displays a 3D preview of a red plastic chair. The generation unit generates the 3D preview based on the prompt using the generation AI. For example, the generation AI analyzes the prompt and generates the 3D preview using a text generation AI (e.g., LLM). The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. 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 chair's dimensions and structure in detail based on the 3D preview generated by the generation AI. The design department uses a design AI to create detailed design drawings. For example, the design AI creates detailed design drawings using CAD data. The processing department uses the process AI to layout a production line based on the detailed shapes designed by the design department. For example, the processing department plans an optimal manufacturing process based on the design drawings created by the design AI and builds a production line. The processing department uses the process AI to layout the production line. For example, the process AI plans the order in which each part of a chair should be manufactured and assembled. This allows the customized product manufacturing system according to the embodiment to enable consumers to easily customize what they want and obtain it in a short period of time. Some or all of the above-described processing in the processing department may be performed, for example, using AI, or may be performed without using AI. For example, the processing department can layout a production line using an AI model that inputs the detailed shapes designed by the design department and outputs the layout of the production line.

[0065] The reception unit inputs prompts such as the shape, color, and material of the item desired by the consumer. Examples of prompts input by the consumer include, but are not limited to, text, voice input, and image input. The reception unit allows the consumer to input a prompt such as "a red plastic chair." Specifically, the reception unit provides an interface compatible with various input formats and is designed to be intuitive for consumers. For text input, the consumer can enter detailed specifications using a keyboard. For voice input, the system uses voice recognition technology to convert the consumer's request into text. For image input, the consumer uploads a reference image, and the system analyzes the image. This allows the consumer to specifically communicate their image and accurately understand their request. Furthermore, the reception unit can analyze the prompt input by the consumer in real time and request additional information as needed. For example, if a consumer inputs "a red chair," the system can ask an additional question such as "What material would you like it made of?" 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, the next step.

[0066] The generation unit uses a generation AI to analyze the prompt entered by the reception unit and generate a 3D preview. The generated 3D preview, for example, displays a 3D preview of a red plastic chair. The generation unit uses the generation AI to generate the 3D preview based on the prompt. For example, the generation AI analyzes the prompt using a text generation AI (e.g., LLM) and generates the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. Specifically, the generation AI analyzes text, image, and audio data entered by the consumer and generates a 3D model based on that data. For example, the text generation AI analyzes the prompt "red plastic chair" entered 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, images, and audio, to generate a more accurate 3D preview. The generated 3D preview is displayed in an interactive viewer for the consumer to view, allowing the consumer to rotate the model and zoom in and out to view details. 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'd like the backrest to be a little higher," the generator AI will reflect that feedback and update the 3D preview. This allows the generator to flexibly respond to consumer requests and finalize the final product image.

[0067] The design department uses design AI to design the detailed shape based on the 3D preview generated by the generation department. For example, the design department designs the chair's dimensions and structure in detail based on the 3D preview generated by the generation AI. The design department then uses design AI to create detailed blueprints. For example, the design AI uses CAD data to create detailed blueprints. Specifically, the design AI analyzes the 3D preview provided by the generation department and creates detailed designs by taking into account the dimensions, shape, and material properties of each product part. The design AI can automatically generate precise blueprints in conjunction with CAD software. For example, it calculates the strength of a chair's legs and selects appropriate materials and dimensions. The design AI also takes into account the product's assembly and manufacturing processes to create an optimal design. For example, it designs the joints so that each part of the chair can be assembled efficiently. Furthermore, the design department also has the ability to modify designs based on consumer feedback. For example, if a consumer inputs a request such as "I wish the seat was 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. By working closely with the production department, the design department can create designs that accurately reflect consumer demands, thereby improving product quality and customer satisfaction.

[0068] The process department uses process AI to lay out the production line based on the detailed shapes designed by the design department. For example, the process department plans the optimal manufacturing process based on the design drawings created by the design AI and builds the production line. The process department also uses process AI to lay out the production line. For example, the process AI plans the order in which each chair part should be manufactured and assembled. Specifically, the process AI analyzes the detailed design drawings provided by the design department and optimizes the manufacturing process for each product part. The process AI optimizes the layout of manufacturing equipment and work procedures to build an efficient production line. For example, it plans the order in which each part of a chair, such as the legs, seat, and backrest, should be manufactured and assembled. The process AI also simulates the manufacturing process and verifies the optimal manufacturing procedure. This maximizes the efficiency of the production line and improves product quality. Furthermore, the process department is equipped with real-time monitoring and feedback functions for the manufacturing process. For example, it can detect abnormalities and problems that occur on the production line and respond quickly to them, ensuring the stability and reliability of the manufacturing process. The process department works closely with the design department to realize the optimal manufacturing process based on the design drawings, thereby enabling the rapid provision of 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, for example, uses a generation AI to generate the 3D preview based on the prompt. The generation unit, for example, uses a generation AI to generate the 3D preview based on the prompt. For example, the generation AI analyzes the prompt using a text generation AI (e.g., 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 can also generate the 3D preview based on the prompt using a generation AI. For example, the generation AI analyzes the prompt using a text generation AI (e.g., LLM) to generate the 3D preview. The generation unit can also generate the 3D preview based on the prompt using a multimodal generation AI. In this way, generating a 3D preview based on the prompt allows a consumer to visually confirm it.

[0070] The design department can create a detailed design drawing based on the 3D preview generated by the generation AI. The design department, for example, creates a detailed design drawing based on the 3D preview generated by the generation AI. 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 production line based on the design drawings created by the design AI. The process department can plan an efficient manufacturing process and build a production line based on the design drawings created by the design AI. For example, the process AI plans the order in which each part of a chair will be manufactured and assembled. The process department can use the design AI to layout the production line. For example, the process AI plans the order in which each part of a chair will be manufactured and assembled. The process department can use the design AI to layout the production line. For example, the process AI plans the order in which each part of a chair will be manufactured and assembled. In this way, efficient manufacturing is possible by planning an efficient manufacturing process and building a production line based on the design drawings created by the design AI.

[0072] The processing department can ship the completed manufactured product to the consumer's home. The processing department, for example, ships the completed manufactured product to the consumer's home. The processing department can ship the completed manufactured product to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing department can ship the completed manufactured product to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. The processing department can ship the completed manufactured product to the consumer's home. For example, a red plastic chair is delivered to the consumer's home. By shipping the completed manufactured product to the consumer's home, the consumer can receive the product quickly.

[0073] The reception unit allows the consumer to input a prompt for the shape, color, and material of the product they want. The reception unit, for example, inputs a prompt for the shape, color, and material of the product they want. The reception unit inputs a prompt for the shape, color, and material of the product they want. For example, the reception unit allows 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 product they want. For example, the reception unit allows 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 product they want. For example, the reception unit allows 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 for the shape, color, and material of the product they want.

[0074] The reception unit can estimate the user's emotions and customize the prompt input interface based on the estimated user emotions. For example, when the user is stressed, the reception unit provides a simple and intuitive interface and minimizes input steps. 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 prompt input quickly. This improves user convenience by customizing the prompt input interface according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0075] The reception unit can analyze 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 has frequently input in the past as candidates. The reception unit prioritizes suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit predicts and suggests prompts to be used in specific time periods based on the user's past input history. In this way, by analyzing the past prompt input history, it is possible to suggest the optimal prompt input method to the user. 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 past prompt input history to a generation AI and have the generation AI suggest the optimal prompt input method.

[0076] When inputting a prompt, the reception unit can present input candidates based on the user's current project or area of ​​interest. For example, the reception unit displays shapes, colors, and materials related to the user's current project as candidates. The reception unit proposes related prompts based on the user's area of ​​interest. The reception unit proposes optimal prompts based on projects in which the user has previously shown interest. This makes user input more efficient by presenting input candidates based on the user's current project or area of ​​interest. Some or all of the above-described 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 data on the user's current project or area of ​​interest into the generation AI and cause the generation AI to present related input candidates.

[0077] The reception unit can estimate the user's emotions and determine the priority of prompt inputs based on the estimated user emotions. For example, if the user is stressed, the reception unit prioritizes input of important prompts. If the user is relaxed, the reception unit prioritizes input of detailed prompts. If the user is in a hurry, the reception unit prioritizes input of the most important prompts first. This improves user convenience by determining the priority of prompt inputs according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's emotion data into the generation AI and cause the generation AI to perform emotion estimation.

[0078] When a user inputs a prompt, the reception unit can prioritize highly relevant input candidates based on the user's geographical location information. For example, if the user is in a specific area, the reception unit displays shapes, colors, and materials related to that area as candidates. The reception unit suggests the most appropriate prompt based on the user's current location. The reception unit suggests related prompts based on places the user has previously visited. This makes user input more efficient by presenting highly relevant input candidates based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's geographical location information to a 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 when entering a prompt and present related input candidates. For example, the reception unit displays related shapes, colors, and materials as candidates based on 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 activity. Some or all of the above-described processing by the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the user's social media activity data into a generation AI and cause the generation AI to present related input candidates.

[0080] The generation unit can estimate the user's emotions and adjust the display method of the 3D preview based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates a 3D preview that progresses at a leisurely pace. If the user is in a hurry, the generation unit generates a 3D preview that emphasizes the shortest route. If the user is excited, the generation unit generates a 3D preview that adds a visually stimulating effect. This improves user convenience by adjusting the display method of the 3D preview according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the 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 into the generation AI and cause the generation AI to estimate the emotion.

[0081] The generation unit can adjust the level of detail of the 3D preview based on the importance of the prompt during generation. For example, the generation unit generates a detailed 3D preview based on an important prompt. The generation unit generates a simplified 3D preview based on a general prompt. The generation unit generates a 3D preview with a required level of detail based on a specific prompt. By adjusting the level of detail of the 3D preview based on the importance of the prompt, a preview that meets the user's needs is provided. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit may input prompt importance data to the generation AI and cause the generation AI to adjust the level of detail of the 3D preview.

[0082] During generation, the generation unit can apply different generation algorithms depending on the category of the prompt. 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-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit inputs category data of the prompt to the generation AI and causes 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 emotion. For example, if the user is in a hurry, the generation unit generates a short, to-the-point 3D preview. If the user is relaxed, the generation unit generates a longer 3D preview with detailed explanations. If the user is excited, the generation unit generates a 3D preview with visually stimulating effects. This improves user convenience by adjusting the length of the 3D preview according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the 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 into the generation AI and cause the generation AI to estimate the emotion.

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

[0085] The generation unit can adjust the order of 3D previews based on the relevance of the prompts during generation. For example, the generation unit generates 3D previews with priority 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 enables efficient preview generation by adjusting the order of 3D previews based on the relevance of the prompts. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input prompt relevance data 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 emotions and adjust the detailed design method based on the estimated user emotions. For example, if the user is relaxed, the design unit performs a detailed design. If the user is in a hurry, the design unit performs a simplified design. If the user is excited, the design unit performs a visually appealing design. This allows for a design that meets the user's needs by adjusting the detailed design method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the design unit may be performed using an AI, or may be performed without using an AI. For example, the design unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0087] During design, the design department can adjust the level of detail of the design based on the importance of the 3D preview. For example, the design department may create a detailed design based on an important 3D preview. The design department may create a simplified design based on a general 3D preview. The design department may create a design with the required level of detail based on a specific 3D preview. This enables efficient design by adjusting the level of detail of the design based on the importance of the 3D preview. Some or all of the above-described processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department may input the importance data of the 3D preview into the generation AI and have the generation AI adjust the level of detail of the design.

[0088] During design, the design unit can apply different design algorithms depending on the category of the prompt. 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 prompt category. Some or all of the above-mentioned processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input prompt category data into a generation AI and have the generation AI apply a design algorithm.

[0089] The design unit can estimate the user's emotions and determine design priorities based on the estimated user emotions. For example, if the user is stressed, the design unit prioritizes important design. If the user is relaxed, the design unit prioritizes detailed design. If the user is in a hurry, the design unit performs the most important design first. This enables design that meets the user's needs by determining design priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the design unit may be performed using AI, or may be performed without AI. For example, the design unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0090] During design, the design department can determine design priorities based on the timing of prompt submission. For example, the design department prioritizes design for prompts submitted early. The design department prioritizes design for urgent prompts. The design department designs for normal prompts in the order of submission. In this way, efficient design is possible by determining design priorities based on the timing of prompt submission. Some or all of the above-described processing in the design department may be performed using, for example, AI, or may be performed without using AI. For example, the design department can input prompt submission time data into the generation AI and have the generation AI determine the design priorities.

[0091] The design unit can adjust the design order 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. In this way, adjusting the design order based on the relevance of the prompts enables efficient design. Some or all of the above-described processing in the design unit may be performed using, for example, AI, or may be performed without using AI. For example, the design unit can input prompt relevance data to the generation AI and have the generation AI adjust the design order.

[0092] The processing unit can estimate the user's emotions and adjust the layout of the production line based on the estimated user's emotions. For example, if the user is relaxed, the processing unit builds an efficient production line. If the user is in a hurry, the processing unit builds a fast production line. If the user is excited, the processing unit builds a visually appealing production line. This enables efficient production by adjusting the production line layout according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the processing unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

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

[0094] The processing unit can apply different layout algorithms depending on the category of the blueprint when laying out the production line. For example, the processing unit applies a layout algorithm dedicated to furniture to blueprints in the furniture category. The processing unit applies a layout algorithm dedicated to clothing to blueprints in the clothing category. The processing unit applies a layout algorithm dedicated to electronic devices to blueprints 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 processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the processing unit can input blueprint category data into a generation AI and have the generation AI apply a layout algorithm.

[0095] The processing unit can estimate the user's emotions and determine the priorities of the production line based on the estimated user's emotions. For example, if the user is stressed, the processing unit prioritizes important production processes. If the user is relaxed, the processing unit prioritizes detailed production processes. If the user is in a hurry, the processing unit performs the most important production process first. This enables efficient production by determining the priorities of the production line according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the processing unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation.

[0096] When laying out a production line, the production department can determine the priority of production processes based on the submission date of the design drawings. For example, the production department prioritizes the production process for design drawings that are submitted early. For urgent design drawings, the production department prioritizes the production process for them. For regular design drawings, the production department plans the production process in the order of submission. This enables efficient production by determining the priority of production processes based on the submission date of the design drawings. Some or all of the above-mentioned processing in the production department may be performed using, or without, AI. For example, the production department can input design drawing submission date data into a generation AI and have the generation AI determine the priority of production processes.

[0097] When laying out a production line, the processing department can adjust the order of manufacturing processes based on the relevance of the design drawings. For example, the processing department prioritizes planning of manufacturing processes for design drawings with high relevance. For design drawings with medium relevance, the processing department plans manufacturing processes in the normal order. For design drawings with low relevance, the processing department postpones planning of manufacturing processes. In this way, adjusting the order of manufacturing processes based on the relevance of the design drawings enables efficient manufacturing. Some or all of the above-mentioned processing in the processing department may be performed using, for example, AI, or may be performed without using AI. For example, the processing department can input relevance data of the design drawings into a generation AI and have the generation AI adjust the order of manufacturing processes.

[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, it can present similar prompts based on the shape, color, and material of products the user has previously purchased. It can also extract features of products that the user has previously given high ratings to and suggest new prompts based on these. It can also analyze the usage of products the user has previously purchased and prioritize 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 breathable materials on sunny days. It can also suggest appropriate shapes and colors depending on the season. It can also analyze weather data for the user's location over the long term and present prompts for products suitable for that area. This allows for prompt suggestions that take the user's current weather information into account, improving user convenience.

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

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

[0103] The processing department can optimize energy consumption during the manufacturing process. For example, it can monitor energy consumption at each stage of the manufacturing line in real time and select the most energy-efficient process. It can also adjust energy supply according to the operating status of the manufacturing line to reduce unnecessary energy consumption. It can also actively use renewable energy to reduce environmental impact. This optimizes energy consumption and realizes an environmentally friendly manufacturing process.

[0104] The reception unit can estimate the user's emotions and suggest a prompt input method based on the estimated user emotions. For example, if the user is feeling 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 as a priority. This provides the optimal input method according to the user's emotions, improving user convenience.

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

[0106] The design unit can estimate the user's emotions and adjust the design progress based on the estimated user emotions. For example, if the user is in a hurry, the design progress can be expedited. 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 results can be provided quickly. This provides a design process that corresponds to the user's emotions, improving user convenience.

[0107] The production department 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 production process can be accelerated. If the user is relaxed, production can be carried out at a normal speed. Also, if the user is feeling stressed, the production process can be optimized to quickly deliver the product. This provides a manufacturing process that corresponds to the user's emotions, improving user satisfaction.

[0108] The processing unit can estimate the user's emotions and adjust the product delivery method based on the estimated user emotions. For example, if the user is in a hurry, a fast delivery option can be provided. If the user is relaxed, a standard delivery option can be provided. Also, if the user is stressed, a trackable delivery option can be provided to give the user peace of mind. This allows the optimal delivery method to be provided according to the user's emotions, thereby improving user satisfaction.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The reception unit inputs prompts such as the shape, color, and material of the item the consumer wants. The prompts input by the consumer can be in text format, voice input, image input, etc. For example, the consumer can input a prompt such as "a red plastic chair." Step 2: The generation unit uses a generation AI to analyze the prompt entered by the reception unit and generate a 3D preview. For example, the generation AI may analyze the prompt using a text generation AI (e.g., LLM) or a multimodal generation AI and generate 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 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[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 engine).<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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but are not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or in whole by AI, but are not limited to these 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 processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[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 allows 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 production line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications 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 a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

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

[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 carried out in a secure state.

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[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, allowing the consumer to input prompts 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 detailed shapes 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 production line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 audio according to instructions from the processor 46.

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

[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 carried out 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[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, allowing the consumer to input prompts 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 production line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications 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, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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. 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 audio according to instructions from the processor 46.

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

[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 carried out in a secure state.

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

[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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[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. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[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, allowing the consumer to input prompts 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 production line using a process AI. The correspondence between each unit and the device or control unit is not limited to the above example, and various modifications are possible.

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

[0165] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

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

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

[0168] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

[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 USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[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] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

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

[0178] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

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

[0180] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

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

[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 Generate a 3D preview based on a 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 unit comprises: Based on blueprints 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 unit comprises: Ship completed products to consumers' homes 2. The system of claim 1. (Appendix 6) The reception unit Enter prompts for the shape, color, and material of what the consumer wants 2. The system of claim 1. (Appendix 7) The reception unit 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 Analyzes past prompt input history and suggests efficient prompt input methods to users 2. The system of claim 1. (Appendix 9) The reception unit As you type, prompts offer suggestions based on your current projects and interests 2. The system of claim 1. (Appendix 10) The reception unit To provide a method for estimating a user's emotion and determining the priority of prompt inputs based on the estimated user's emotion. 2. The system of claim 1. (Appendix 11) The reception unit 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 Analyzes your social media activity and offers relevant suggestions as you type 2. The system of claim 1. (Appendix 13) The generation unit Provide a method to estimate the user's emotions and adjust the display method of the 3D preview based on the estimated user emotions. 2. The system of claim 1. (Appendix 14) The generation unit At generation time, adjust the detail level of the 3D preview based on the importance of the prompt 2. The system of claim 1. (Appendix 15) The generation unit During generation, apply different generation algorithms depending on the category of the prompt 2. The system of claim 1. (Appendix 16) The generation unit Provide a method to estimate the user's emotion and adjust the length of the 3D preview based on the estimated user emotion. 2. The system of claim 1. (Appendix 17) The generation unit At generation time, prioritize 3D previews based on when prompts were submitted 2. The system of claim 1. (Appendix 18) The generation unit 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 Adjust the level of design detail based on the importance of the 3D preview as you design 2. The system of claim 1. (Appendix 21) The design unit At design time, apply different design algorithms depending on the category of 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 unit comprises: To provide a method for estimating a user's emotion and adjusting the layout of a production line based on the estimated user's emotion. 2. The system of claim 1. (Appendix 26) The process unit comprises: When laying out a manufacturing line, adjust the level of detail in the manufacturing process based on the importance of the blueprints 2. The system of claim 1. (Appendix 27) The process unit comprises: 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 unit comprises: To provide a method for estimating user emotions and determining priorities of production lines based on the estimated user emotions. 2. The system of claim 1. (Appendix 29) The process unit comprises: When laying out a manufacturing line, prioritize manufacturing processes based on the timing of blueprint submissions 2. The system of claim 1. (Appendix 30) The process unit comprises: 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 that receives a prompt input by a user; a generating unit that analyzes the prompt received 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 processing unit that lays out a manufacturing line based on the detailed shape designed by the design unit, The reception unit The system estimates the user's emotion using an emotion identification model, and provides a simple input interface with minimal input steps if the estimated user's emotion is stressed, and an input interface including detailed input options if the estimated user's emotion is relaxed. A system characterized by:

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

3. The process unit comprises: Based on design drawings created by design AI, an efficient manufacturing process is planned and a manufacturing line is constructed. The system of claim 1 .

4. The process unit comprises: Ship completed products to consumers' homes The system of claim 1 .

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

6. The reception unit Analyzes past prompt input history and suggests efficient prompt input methods to users The system of claim 1 .

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