System

Abstract

An object of a system according to an embodiment is to create a 3D model that fits Cinderella based on a request from a customer and to perform 3D printing.SOLUTION: A system according to an embodiment includes a 3D model acquiring unit, a request collecting unit, a 3D model creating unit, and a 3D printing unit. The 3D model acquiring unit acquires a 3D model of a commodity. The request collection unit collects requests from customers. The 3D model creation unit creates a 3D model that fits Cinderella based on the requests collected by the request collection unit. The 3D printing unit performs 3D printing of the 3D model created by the 3D model creation 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] With conventional technology, product accessories did not meet the customer's detailed requests, resulting in low customer satisfaction.

[0005] The system of the embodiment aims to create and 3D print a perfect fit 3D model based on a customer request. [Means for solving the problem]

[0006] The system according to the embodiment includes a 3D model acquisition unit, a request collection unit, a 3D model creation unit, and a 3D printing unit. The 3D model acquisition unit acquires a 3D model of a product. The request collection unit collects customer requests. The 3D model creation unit creates a 3D model that fits perfectly based on the requests collected by the request collection unit. The 3D printing unit 3D prints the 3D model created by the 3D model creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can create and 3D print a perfect fit 3D model based on the customer's request. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) A 3D printing service according to an embodiment of the present invention is a system that acquires a 3D model of a product, uses a generation AI to create a perfect-fit 3D model based on a customer's request, and sells the 3D-printed product. This allows the 3D printing service to provide accessories that meet customer needs.

[0029] A 3D printing service according to an embodiment includes a 3D model acquisition unit, a request collection unit, a 3D model creation unit, and a 3D printing unit. The 3D model acquisition unit acquires 3D models of products. For example, it uses data scanned from existing products or data provided by manufacturers. The 3D model acquisition unit can also acquire 3D models of various products, such as smartphone cases and furniture parts. The request collection unit collects customer requests. For example, it collects requests provided in the form of text or illustrations. The request collection unit can also collect specific requests, such as "I want a specific design added to this smartphone case" or "I want this furniture part resized to a specific size." The 3D model creation unit creates a perfect 3D model based on the requests collected by the request collection unit. For example, the generation AI can interpret instructions in text, such as "I want this part to be a little bigger," and modify the 3D model accordingly. The generation AI can also add designs provided in illustrations to the 3D model. The generation AI generates a 3D model based on user instructions using a text generation AI (e.g., LLM) or a multimodal generation AI. The 3D printing unit 3D prints the 3D model created by the 3D model creation unit. For example, it manufactures smartphone cases with specific designs or furniture parts adjusted to specific sizes. This allows the 3D printing service according to the embodiment to provide accessories that meet customer needs. For example, even if a customer is particular about a specific design or size and is not satisfied with ready-made products, they can use this service to obtain a product that completely satisfies them.

[0030] The 3D model acquisition unit uses generative AI to automatically optimize the 3D model of a product, generating a highly accurate model while reducing data volume. The 3D model acquisition unit, for example, uses generative AI to automatically optimize the 3D model of a product. For example, it can optimize a 3D model of a smartphone case to reduce data volume while retaining fine details. The 3D model acquisition unit can also use generative AI to optimize 3D models of furniture parts to generate a highly accurate model while reducing data volume. This makes it possible to generate a highly accurate 3D model while reducing data volume.

[0031] The 3D model acquisition unit can use generative AI to perform simulations that take into account the product's usage environment and usage method, and propose the optimal shape. The 3D model acquisition unit can, for example, use generative AI to simulate the product's usage environment and propose the optimal shape. For example, the 3D model of an outdoor smartphone case can be optimized taking into account impact resistance. The 3D model acquisition unit can also use generative AI to simulate how furniture parts are used and propose the optimal shape. For example, optimization can be performed taking into account the strength and durability of the furniture parts. This makes it possible to propose the optimal shape taking into account the usage environment and usage method.

[0032] The 3D model acquisition unit can integrate image data from different viewpoints to generate a more three-dimensional and detailed 3D model. For example, the 3D model acquisition unit can integrate image data from different viewpoints to generate a 3D model. For example, a 3D model of a smartphone case can be reconstructed based on images taken from multiple angles. The 3D model acquisition unit can also integrate image data from different viewpoints to generate a 3D model of a furniture part. For example, the furniture part can be reconstructed based on images taken from multiple angles. This allows a more three-dimensional and detailed 3D model to be generated.

[0033] The 3D model acquisition unit can use generative AI to combine 3D models of different products to propose new accessories. For example, the 3D model acquisition unit can use generative AI to combine 3D models of different products to propose new accessories. For example, it can create a new design that combines a smartphone case and an earphone holder. The 3D model acquisition unit can also use generative AI to combine furniture parts with other furniture parts to propose new designs. For example, it can create a new design that combines chair legs with table legs. This makes it possible to propose new accessories by combining 3D models of different products.

[0034] The request collection unit can use the generation AI to automatically analyze text or illustration requests and classify the request content by category. For example, the request collection unit can use the generation AI to automatically analyze text requests provided by customers and classify them by category. For example, it can classify them into categories such as "design change" and "size adjustment." The request collection unit can also use the generation AI to automatically analyze illustration requests and classify them by category. For example, it can classify them into categories such as "add decoration" and "change shape." This allows the request content to be automatically analyzed and classified by category.

[0035] The request collection unit can make more personalized suggestions by taking into account the user's past purchase history and preferences. The request collection unit, for example, analyzes the user's past purchase history and makes personalized suggestions based on the request content. For example, it makes new suggestions based on the design of smartphone cases purchased in the past. The request collection unit can also make personalized suggestions by taking into account the user's preferences and based on the request content. For example, it can suggest new designs based on the user's preferred colors and shapes. This makes it possible to make personalized suggestions based on the user's past purchase history and preferences.

[0036] The request collection unit can collect user requests in a variety of ways by introducing voice input or gesture input. For example, the request collection unit can introduce voice input to allow the user to provide requests by voice. For example, the user can say, "Make this part a little bigger." The request collection unit can also introduce gesture input to allow the user to provide requests by gesture. For example, a specific gesture can be used to request a design change. This allows the user's requests to be collected in a variety of ways using voice input or gesture input.

[0037] The request collection unit can integrate requests from different users, extract common needs, and propose new accessories. For example, the request collection unit analyzes requests from different users, extracts common needs, and proposes new accessories. For example, it can propose a smartphone case that integrates design elements desired by multiple users. The request collection unit can also integrate requests from different users, extract common needs, and propose new accessories. For example, it can propose furniture parts that integrate size adjustments desired by multiple users. In this way, it is possible to integrate requests from different users, extract common needs, and propose new accessories.

[0038] The 3D model creation unit can use a generation AI to generate multiple 3D models based on the request content and provide the user with options. The 3D model creation unit can, for example, use a generation AI to generate multiple 3D models based on the request content and provide the user with options. For example, the unit can generate multiple smartphone cases with different designs and sizes and let the user choose from them. The 3D model creation unit can also use a generation AI to generate multiple furniture parts based on the request content and provide the user with options. For example, the unit can generate multiple furniture parts with different designs and sizes and let the user choose from them. This allows multiple 3D models to be generated and provides the user with options.

[0039] The 3D model creation unit can use generative AI to propose optimal designs taking into account material properties and manufacturing costs. The 3D model creation unit, for example, uses generative AI to propose optimal designs taking into account material properties. For example, it designs a smartphone case that takes into account durability and lightness. The 3D model creation unit can also use generative AI to propose optimal designs taking into account manufacturing costs. For example, it designs furniture parts using cost-effective materials. This allows it to propose optimal designs taking into account material properties and manufacturing costs.

[0040] The 3D model creation unit can use a generation AI to generate a 3D model that combines different requested contents and propose new accessories. The 3D model creation unit can, for example, use a generation AI to generate a 3D model that combines different requested contents and propose new accessories. For example, it can propose a smartphone case that combines design changes and size adjustments. The 3D model creation unit can also use a generation AI to generate furniture parts that combine different requested contents and propose new designs. For example, it can propose furniture parts that combine decoration additions and shape changes. This allows it to generate a 3D model that combines different requested contents and propose new accessories.

[0041] The 3D model creation unit can use generative AI to add different material or color variations to provide a variety of options to the user. For example, the 3D model creation unit can use generative AI to add different material variations to the 3D model to provide a variety of options to the user. For example, the unit can suggest smartphone cases that can be made from a variety of materials, such as plastic, metal, and wood. The 3D model creation unit can also use generative AI to add different color variations to the 3D model to provide a variety of options to the user. For example, the unit can suggest furniture parts with multiple color variations. This allows the unit to add different material and color variations to provide a variety of options to the user.

[0042] The 3D printing department can monitor the manufacturing process in real time and introduce a feedback loop to ensure quality. The 3D printing department can, for example, monitor the 3D printing manufacturing process in real time and introduce a feedback loop to ensure quality. For example, it can detect abnormalities during printing and immediately correct them. The 3D printing department can also monitor the manufacturing process in real time and introduce a feedback loop to ensure quality. For example, it can evaluate the accuracy and finish of the print in real time and adjust as necessary. This allows the manufacturing process to be monitored in real time and introduce a feedback loop to ensure quality.

[0043] The 3D printing department can use environmentally friendly materials in material selection and achieve sustainable manufacturing. The 3D printing department can, for example, use environmentally friendly materials in material selection for 3D printing. For example, they can manufacture smartphone cases using biodegradable plastic. The 3D printing department can also use environmentally friendly materials in material selection and achieve sustainable manufacturing. For example, they can manufacture furniture parts using recycled materials. This allows them to use environmentally friendly materials and achieve sustainable manufacturing.

[0044] 3D printing units can combine different printing technologies in their manufacturing processes to achieve more complex shapes and functions. 3D printing units can combine different printing technologies in their manufacturing processes, for example, combining FDM and SLA to manufacture smartphone cases with complex shapes. 3D printing units can also combine different printing technologies in their manufacturing processes to achieve more complex shapes and functions. For example, combining SLS and DLP to manufacture furniture parts with complex shapes. This allows for more complex shapes and functions to be achieved by combining different printing technologies.

[0045] The 3D printing department can integrate the manufacturing process with CNC machining or injection molding to propose a hybrid manufacturing method. For example, the 3D printing department can integrate the 3D printing manufacturing process with CNC machining to propose a hybrid manufacturing method. For example, parts created by 3D printing are finished by CNC machining. The 3D printing department can also integrate the manufacturing process with injection molding to propose a hybrid manufacturing method. For example, parts created by 3D printing are mass-produced by injection molding. This allows the integration of 3D printing with other manufacturing processes to propose a hybrid manufacturing method.

[0046] The sales department can make personalized suggestions that take into account the user's past purchase history and preferences. For example, the sales department analyzes the user's past purchase history and makes personalized suggestions. For example, it makes new suggestions based on the design of smartphone cases purchased in the past. The sales department can also make personalized suggestions that take into account the user's preferences. For example, it suggests new designs based on the user's preferred colors and shapes. In this way, it is possible to make personalized suggestions based on the user's past purchase history and preferences.

[0047] The sales department can collect user feedback in real time and reflect it in the next product improvement. For example, the sales department can add a feedback function to the sales platform and collect user opinions in real time. For example, the next product improvement can be made based on reviews and ratings after purchase. The sales department can also collect user feedback in real time and reflect it in the next product improvement. For example, the design or functions can be improved based on user opinions. In this way, user feedback can be collected in real time and reflected in the next product improvement.

[0048] The sales department can reach a wider customer base by utilizing different marketplaces or platforms. The sales department can, for example, sell products through online marketplaces such as Amazon and Rakuten. The sales department can also reach a wider customer base by utilizing different platforms. For example, they can sell products through a website or a mobile app. This allows them to reach a wider customer base by utilizing different marketplaces or platforms.

[0049] The sales department can introduce bundle sales on the platform that combine other related products or services to provide added value. For example, the sales department can introduce bundle sales on the sales platform to provide a combination of related products and services. For example, a smartphone case and a screen protector can be sold as a set. The sales department can also introduce bundle sales that combine other related products and services to provide added value. For example, furniture parts and assembly services can be offered as a set. This allows the sales department to introduce bundle sales that combine other related products and services to provide added value.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The request collection unit can also incorporate voice input and gesture input when collecting user requests. For example, a user can say, "I want this part to be a little bigger," by voice. Gesture input can also be used to instruct design changes with specific gestures. This allows users to submit requests more intuitively, improving convenience.

[0052] The 3D model acquisition unit can also integrate image data from different perspectives to generate more three-dimensional and detailed 3D models. For example, a 3D model of a smartphone case can be reconstructed based on images taken from multiple angles. It can also generate 3D models of furniture parts by integrating image data from different perspectives. This allows for the provision of more realistic and detailed 3D models.

[0053] The 3D model acquisition unit can also use generative AI to combine 3D models of different products to propose new accessories. For example, it can create a new design by combining a smartphone case with an earphone holder. It can also propose new designs by combining furniture parts with other furniture parts. This allows the system to provide users with new ideas and designs.

[0054] The request collection unit can make more personalized suggestions by taking into account the user's past purchase history and preferences. For example, it can make new suggestions based on the design of a smartphone case previously purchased. It can also make personalized suggestions based on the user's preferences and the content of the request. This allows for more appropriate suggestions to be made to the user.

[0055] The 3D model creation unit uses generation AI to generate multiple 3D models based on the request, offering the user a choice. For example, it can generate multiple smartphone cases with different designs and sizes and let the user choose. It can also generate multiple furniture parts with different designs and sizes and offer the user a choice. This allows the user to choose a model that suits their preferences.

[0056] The 3D printing department can monitor the manufacturing process in real time and implement a feedback loop to ensure quality. For example, it can detect abnormalities during printing and correct them immediately. It can also evaluate the accuracy and finish of the print in real time and make adjustments as needed. This improves the quality of the manufacturing process and provides users with high-quality products.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The 3D model acquisition unit acquires a 3D model of the product. For example, it uses data scanned from an existing product or data provided by the manufacturer. It can also acquire 3D models of various products, such as smartphone cases and furniture parts. Step 2: The request collection unit collects customer requests. For example, requests provided in the form of text or illustrations can be collected. It can also collect specific requests such as "I want a specific design added to this smartphone case" or "I want this furniture part to be changed to a specific size." Step 3: The 3D model creation unit creates a perfect-fit 3D model based on the requests collected by the request collection unit. For example, the generation AI understands instructions in text, such as "Make this part a little bigger," and modifies the 3D model accordingly. It can also add designs provided as illustrations to the 3D model. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate a 3D model based on the user's instructions. Step 4: The 3D printing department 3D prints the 3D models created by the 3D model creation department, for example, to produce smartphone cases with specific designs or furniture parts tailored to specific sizes, thereby providing accessories that meet customer needs.

[0059] (Example 2) A 3D printing service according to an embodiment of the present invention is a system that acquires a 3D model of a product, uses a generation AI to create a perfect-fit 3D model based on a customer's request, and sells the 3D-printed product. This allows the 3D printing service to provide accessories that meet customer needs.

[0060] A 3D printing service according to an embodiment includes a 3D model acquisition unit, a request collection unit, a 3D model creation unit, and a 3D printing unit. The 3D model acquisition unit acquires 3D models of products. For example, it uses data scanned from existing products or data provided by manufacturers. The 3D model acquisition unit can also acquire 3D models of various products, such as smartphone cases and furniture parts. The request collection unit collects customer requests. For example, it collects requests provided in the form of text or illustrations. The request collection unit can also collect specific requests, such as "I want a specific design added to this smartphone case" or "I want this furniture part resized to a specific size." The 3D model creation unit creates a perfect 3D model based on the requests collected by the request collection unit. For example, the generation AI can interpret instructions in text, such as "I want this part to be a little bigger," and modify the 3D model accordingly. The generation AI can also add designs provided in illustrations to the 3D model. The generation AI generates a 3D model based on user instructions using a text generation AI (e.g., LLM) or a multimodal generation AI. The 3D printing unit 3D prints the 3D model created by the 3D model creation unit. For example, it manufactures smartphone cases with specific designs or furniture parts adjusted to specific sizes. This allows the 3D printing service according to the embodiment to provide accessories that meet customer needs. For example, even if a customer is particular about a specific design or size and is not satisfied with ready-made products, they can use this service to obtain a product that completely satisfies them.

[0061] The 3D model acquisition unit uses generative AI to automatically optimize the 3D model of a product, generating a highly accurate model while reducing data volume. The 3D model acquisition unit, for example, uses generative AI to automatically optimize the 3D model of a product. For example, it can optimize a 3D model of a smartphone case to reduce data volume while retaining fine details. The 3D model acquisition unit can also use generative AI to optimize 3D models of furniture parts to generate a highly accurate model while reducing data volume. This makes it possible to generate a highly accurate 3D model while reducing data volume.

[0062] The 3D model acquisition unit can use generative AI to perform simulations that take into account the product's usage environment and usage method, and propose the optimal shape. The 3D model acquisition unit can, for example, use generative AI to simulate the product's usage environment and propose the optimal shape. For example, the 3D model of an outdoor smartphone case can be optimized taking into account impact resistance. The 3D model acquisition unit can also use generative AI to simulate how furniture parts are used and propose the optimal shape. For example, optimization can be performed taking into account the strength and durability of the furniture parts. This makes it possible to propose the optimal shape taking into account the usage environment and usage method.

[0063] The 3D model acquisition unit can use the emotion estimation function to estimate which part the user is most interested in and focus on refining that part. For example, the 3D model acquisition unit can use the emotion estimation function to identify the part the user is most interested in and refining that part. For example, in the design of a smartphone case, the part that the user pays attention to can be reproduced with high accuracy. The 3D model acquisition unit can also use the emotion estimation function to identify the part that the user is most interested in in the design of a furniture part and refining that part. For example, the decorative parts of the furniture part can be reproduced with high accuracy. This allows the part that the user is most interested in to be detailed.

[0064] The 3D model acquisition unit can integrate image data from different viewpoints to generate a more three-dimensional and detailed 3D model. For example, the 3D model acquisition unit can integrate image data from different viewpoints to generate a 3D model. For example, a 3D model of a smartphone case can be reconstructed based on images taken from multiple angles. The 3D model acquisition unit can also integrate image data from different viewpoints to generate a 3D model of a furniture part. For example, the furniture part can be reconstructed based on images taken from multiple angles. This allows a more three-dimensional and detailed 3D model to be generated.

[0065] The 3D model acquisition unit can use generative AI to combine 3D models of different products to propose new accessories. For example, the 3D model acquisition unit can use generative AI to combine 3D models of different products to propose new accessories. For example, it can create a new design that combines a smartphone case and an earphone holder. The 3D model acquisition unit can also use generative AI to combine furniture parts with other furniture parts to propose new designs. For example, it can create a new design that combines chair legs with table legs. This makes it possible to propose new accessories by combining 3D models of different products.

[0066] The 3D model acquisition unit can use the emotion estimation function to extract the features of a product that evoke the most positive emotions in the user and generate a 3D model that emphasizes those features. For example, the 3D model acquisition unit can use the emotion estimation function to extract the features that evoke the most positive emotions in the user and refine those features. For example, in the design of a smartphone case, the 3D model acquisition unit can emphasize the user's favorite color and shape. The 3D model acquisition unit can also use the emotion estimation function to extract the features that evoke the most positive emotions in the design of a furniture part and emphasize those features. For example, the decorative parts of the furniture part can be emphasized. This makes it possible to generate a 3D model that emphasizes the features that evoke the most positive emotions in the user.

[0067] The request collection unit can use the generation AI to automatically analyze text or illustration requests and classify the request content by category. For example, the request collection unit can use the generation AI to automatically analyze text requests provided by customers and classify them by category. For example, it can classify them into categories such as "design change" and "size adjustment." The request collection unit can also use the generation AI to automatically analyze illustration requests and classify them by category. For example, it can classify them into categories such as "add decoration" and "change shape." This allows the request content to be automatically analyzed and classified by category.

[0068] The request collection unit can make more personalized suggestions by taking into account the user's past purchase history and preferences. The request collection unit, for example, analyzes the user's past purchase history and makes personalized suggestions based on the request content. For example, it makes new suggestions based on the design of smartphone cases purchased in the past. The request collection unit can also make personalized suggestions by taking into account the user's preferences and based on the request content. For example, it can suggest new designs based on the user's preferred colors and shapes. This makes it possible to make personalized suggestions based on the user's past purchase history and preferences.

[0069] The request collection unit can use the emotion estimation function to analyze the user's emotion regarding the request content and prioritize processing requests that elicit positive emotions. The request collection unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the request content and prioritize processing requests that elicit positive emotions. For example, a design change request that makes the user happy is prioritized. The request collection unit can also use the emotion estimation function to analyze the user's emotion regarding the request content and prioritize processing requests that elicit positive emotions. For example, a size adjustment request that satisfies the user is prioritized. This allows requests that elicit positive emotions to be prioritized.

[0070] The request collection unit can collect user requests in a variety of ways by introducing voice input or gesture input. For example, the request collection unit can introduce voice input to allow the user to provide requests by voice. For example, the user can say, "Make this part a little bigger." The request collection unit can also introduce gesture input to allow the user to provide requests by gesture. For example, a specific gesture can be used to request a design change. This allows the user's requests to be collected in a variety of ways using voice input or gesture input.

[0071] The request collection unit can integrate requests from different users, extract common needs, and propose new accessories. For example, the request collection unit analyzes requests from different users, extracts common needs, and proposes new accessories. For example, it can propose a smartphone case that integrates design elements desired by multiple users. The request collection unit can also integrate requests from different users, extract common needs, and propose new accessories. For example, it can propose furniture parts that integrate size adjustments desired by multiple users. In this way, it is possible to integrate requests from different users, extract common needs, and propose new accessories.

[0072] The request collection unit can use the emotion estimation function to collect other users' emotional reactions to the request content and prioritize processing requests that are likely to be relatable. The request collection unit, for example, uses the emotion estimation function to collect other users' emotional reactions to the request content and prioritize processing requests that are likely to be relatable. For example, a design change request that many users can relatable to is prioritized. The request collection unit can also use the emotion estimation function to collect other users' emotional reactions to the request content and prioritize processing requests that are likely to be relatable. For example, a size adjustment request that many users can relatable to is prioritized. This allows requests that are likely to be relatable to be prioritized processing.

[0073] The 3D model creation unit can use a generation AI to generate multiple 3D models based on the request content and provide the user with options. The 3D model creation unit can, for example, use a generation AI to generate multiple 3D models based on the request content and provide the user with options. For example, the unit can generate multiple smartphone cases with different designs and sizes and let the user choose from them. The 3D model creation unit can also use a generation AI to generate multiple furniture parts based on the request content and provide the user with options. For example, the unit can generate multiple furniture parts with different designs and sizes and let the user choose from them. This allows multiple 3D models to be generated and provides the user with options.

[0074] The 3D model creation unit can use generative AI to propose optimal designs taking into account material properties and manufacturing costs. The 3D model creation unit, for example, uses generative AI to propose optimal designs taking into account material properties. For example, it designs a smartphone case that takes into account durability and lightness. The 3D model creation unit can also use generative AI to propose optimal designs taking into account manufacturing costs. For example, it designs furniture parts using cost-effective materials. This allows it to propose optimal designs taking into account material properties and manufacturing costs.

[0075] The 3D model creation unit can use the emotion estimation function to estimate the design that will most satisfy the user and create a 3D model based on that design. The 3D model creation unit can, for example, use the emotion estimation function to estimate the design that will most satisfy the user and create a 3D model based on that design. For example, it can create a smartphone case that reflects the user's preferred color and shape. The 3D model creation unit can also use the emotion estimation function to estimate the design that will most satisfy the user and create furniture parts based on that design. For example, it can create furniture parts that reflect the user's preferred decorations and size. This allows the creation of a 3D model based on the design that will most satisfy the user.

[0076] The 3D model creation unit can use a generation AI to generate a 3D model that combines different requested contents and propose new accessories. The 3D model creation unit can, for example, use a generation AI to generate a 3D model that combines different requested contents and propose new accessories. For example, it can propose a smartphone case that combines design changes and size adjustments. The 3D model creation unit can also use a generation AI to generate furniture parts that combine different requested contents and propose new designs. For example, it can propose furniture parts that combine decoration additions and shape changes. This allows it to generate a 3D model that combines different requested contents and propose new accessories.

[0077] The 3D model creation unit can use generative AI to add different material or color variations to provide a variety of options to the user. For example, the 3D model creation unit can use generative AI to add different material variations to the 3D model to provide a variety of options to the user. For example, the unit can suggest smartphone cases that can be made from a variety of materials, such as plastic, metal, and wood. The 3D model creation unit can also use generative AI to add different color variations to the 3D model to provide a variety of options to the user. For example, the unit can suggest furniture parts with multiple color variations. This allows the unit to add different material and color variations to provide a variety of options to the user.

[0078] The 3D model creation unit can use the emotion estimation function to extract design elements that evoke the most positive emotions in the user and generate a 3D model that emphasizes those elements. For example, the 3D model creation unit can use the emotion estimation function to extract design elements that evoke the most positive emotions in the user and generate a 3D model that emphasizes those elements. For example, the 3D model creation unit can create a smartphone case that emphasizes the user's favorite color and shape. The 3D model creation unit can also use the emotion estimation function to extract design elements that evoke the most positive emotions in the user and create furniture parts that emphasize those elements. For example, the 3D model creation unit can create furniture parts that emphasize the user's favorite decorations and size. This allows the creation of a 3D model that emphasizes the design elements that evoke the most positive emotions in the user.

[0079] The 3D printing department can monitor the manufacturing process in real time and introduce a feedback loop to ensure quality. The 3D printing department can, for example, monitor the 3D printing manufacturing process in real time and introduce a feedback loop to ensure quality. For example, it can detect abnormalities during printing and immediately correct them. The 3D printing department can also monitor the manufacturing process in real time and introduce a feedback loop to ensure quality. For example, it can evaluate the accuracy and finish of the print in real time and adjust as necessary. This allows the manufacturing process to be monitored in real time and introduce a feedback loop to ensure quality.

[0080] The 3D printing department can use environmentally friendly materials in material selection and achieve sustainable manufacturing. The 3D printing department can, for example, use environmentally friendly materials in material selection for 3D printing. For example, they can manufacture smartphone cases using biodegradable plastic. The 3D printing department can also use environmentally friendly materials in material selection and achieve sustainable manufacturing. For example, they can manufacture furniture parts using recycled materials. This allows them to use environmentally friendly materials and achieve sustainable manufacturing.

[0081] The 3D printing unit can use the emotion estimation function to estimate the finish that the user will be most satisfied with and optimize print settings to achieve that finish. The 3D printing unit can, for example, use the emotion estimation function to estimate the finish that the user will be most satisfied with and optimize print settings to achieve that finish. For example, it can achieve a surface finish that the user prefers. The 3D printing unit can also use the emotion estimation function to estimate the finish that the user will be most satisfied with and optimize print settings to achieve that finish. For example, it can achieve a color or texture that the user prefers. This allows the print settings to be optimized to achieve a finish that the user will be most satisfied with.

[0082] 3D printing units can combine different printing technologies in their manufacturing processes to achieve more complex shapes and functions. 3D printing units can combine different printing technologies in their manufacturing processes, for example, combining FDM and SLA to manufacture smartphone cases with complex shapes. 3D printing units can also combine different printing technologies in their manufacturing processes to achieve more complex shapes and functions. For example, combining SLS and DLP to manufacture furniture parts with complex shapes. This allows for more complex shapes and functions to be achieved by combining different printing technologies.

[0083] The 3D printing department can integrate the manufacturing process with CNC machining or injection molding to propose a hybrid manufacturing method. For example, the 3D printing department can integrate the 3D printing manufacturing process with CNC machining to propose a hybrid manufacturing method. For example, parts created by 3D printing are finished by CNC machining. The 3D printing department can also integrate the manufacturing process with injection molding to propose a hybrid manufacturing method. For example, parts created by 3D printing are mass-produced by injection molding. This allows the integration of 3D printing with other manufacturing processes to propose a hybrid manufacturing method.

[0084] The 3D printing unit can use the emotion estimation function to identify the manufacturing process for which the user feels the most positive, and prioritize the adoption of that process. The 3D printing unit can, for example, use the emotion estimation function to identify the manufacturing process for which the user feels the most positive, and prioritize the adoption of that process. For example, it can select a process that achieves a surface finish preferred by the user. The 3D printing unit can also use the emotion estimation function to identify the manufacturing process for which the user feels the most positive, and prioritize the adoption of that process. For example, it can select a process that achieves a color or texture that the user is satisfied with. This allows the manufacturing process for which the user feels the most positive to be identified, and prioritize the adoption of that process.

[0085] The sales department can make personalized suggestions that take into account the user's past purchase history and preferences. For example, the sales department analyzes the user's past purchase history and makes personalized suggestions. For example, it makes new suggestions based on the design of smartphone cases purchased in the past. The sales department can also make personalized suggestions that take into account the user's preferences. For example, it suggests new designs based on the user's preferred colors and shapes. In this way, it is possible to make personalized suggestions based on the user's past purchase history and preferences.

[0086] The sales department can collect user feedback in real time and reflect it in the next product improvement. For example, the sales department can add a feedback function to the sales platform and collect user opinions in real time. For example, the next product improvement can be made based on reviews and ratings after purchase. The sales department can also collect user feedback in real time and reflect it in the next product improvement. For example, the design or functions can be improved based on user opinions. In this way, user feedback can be collected in real time and reflected in the next product improvement.

[0087] The sales department can use the emotion estimation function to estimate the sales method that will most satisfy the user and adopt that method. The sales department, for example, uses the emotion estimation function to estimate the sales method that will most satisfy the user and adopt that method. For example, it realizes a purchasing process that the user prefers. The sales department can also use the emotion estimation function to estimate the sales method that will most satisfy the user and adopt that method. For example, it provides a payment method or delivery method that the user is satisfied with. In this way, it is possible to estimate the sales method that will most satisfy the user and adopt that method.

[0088] The sales department can reach a wider customer base by utilizing different marketplaces or platforms. The sales department can, for example, sell products through online marketplaces such as Amazon and Rakuten. The sales department can also reach a wider customer base by utilizing different platforms. For example, they can sell products through a website or a mobile app. This allows them to reach a wider customer base by utilizing different marketplaces or platforms.

[0089] The sales department can introduce bundle sales on the platform that combine other related products or services to provide added value. For example, the sales department can introduce bundle sales on the sales platform to provide a combination of related products and services. For example, a smartphone case and a screen protector can be sold as a set. The sales department can also introduce bundle sales that combine other related products and services to provide added value. For example, furniture parts and assembly services can be offered as a set. This allows the sales department to introduce bundle sales that combine other related products and services to provide added value.

[0090] The sales department can use the emotion estimation function to identify a sales campaign that will evoke the most positive emotions in the user and implement that campaign. For example, the sales department can use the emotion estimation function to identify a sales campaign that will evoke the most positive emotions in the user and implement that campaign. For example, the sales department can run a discount campaign that the user likes. The sales department can also use the emotion estimation function to identify a sales campaign that will evoke the most positive emotions in the user and implement that campaign. For example, the sales department can run a campaign with special benefits that will satisfy the user. In this way, the sales campaign that will evoke the most positive emotions in the user can be identified and implemented.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The request collection unit can also incorporate voice input and gesture input when collecting user requests. For example, a user can say, "I want this part to be a little bigger," by voice. Gesture input can also be used to instruct design changes with specific gestures. This allows users to submit requests more intuitively, improving convenience.

[0093] The 3D model acquisition unit can also integrate image data from different perspectives to generate more three-dimensional and detailed 3D models. For example, a 3D model of a smartphone case can be reconstructed based on images taken from multiple angles. It can also generate 3D models of furniture parts by integrating image data from different perspectives. This allows for the provision of more realistic and detailed 3D models.

[0094] The 3D model acquisition unit can also use generative AI to combine 3D models of different products to propose new accessories. For example, it can create a new design by combining a smartphone case with an earphone holder. It can also propose new designs by combining furniture parts with other furniture parts. This allows the system to provide users with new ideas and designs.

[0095] The 3D model acquisition unit uses the emotion estimation function to extract the product features that evoke the most positive emotions in users and generate 3D models that emphasize those features. For example, in the design of a smartphone case, it is possible to emphasize the colors and shapes that users prefer. It is also possible to extract the features that evoke the most positive emotions in the design of furniture parts and emphasize those features. This makes it possible to provide designs that will satisfy users the most.

[0096] The request collection unit can make more personalized suggestions by taking into account the user's past purchase history and preferences. For example, it can make new suggestions based on the design of a smartphone case previously purchased. It can also make personalized suggestions based on the user's preferences and the content of the request. This allows for more appropriate suggestions to be made to the user.

[0097] The request collection unit uses the emotion estimation function to analyze the user's emotions regarding the request content and can prioritize requests that elicit positive emotions. For example, it can prioritize design change requests that bring joy to the user. It can also prioritize size adjustment requests to ensure user satisfaction. This can increase user satisfaction.

[0098] The 3D model creation unit uses generation AI to generate multiple 3D models based on the request, offering the user a choice. For example, it can generate multiple smartphone cases with different designs and sizes and let the user choose. It can also generate multiple furniture parts with different designs and sizes and offer the user a choice. This allows the user to choose a model that suits their preferences.

[0099] The 3D model creation unit uses the emotion estimation function to estimate the design that will most satisfy the user and create a 3D model based on that design. For example, it can create a smartphone case that reflects the user's preferred color and shape. It can also create furniture parts that reflect the user's preferred decoration and size. This allows it to provide the user with a design that will most satisfy them.

[0100] The 3D printing department can monitor the manufacturing process in real time and implement a feedback loop to ensure quality. For example, it can detect abnormalities during printing and correct them immediately. It can also evaluate the accuracy and finish of the print in real time and make adjustments as needed. This improves the quality of the manufacturing process and provides users with high-quality products.

[0101] The 3D printing unit uses the emotion estimation function to estimate the finish that the user is most satisfied with and can optimize the print settings to achieve that finish. For example, it can achieve the surface finish that the user prefers. It can also optimize the print settings to achieve the color and texture that the user is most satisfied with. This allows it to provide the finish that the user is most satisfied with.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The 3D model acquisition unit acquires a 3D model of the product. For example, it uses data scanned from an existing product or data provided by the manufacturer. It can also acquire 3D models of various products, such as smartphone cases and furniture parts. Step 2: The request collection unit collects customer requests. For example, requests provided in the form of text or illustrations can be collected. It can also collect specific requests such as "I want a specific design added to this smartphone case" or "I want this furniture part to be changed to a specific size." Step 3: The 3D model creation unit creates a perfect-fit 3D model based on the requests collected by the request collection unit. For example, the generation AI understands instructions in text, such as "Make this part a little bigger," and modifies the 3D model accordingly. It can also add designs provided as illustrations to the 3D model. The generation AI uses text generation AI (e.g., LLM) and multimodal generation AI to generate a 3D model based on the user's instructions. Step 4: The 3D printing department 3D prints the 3D models created by the 3D model creation department, for example, to produce smartphone cases with specific designs or furniture parts tailored to specific sizes, thereby providing accessories that meet customer needs.

[0104] 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.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification 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 model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, 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 is 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 entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text data. 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 model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, 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 is 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 entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text data. 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 model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, 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 is 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 entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text data. 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 model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, 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 is 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 entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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. [Explanation of symbols]

[0171] 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 3D model acquisition unit that acquires a 3D model of the product; a request collection unit that collects customer requests; a 3D model creation unit that creates a Cinderella-fit 3D model based on the requests collected by the request collection unit; a 3D printing unit that 3D prints the 3D model created by the 3D model creation unit. A system characterized by:

2. The 3D model acquisition unit Guess which parts users are most interested in and focus on those parts 2. The system of claim 1.

3. The request collection unit Using generative AI, we automatically analyze text or illustration requests and categorize them.

2. The system of claim 1.

4. The 3D model creation unit Using generative AI, multiple 3D models are generated based on the request, and users are given a choice.

2. The system of claim 1.

5. The 3D printing unit is Monitor the manufacturing process in real time and implement a feedback loop to ensure quality 2. The system of claim 1.

6. The request collection unit Analyzes user sentiment toward the request content and prioritizes requests that elicit positive emotions.

2. The system of claim 1.

7. The 3D model creation unit Estimate the design that the user is most satisfied with and create the 3D model based on that design.

2. The system of claim 1.

8. The 3D printing unit is Identify the manufacturing process that users feel most positive about and prioritize that process.

2. The system of claim 1.

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