System

Abstract

An object of a system according to an embodiment is to generate a new design by effectively utilizing a surplus stock of a brand.SOLUTION: A system according to an embodiment includes a surplus stock acquisition unit, a data conversion unit, and a generation unit. The surplus stock acquisition unit acquires a surplus stock from a brand. The data generation unit registers the visual and material information of the product acquired by the surplus stock acquisition unit in the database. The generation unit generates a new design plan based on the information registered by the data generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to effectively utilize a brand's excess inventory to create new designs.

[0005] The system according to the embodiment aims to effectively utilize a brand's excess inventory to create new designs. [Means for solving the problem]

[0006] The system according to the embodiment includes a surplus inventory acquisition unit, a data conversion unit, and a generation unit. The surplus inventory acquisition unit acquires surplus inventory from brands. The data conversion unit registers the product visuals and material information acquired by the surplus inventory acquisition unit in a database. The generation unit generates new design proposals based on the information registered by the data conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize a brand's excess inventory to create new designs. [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) The upcycle design proposal system according to an embodiment of the present invention purchases excess inventory from brands and uses a generation AI to generate new design proposals. This allows the upcycle design proposal system to effectively utilize excess inventory and provide environmentally friendly upcycled products.

[0029] An upcycle design proposal system according to an embodiment includes a surplus inventory acquisition unit, a data conversion unit, and a generation unit. The surplus inventory acquisition unit acquires surplus inventory from brands. For example, it purchases surplus inventory such as clothing and accessories. The surplus inventory acquisition unit can also receive surplus inventory directly from brands. The data conversion unit registers the product visual and material information acquired by the surplus inventory acquisition unit in a database. For example, it takes a photo of the product and inputs information such as material, size, and color. The data conversion unit can also manually input detailed product information. The generation unit generates new design proposals based on the information registered by the data conversion unit. For example, the generation AI generates new design proposals using a text generation AI (e.g., LLM). The generation AI can also use a multimodal generation AI to analyze the product visual and material information and generate new design proposals. The generation AI can also generate design proposals taking into account the product's characteristics and color combinations. This allows the upcycle design proposal system according to an embodiment to effectively utilize surplus inventory and generate new design proposals. For example, the generated design proposals are instantly presented to the user, who can choose their favorite from multiple design proposals. The generative AI can also learn the user's selection history and preferences to generate more personalized design proposals. The generative AI can also refer to past design trends and fashion show data to generate design proposals that reflect the latest trends.

[0030] The generation unit may be equipped with a generative AI algorithm that analyzes material information in detail and identifies the deterioration state of the material and reusable portions. The generation unit may develop a generative AI algorithm to analyze material information of, for example, surplus clothing and accessories in detail. For example, image analysis technology may be used to detect the deterioration state of the material and identify reusable portions. The generation unit may also use a generative AI algorithm to evaluate the deterioration state of the material. For example, it may detect fading or damage to the material and identify reusable portions. The generation unit may also analyze the characteristics of the material and propose the optimal reuse method. For example, it may evaluate the strength and flexibility of the material and identify reusable portions. This allows for more effective upcycling by identifying the deterioration state of the material and reusable portions.

[0031] The digitization unit can add metadata including the product's history and brand background, allowing the generation AI to generate designs that are more in line with the context. For example, when digitizing surplus inventory, the digitization unit adds metadata including the product's history and brand background. For example, the year of manufacture of the product and the brand's characteristics can be registered in a database, allowing the generation AI to generate designs that are in line with the context. The digitization unit can also add information about the product's usage scenarios and target users. For example, the purpose of use and usage scenarios of the product can be registered in a database, allowing the generation AI to generate more personalized designs. The digitization unit can also collect information about the product's history and brand background and provide it to the generation AI. For example, information about the brand's history and the product's manufacturing process can be collected, allowing the generation AI to generate designs that are more in line with the context. This makes it possible to generate designs that take the product's history and brand background into consideration, thereby providing more attractive upcycled products.

[0032] The surplus inventory acquisition unit can purchase and digitize surplus inventory not only for clothing and accessories, but also for furniture and home appliances, enabling upcycling across a wide range of product categories. For example, the surplus inventory acquisition unit can purchase and digitize surplus inventory not only for clothing and accessories, but also for furniture and home appliances. For example, it can register furniture material information and home appliance part information in a database, allowing the generative AI to generate new design proposals. The surplus inventory acquisition unit can also purchase surplus furniture and home appliance inventory. For example, it can purchase surplus furniture and home appliance inventory from brands and register that information in a database. The surplus inventory acquisition unit can also directly receive surplus furniture and home appliance inventory. This enables upcycling across a wide range of product categories, enabling more surplus inventory to be effectively utilized.

[0033] The digitization department can introduce 3D scanning technology in the digitization process and provide the generating AI with three-dimensional information about the product. For example, the digitization department can introduce 3D scanning technology in the digitization process of excess inventory and provide the generating AI with three-dimensional information about the product. For example, a 3D model of clothing or furniture can be created, allowing the generating AI to generate new design proposals. The digitization department can also use 3D scanning technology to provide three-dimensional information about the product. For example, the shape and dimensions of the product can be 3D scanned and the information can be registered in a database. The digitization department can also enable the generating AI to generate more accurate design proposals based on the three-dimensional information about the product. For example, the digitization department can analyze the three-dimensional information about the product and generate optimal design proposals. In this way, providing three-dimensional information about the product can generate more accurate design proposals.

[0034] The generation unit can learn a user's past selection history and preferences and build a system that recommends optimal products to the user. The generation unit, for example, learns a user's past selection history and preferences and builds a system in which a generation AI recommends optimal products to the user. For example, the generation unit analyzes data on products selected in the past and recommends similar products. The generation unit can also use a generation AI to learn a user's preferences. For example, the generation unit develops an algorithm that recommends optimal products based on a user's selection history and preferences. The generation unit can also recommend personalized products based on a user's selection history and preferences. For example, the generation unit recommends products that take into account the user's favorite colors and styles. This makes it possible to recommend products that take into account a user's past selection history and preferences.

[0035] The generation unit can input information about the user's lifestyle and intended use, as well as the visual and material information of the product selected by the user, into the generation AI to generate more personalized design proposals. For example, the generation unit inputs information about the user's lifestyle and intended use, in addition to the visual and material information of the product selected by the user, into the generation AI. For example, it generates design proposals that take into account the user's lifestyle and intended use. The generation unit can also build a system for collecting information about the user's lifestyle and intended use. For example, it can collect information about lifestyle and intended use through user questionnaires and interviews. The generation unit can also develop an algorithm for generating design proposals that suit the user's lifestyle and intended use. For example, it generates optimal design proposals based on the user's lifestyle and intended use. This makes it possible to generate personalized design proposals that suit the user's lifestyle and intended use.

[0036] The generation unit can display reviews and ratings from other users in real time when the user is selecting a product, to help the user make a selection. The generation unit, for example, builds a system that displays reviews and ratings from other users in real time when the user is selecting a product. For example, the reviews and ratings of the selected product are displayed on a screen to help the user make a selection. The generation unit can also build a system for collecting reviews and ratings from other users. For example, it collects user comments and rating scores and registers them in a database. The generation unit can also recommend products that are optimal for the user based on the reviews and ratings of other users. For example, it prioritizes the recommendation of products that are highly rated by other users. This allows the user to make more accurate selections by referring to the reviews and ratings of other users.

[0037] The generation unit can refer to past design trends and fashion show data and reflect the latest trends when the generation AI generates new design proposals. For example, the generation unit can refer to past design trends and fashion show data when the generation AI generates new design proposals. For example, the generation unit can analyze data from fashion shows over the past few years and reflect the latest trends. The generation unit can also build a system for collecting data on design trends and fashion shows. For example, it can collect videos of fashion shows and photos of designs and register them in a database. The generation unit can also develop an algorithm for generating design proposals that reflect the latest trends. For example, it can analyze past design trends and generate design proposals that reflect the latest trends. This improves user satisfaction by generating design proposals that reflect the latest trends.

[0038] The generation unit can develop algorithms that allow the generation AI to analyze the properties of materials in detail and optimize material combinations and processing methods. For example, the generation unit can analyze the strength and flexibility of materials and propose optimal combinations. The generation unit can also build a system for analyzing material properties. For example, it can analyze the physical and chemical properties of materials and register them in a database. The generation unit can also develop algorithms that optimize material combinations and processing methods. For example, it can propose optimal combinations and processing methods based on the properties of materials. This allows for detailed analysis of material properties and proposals of optimal combinations and processing methods, thereby generating higher quality design proposals.

[0039] The generation unit can incorporate design elements from different cultures and regions when the generative AI generates new design proposals, and propose designs from a global perspective. For example, the generation unit can incorporate design elements from different cultures and regions when the generative AI generates new design proposals. For example, it can propose designs that combine traditional design elements from Asia and Europe. The generation unit can also build a system for collecting design elements from different cultures and regions. For example, it can collect information on traditional crafts and art from each region and register it in a database. The generation unit can also develop algorithms for proposing designs from a global perspective. For example, it can analyze design elements from different cultures and regions and propose optimal designs. This makes it possible to propose designs from a global perspective by incorporating design elements from different cultures and regions.

[0040] The generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can propose designs using recycled plastic and organic cotton. The generation unit can also build a system for using eco-friendly and recyclable materials. For example, the generation unit can collect information on eco-friendly and recyclable materials and register it in a database. The generation unit can also develop an algorithm for using eco-friendly and recyclable materials. For example, the generation unit can analyze the characteristics of materials and select the optimal materials. This makes it possible to generate environmentally friendly design proposals by using eco-friendly and recyclable materials.

[0041] The generation unit can build a system in which users provide real-time feedback on design proposals presented by the generation AI, and the design proposals are improved based on that feedback. The generation unit, for example, builds a system in which users provide real-time feedback on design proposals presented by the generation AI. For example, users input their opinions and thoughts about the design proposals in real time, and the design proposals are improved based on that feedback. The generation unit can also build a system for collecting user feedback. For example, it collects user comments and evaluation scores and registers them in a database. The generation unit can also develop an algorithm for improving design proposals based on user feedback. For example, it analyzes user opinions and thoughts and improves design proposals. In this way, user satisfaction is increased by improving design proposals based on user feedback.

[0042] The generation unit can take into account the user's past selection history and preferences when presenting design proposals and prioritize personalized design proposals. For example, the generation unit can present design proposals based on the user's past selection history and preferences when presenting design proposals. For example, the generation unit can present design proposals based on previously selected designs and preferred styles. The generation unit can also build a system for collecting the user's selection history and preferences. For example, the generation unit can register the user's selection history and preferences in a database so that the generation AI can generate personalized design proposals. The generation unit can also develop an algorithm for presenting optimal design proposals based on the user's selection history and preferences. For example, the generation unit can present design proposals that take into account the user's preferred colors and styles. This improves user satisfaction by presenting design proposals that take into account the user's past selection history and preferences.

[0043] The generation unit can add a function to the presentation of design proposals that allows the user to virtually try on the design proposals using AR technology. For example, the generation unit can add a function to the presentation of design proposals that allows the user to virtually try on the design proposals using AR technology. For example, the design proposals can be virtually tried on using a smartphone camera. The generation unit can also develop an algorithm for realizing the virtual try-on of the design proposals using AR technology. For example, a 3D model of the design proposal can be created and the virtual try-on can be performed in real time. The generation unit can also build a system for the user to virtually try on the design proposals. For example, the design proposals can be virtually tried on using a smartphone camera. This allows the user to virtually try on the design proposals, thereby improving the accuracy of selection.

[0044] The generation unit can support the user's selection by displaying other users' ratings and reviews in real time when presenting design proposals. The generation unit, for example, builds a system that displays other users' ratings and reviews in real time when presenting design proposals. For example, other users' ratings and comments on a selected design proposal can be displayed on a screen to help the user make a selection. The generation unit can also build a system for collecting other users' ratings and reviews. For example, it can collect user comments and rating scores and register them in a database. The generation unit can also recommend the most suitable design proposal to the user based on other users' ratings and reviews. For example, it can preferentially recommend design proposals that have received high ratings from other users. This allows the user to make more accurate selections by referring to other users' ratings and reviews.

[0045] The generation unit uses a generative AI to optimize the production process in the production process of upcycled products, thereby enabling the provision of efficient, high-quality products. For example, the generation unit uses a generative AI to optimize the production process in the production process of upcycled products. For example, it optimizes the order in which materials are cut and sewn, thereby enabling the provision of efficient, high-quality products. The generation unit can also develop algorithms to optimize the production process. For example, it can optimize the order of the production process and the machines and tools used. The generation unit can also build a system to improve the efficiency of the production process. For example, it can collect data on the production process and propose efficient production methods. This can optimize the production process, allowing the provision of efficient, high-quality upcycled products.

[0046] The generation unit can build a system in which the generation AI automatically performs quality inspections of manufactured products and eliminates defective products. The generation unit, for example, builds a system in which the generation AI automatically performs quality inspections of manufactured products. For example, it uses image analysis technology to inspect the appearance and dimensions of products and eliminates defective products. The generation unit can also develop algorithms to automate quality inspections. For example, it can detect product defects and identify defective products. The generation unit can also build a system to improve the efficiency of quality inspections. For example, it can collect quality inspection data and propose efficient inspection methods. This makes it possible to automatically perform quality inspections and eliminate defective products, thereby providing high-quality products.

[0047] The generation unit can cooperate with local artisans and artists in the production of upcycled products and incorporate unique design elements. For example, the generation unit can cooperate with local artisans and artists in the production of upcycled products and incorporate unique design elements. For example, the generation unit can propose designs that incorporate local traditional crafts and art. The generation unit can also build a system for collaborating with local artisans and artists. For example, the generation unit can collect information on local artisans and artists and register it in a database. The generation unit can also develop an algorithm for collaborating with local artisans and artists to propose designs. For example, the generation unit can analyze local traditional crafts and art and propose optimal designs. This makes it possible to provide upcycled products that incorporate unique design elements by collaborating with local artisans and artists.

[0048] The generation unit can employ eco-friendly packaging materials and delivery methods when delivering manufactured products, thereby reducing the environmental impact. The generation unit, for example, employs eco-friendly packaging materials and delivery methods when delivering manufactured products. For example, packaging materials using recycled paper or biodegradable plastics are employed. The generation unit can also build a system for employing eco-friendly packaging materials and delivery methods. For example, it collects information on eco-friendly packaging materials and delivery methods and registers it in a database. The generation unit can also develop an algorithm for reducing the environmental impact. For example, it improves the efficiency of delivery methods and reduces the environmental impact. As a result, the environmental impact can be reduced by employing eco-friendly packaging materials and delivery methods.

[0049] The generation unit can display reviews and ratings from other users in real time when the user is selecting a product, to help the user make a selection. The generation unit, for example, builds a system that displays reviews and ratings from other users in real time when the user is selecting a product. For example, the reviews and ratings of the selected product are displayed on a screen to help the user make a selection. The generation unit can also build a system for collecting reviews and ratings from other users. For example, it collects user comments and rating scores and registers them in a database. The generation unit can also recommend products that are optimal for the user based on the reviews and ratings of other users. For example, it prioritizes the recommendation of products that are highly rated by other users. This allows the user to make more accurate selections by referring to the reviews and ratings of other users.

[0050] The generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can propose designs using recycled plastic and organic cotton. The generation unit can also build a system for using eco-friendly and recyclable materials. For example, the generation unit can collect information on eco-friendly and recyclable materials and register it in a database. The generation unit can also develop an algorithm for using eco-friendly and recyclable materials. For example, the generation unit can analyze the characteristics of materials and select the optimal materials. This makes it possible to generate environmentally friendly design proposals by using eco-friendly and recyclable materials.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The generation unit can incorporate design elements from different cultures and regions when the generative AI generates new design proposals. For example, it can propose designs that combine traditional design elements from Asia and Europe. The generation unit can also build a system to collect design elements from different cultures and regions. For example, it can collect information on traditional crafts and art from each region and register it in a database. The generation unit can also develop algorithms to propose designs from a global perspective. For example, it can analyze design elements from different cultures and regions and propose optimal designs. This allows it to propose designs from a global perspective by incorporating design elements from different cultures and regions.

[0053] The generation unit prioritizes the use of eco-friendly and recyclable materials when the generative AI generates new design proposals. For example, it can propose designs using recycled plastic and organic cotton. The generation unit can also build a system for using eco-friendly and recyclable materials. For example, it can collect information on eco-friendly and recyclable materials and register it in a database. The generation unit can also develop an algorithm for using eco-friendly and recyclable materials. For example, it can analyze the characteristics of materials and select the optimal materials. This makes it possible to generate environmentally friendly design proposals by using eco-friendly and recyclable materials.

[0054] The generation unit can build a system in which users provide real-time feedback on design proposals presented by the generation AI and improve the design proposals based on that feedback. For example, users input their opinions and thoughts about the design proposals in real time, and the design proposals are improved based on that feedback. The generation unit can also build a system for collecting user feedback. For example, it can collect user comments and evaluation scores and register them in a database. The generation unit can also develop an algorithm for improving design proposals based on user feedback. For example, it can analyze user opinions and thoughts and improve the design proposals. In this way, user satisfaction can be improved by improving the design proposals based on user feedback.

[0055] The generation unit can work with local artisans and artists to incorporate unique design elements into the creation of upcycled products. For example, it can propose designs that incorporate local traditional crafts and art. The generation unit can also build a system for collaborating with local artisans and artists. For example, it can collect information on local artisans and artists and register it in a database. The generation unit can also develop an algorithm for collaborating with local artisans and artists to propose designs. For example, it can analyze local traditional crafts and art and propose optimal designs. This makes it possible to provide upcycled products that incorporate unique design elements by collaborating with local artisans and artists.

[0056] When the generation AI generates new design proposals, the generation unit can refer to data on past design trends and fashion shows to reflect the latest trends. For example, data from fashion shows over the past few years can be analyzed to reflect the latest trends. The generation unit can also build a system for collecting data on design trends and fashion shows. For example, it can collect videos of fashion shows and photos of designs and register them in a database. The generation unit can also develop an algorithm for generating design proposals that reflect the latest trends. For example, it can analyze past design trends and generate design proposals that reflect the latest trends. This improves user satisfaction by generating design proposals that reflect the latest trends.

[0057] When the generation AI generates new design proposals, the generation unit can input information about the user's lifestyle and intended use into the generation AI to generate more personalized design proposals. For example, it generates design proposals that take into account the user's lifestyle and intended use. The generation unit can also build a system for collecting information about the user's lifestyle and intended use. For example, it can collect information about lifestyle and intended use through user questionnaires and interviews. The generation unit can also develop an algorithm for generating design proposals that suit the user's lifestyle and intended use. For example, it generates optimal design proposals based on the user's lifestyle and intended use. This makes it possible to generate personalized design proposals that suit the user's lifestyle and intended use.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The surplus inventory acquisition department acquires surplus inventory from brands. For example, they purchase surplus clothing and accessories. The surplus inventory acquisition department can also receive surplus inventory directly from brands. Step 2: The data conversion unit registers the product visual and material information acquired by the excess inventory acquisition unit in a database. For example, it takes a photo of the product and inputs information such as material, size, and color. The data conversion unit can also manually input detailed product information. Step 3: The generation unit generates new design proposals based on the information registered by the data conversion unit. For example, the generation AI may use a text generation AI (e.g., LLM) to generate new design proposals. The generation AI may also use a multimodal generation AI to analyze the product's visual and material information to generate new design proposals. The generation AI may also generate design proposals taking into account the product's characteristics and color combinations.

[0060] (Example 2) The upcycle design proposal system according to an embodiment of the present invention purchases excess inventory from brands and uses a generation AI to generate new design proposals. This allows the upcycle design proposal system to effectively utilize excess inventory and provide environmentally friendly upcycled products.

[0061] An upcycle design proposal system according to an embodiment includes a surplus inventory acquisition unit, a data conversion unit, and a generation unit. The surplus inventory acquisition unit acquires surplus inventory from brands. For example, it purchases surplus inventory such as clothing and accessories. The surplus inventory acquisition unit can also receive surplus inventory directly from brands. The data conversion unit registers the product visual and material information acquired by the surplus inventory acquisition unit in a database. For example, it takes a photo of the product and inputs information such as material, size, and color. The data conversion unit can also manually input detailed product information. The generation unit generates new design proposals based on the information registered by the data conversion unit. For example, the generation AI generates new design proposals using a text generation AI (e.g., LLM). The generation AI can also use a multimodal generation AI to analyze the product visual and material information and generate new design proposals. The generation AI can also generate design proposals taking into account the product's characteristics and color combinations. This allows the upcycle design proposal system according to an embodiment to effectively utilize surplus inventory and generate new design proposals. For example, the generated design proposals are instantly presented to the user, who can choose their favorite from multiple design proposals. The generative AI can also learn the user's selection history and preferences to generate more personalized design proposals. The generative AI can also refer to past design trends and fashion show data to generate design proposals that reflect the latest trends.

[0062] The generation unit may be equipped with a generative AI algorithm that analyzes material information in detail and identifies the deterioration state of the material and reusable portions. The generation unit may develop a generative AI algorithm to analyze material information of, for example, surplus clothing and accessories in detail. For example, image analysis technology may be used to detect the deterioration state of the material and identify reusable portions. The generation unit may also use a generative AI algorithm to evaluate the deterioration state of the material. For example, it may detect fading or damage to the material and identify reusable portions. The generation unit may also analyze the characteristics of the material and propose the optimal reuse method. For example, it may evaluate the strength and flexibility of the material and identify reusable portions. This allows for more effective upcycling by identifying the deterioration state of the material and reusable portions.

[0063] The digitization unit can add metadata including the product's history and brand background, allowing the generation AI to generate designs that are more in line with the context. For example, when digitizing surplus inventory, the digitization unit adds metadata including the product's history and brand background. For example, the year of manufacture of the product and the brand's characteristics can be registered in a database, allowing the generation AI to generate designs that are in line with the context. The digitization unit can also add information about the product's usage scenarios and target users. For example, the purpose of use and usage scenarios of the product can be registered in a database, allowing the generation AI to generate more personalized designs. The digitization unit can also collect information about the product's history and brand background and provide it to the generation AI. For example, information about the brand's history and the product's manufacturing process can be collected, allowing the generation AI to generate designs that are more in line with the context. This makes it possible to generate designs that take the product's history and brand background into consideration, thereby providing more attractive upcycled products.

[0064] The digitization unit can use the emotion estimation function to evaluate the emotional value of products and prioritize digitization of products with high emotional value. The digitization unit, for example, uses the emotion estimation function to evaluate the emotional value of products in surplus inventory. For example, the digitization unit analyzes users' emotional reactions to the product's design and brand, and prioritizes digitization of products with high emotional value. The digitization unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional value of products. For example, the digitization unit analyzes a user's facial expressions and voice to evaluate emotional value. The digitization unit can also use the emotion estimation function to identify products with high emotional value. For example, products with high emotional value are identified based on the user's emotion score. In this way, user satisfaction can be improved by prioritized digitization of products with high emotional value.

[0065] The surplus inventory acquisition unit can purchase and digitize surplus inventory not only for clothing and accessories, but also for furniture and home appliances, enabling upcycling across a wide range of product categories. For example, the surplus inventory acquisition unit can purchase and digitize surplus inventory not only for clothing and accessories, but also for furniture and home appliances. For example, it can register furniture material information and home appliance part information in a database, allowing the generative AI to generate new design proposals. The surplus inventory acquisition unit can also purchase surplus furniture and home appliance inventory. For example, it can purchase surplus furniture and home appliance inventory from brands and register that information in a database. The surplus inventory acquisition unit can also directly receive surplus furniture and home appliance inventory. This enables upcycling across a wide range of product categories, enabling more surplus inventory to be effectively utilized.

[0066] The digitization department can introduce 3D scanning technology in the digitization process and provide the generating AI with three-dimensional information about the product. For example, the digitization department can introduce 3D scanning technology in the digitization process of excess inventory and provide the generating AI with three-dimensional information about the product. For example, a 3D model of clothing or furniture can be created, allowing the generating AI to generate new design proposals. The digitization department can also use 3D scanning technology to provide three-dimensional information about the product. For example, the shape and dimensions of the product can be 3D scanned and the information can be registered in a database. The digitization department can also enable the generating AI to generate more accurate design proposals based on the three-dimensional information about the product. For example, the digitization department can analyze the three-dimensional information about the product and generate optimal design proposals. In this way, providing three-dimensional information about the product can generate more accurate design proposals.

[0067] The generation unit can learn a user's past selection history and preferences and build a system that recommends optimal products to the user. The generation unit, for example, learns a user's past selection history and preferences and builds a system in which a generation AI recommends optimal products to the user. For example, the generation unit analyzes data on products selected in the past and recommends similar products. The generation unit can also use a generation AI to learn a user's preferences. For example, the generation unit develops an algorithm that recommends optimal products based on a user's selection history and preferences. The generation unit can also recommend personalized products based on a user's selection history and preferences. For example, the generation unit recommends products that take into account the user's favorite colors and styles. This makes it possible to recommend products that take into account a user's past selection history and preferences.

[0068] The generation unit can input information about the user's lifestyle and intended use, as well as the visual and material information of the product selected by the user, into the generation AI to generate more personalized design proposals. For example, the generation unit inputs information about the user's lifestyle and intended use, in addition to the visual and material information of the product selected by the user, into the generation AI. For example, it generates design proposals that take into account the user's lifestyle and intended use. The generation unit can also build a system for collecting information about the user's lifestyle and intended use. For example, it can collect information about lifestyle and intended use through user questionnaires and interviews. The generation unit can also develop an algorithm for generating design proposals that suit the user's lifestyle and intended use. For example, it generates optimal design proposals based on the user's lifestyle and intended use. This makes it possible to generate personalized design proposals that suit the user's lifestyle and intended use.

[0069] The generation unit uses the emotion estimation function to analyze the emotions of a user when selecting a product in real time, thereby promoting product selection that elicits positive emotions. The generation unit, for example, uses the emotion estimation function to analyze the emotions of a user when selecting a product in real time. For example, the generation unit analyzes the user's facial expressions and voice and recommends products that elicit positive emotions. The generation unit can also use the emotion estimation function to develop an algorithm for analyzing the user's emotions. For example, the generation unit calculates an emotion score based on the user's facial expressions and voice. The generation unit can also analyze the user's emotions and identify products that elicit positive emotions. For example, the generation unit recommends products that elicit positive emotions based on the user's emotion score. This enables product selection that takes the user's emotions into consideration.

[0070] The generation unit can display reviews and ratings from other users in real time when the user is selecting a product, to help the user make a selection. The generation unit, for example, builds a system that displays reviews and ratings from other users in real time when the user is selecting a product. For example, the reviews and ratings of the selected product are displayed on a screen to help the user make a selection. The generation unit can also build a system for collecting reviews and ratings from other users. For example, it collects user comments and rating scores and registers them in a database. The generation unit can also recommend products that are optimal for the user based on the reviews and ratings of other users. For example, it prioritizes the recommendation of products that are highly rated by other users. This allows the user to make more accurate selections by referring to the reviews and ratings of other users.

[0071] The generation unit can refer to past design trends and fashion show data and reflect the latest trends when the generation AI generates new design proposals. For example, the generation unit can refer to past design trends and fashion show data when the generation AI generates new design proposals. For example, the generation unit can analyze data from fashion shows over the past few years and reflect the latest trends. The generation unit can also build a system for collecting data on design trends and fashion shows. For example, it can collect videos of fashion shows and photos of designs and register them in a database. The generation unit can also develop an algorithm for generating design proposals that reflect the latest trends. For example, it can analyze past design trends and generate design proposals that reflect the latest trends. This improves user satisfaction by generating design proposals that reflect the latest trends.

[0072] The generation unit can develop algorithms that allow the generation AI to analyze the properties of materials in detail and optimize material combinations and processing methods. For example, the generation unit can analyze the strength and flexibility of materials and propose optimal combinations. The generation unit can also build a system for analyzing material properties. For example, it can analyze the physical and chemical properties of materials and register them in a database. The generation unit can also develop algorithms that optimize material combinations and processing methods. For example, it can propose optimal combinations and processing methods based on the properties of materials. This allows for detailed analysis of material properties and proposals of optimal combinations and processing methods, thereby generating higher quality design proposals.

[0073] The generation unit can use the emotion estimation function to evaluate the emotional impact of the generated design proposals on the user and preferentially generate designs that elicit positive emotions. The generation unit, for example, uses the emotion estimation function to evaluate the emotional impact of the generated design proposals on the user. For example, the generation unit analyzes the user's emotional response to the design proposals and preferentially generates designs that elicit positive emotions. The generation unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional impact of the design proposals. For example, the generation unit analyzes the user's facial expressions and voice to evaluate the emotional impact. The generation unit can also use the emotion estimation function to identify designs that elicit positive emotions. For example, the generation unit identifies designs that elicit positive emotions based on the user's emotion score. This allows the generation unit to preferentially generate design proposals that elicit positive emotions in the user, thereby improving user satisfaction.

[0074] The generation unit can incorporate design elements from different cultures and regions when the generative AI generates new design proposals, and propose designs from a global perspective. For example, the generation unit can incorporate design elements from different cultures and regions when the generative AI generates new design proposals. For example, it can propose designs that combine traditional design elements from Asia and Europe. The generation unit can also build a system for collecting design elements from different cultures and regions. For example, it can collect information on traditional crafts and art from each region and register it in a database. The generation unit can also develop algorithms for proposing designs from a global perspective. For example, it can analyze design elements from different cultures and regions and propose optimal designs. This makes it possible to propose designs from a global perspective by incorporating design elements from different cultures and regions.

[0075] The generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can propose designs using recycled plastic and organic cotton. The generation unit can also build a system for using eco-friendly and recyclable materials. For example, the generation unit can collect information on eco-friendly and recyclable materials and register it in a database. The generation unit can also develop an algorithm for using eco-friendly and recyclable materials. For example, the generation unit can analyze the characteristics of materials and select the optimal materials. This makes it possible to generate environmentally friendly design proposals by using eco-friendly and recyclable materials.

[0076] The generation unit can use the emotion estimation function to evaluate the emotional impact of the generated design proposals on the user and preferentially generate designs that elicit positive emotions. The generation unit, for example, uses the emotion estimation function to evaluate the emotional impact of the generated design proposals on the user. For example, the generation unit analyzes the user's emotional response to the design proposals and preferentially generates designs that elicit positive emotions. The generation unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional impact of the design proposals. For example, the generation unit analyzes the user's facial expressions and voice to evaluate the emotional impact. The generation unit can also use the emotion estimation function to identify designs that elicit positive emotions. For example, the generation unit identifies designs that elicit positive emotions based on the user's emotion score. This allows the generation unit to preferentially generate design proposals that elicit positive emotions in the user, thereby improving user satisfaction.

[0077] The generation unit can build a system in which users provide real-time feedback on design proposals presented by the generation AI, and the design proposals are improved based on that feedback. The generation unit, for example, builds a system in which users provide real-time feedback on design proposals presented by the generation AI. For example, users input their opinions and thoughts about the design proposals in real time, and the design proposals are improved based on that feedback. The generation unit can also build a system for collecting user feedback. For example, it collects user comments and evaluation scores and registers them in a database. The generation unit can also develop an algorithm for improving design proposals based on user feedback. For example, it analyzes user opinions and thoughts and improves design proposals. In this way, user satisfaction is increased by improving design proposals based on user feedback.

[0078] The generation unit can take into account the user's past selection history and preferences when presenting design proposals and prioritize personalized design proposals. For example, the generation unit can present design proposals based on the user's past selection history and preferences when presenting design proposals. For example, the generation unit can present design proposals based on previously selected designs and preferred styles. The generation unit can also build a system for collecting the user's selection history and preferences. For example, the generation unit can register the user's selection history and preferences in a database so that the generation AI can generate personalized design proposals. The generation unit can also develop an algorithm for presenting optimal design proposals based on the user's selection history and preferences. For example, the generation unit can present design proposals that take into account the user's preferred colors and styles. This improves user satisfaction by presenting design proposals that take into account the user's past selection history and preferences.

[0079] The generation unit can add a function to the presentation of design proposals that allows the user to virtually try on the design proposals using AR technology. For example, the generation unit can add a function to the presentation of design proposals that allows the user to virtually try on the design proposals using AR technology. For example, the design proposals can be virtually tried on using a smartphone camera. The generation unit can also develop an algorithm for realizing the virtual try-on of the design proposals using AR technology. For example, a 3D model of the design proposal can be created and the virtual try-on can be performed in real time. The generation unit can also build a system for the user to virtually try on the design proposals. For example, the design proposals can be virtually tried on using a smartphone camera. This allows the user to virtually try on the design proposals, thereby improving the accuracy of selection.

[0080] The generation unit can support the user's selection by displaying other users' ratings and reviews in real time when presenting design proposals. The generation unit, for example, builds a system that displays other users' ratings and reviews in real time when presenting design proposals. For example, other users' ratings and comments on a selected design proposal can be displayed on a screen to help the user make a selection. The generation unit can also build a system for collecting other users' ratings and reviews. For example, it can collect user comments and rating scores and register them in a database. The generation unit can also recommend the most suitable design proposal to the user based on other users' ratings and reviews. For example, it can preferentially recommend design proposals that have received high ratings from other users. This allows the user to make more accurate selections by referring to other users' ratings and reviews.

[0081] The generation unit can use the emotion estimation function to analyze the user's emotional response to the presented design proposals and preferentially present design proposals that elicit positive emotions. The generation unit, for example, uses the emotion estimation function to analyze the user's emotional response to the presented design proposals. For example, the generation unit analyzes the user's facial expressions and voice and preferentially presents design proposals that elicit positive emotions. The generation unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional impact of design proposals. For example, the generation unit analyzes the user's facial expressions and voice and evaluates the emotional impact. The generation unit can also use the emotion estimation function to identify design proposals that elicit positive emotions. For example, the generation unit identifies design proposals that elicit positive emotions based on the user's emotion score. This allows the generation unit to preferentially present design proposals that elicit positive emotions to the user, thereby improving user satisfaction.

[0082] The generation unit uses a generative AI to optimize the production process in the production process of upcycled products, thereby enabling the provision of efficient, high-quality products. For example, the generation unit uses a generative AI to optimize the production process in the production process of upcycled products. For example, it optimizes the order in which materials are cut and sewn, thereby enabling the provision of efficient, high-quality products. The generation unit can also develop algorithms to optimize the production process. For example, it can optimize the order of the production process and the machines and tools used. The generation unit can also build a system to improve the efficiency of the production process. For example, it can collect data on the production process and propose efficient production methods. This can optimize the production process, allowing the provision of efficient, high-quality upcycled products.

[0083] The generation unit can build a system in which the generation AI automatically performs quality inspections of manufactured products and eliminates defective products. The generation unit, for example, builds a system in which the generation AI automatically performs quality inspections of manufactured products. For example, it uses image analysis technology to inspect the appearance and dimensions of products and eliminates defective products. The generation unit can also develop algorithms to automate quality inspections. For example, it can detect product defects and identify defective products. The generation unit can also build a system to improve the efficiency of quality inspections. For example, it can collect quality inspection data and propose efficient inspection methods. This makes it possible to automatically perform quality inspections and eliminate defective products, thereby providing high-quality products.

[0084] The generation unit can use the emotion estimation function to evaluate the emotional satisfaction of a product received by a user and improve the production process based on the feedback. The generation unit, for example, uses the emotion estimation function to evaluate the emotional satisfaction of a product received by a user. For example, the generation unit can analyze user reviews and ratings to calculate the emotional satisfaction. The generation unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional satisfaction of a user. For example, the generation unit can analyze the user's facial expressions and voice to evaluate the emotional satisfaction. The generation unit can also build a system for improving the production process based on user feedback. For example, the generation unit can collect user opinions and impressions and improve the production process. In this way, the user's emotional satisfaction can be evaluated and the production process can be improved based on the feedback, thereby improving user satisfaction.

[0085] The generation unit can cooperate with local artisans and artists in the production of upcycled products and incorporate unique design elements. For example, the generation unit can cooperate with local artisans and artists in the production of upcycled products and incorporate unique design elements. For example, the generation unit can propose designs that incorporate local traditional crafts and art. The generation unit can also build a system for collaborating with local artisans and artists. For example, the generation unit can collect information on local artisans and artists and register it in a database. The generation unit can also develop an algorithm for collaborating with local artisans and artists to propose designs. For example, the generation unit can analyze local traditional crafts and art and propose optimal designs. This makes it possible to provide upcycled products that incorporate unique design elements by collaborating with local artisans and artists.

[0086] The generation unit can employ eco-friendly packaging materials and delivery methods when delivering manufactured products, thereby reducing the environmental impact. The generation unit, for example, employs eco-friendly packaging materials and delivery methods when delivering manufactured products. For example, packaging materials using recycled paper or biodegradable plastics are employed. The generation unit can also build a system for employing eco-friendly packaging materials and delivery methods. For example, it collects information on eco-friendly packaging materials and delivery methods and registers it in a database. The generation unit can also develop an algorithm for reducing the environmental impact. For example, it improves the efficiency of delivery methods and reduces the environmental impact. As a result, the environmental impact can be reduced by employing eco-friendly packaging materials and delivery methods.

[0087] The generation unit can use the emotion estimation function to evaluate the emotional satisfaction of a product received by a user and improve the production process based on the feedback. The generation unit, for example, uses the emotion estimation function to evaluate the emotional satisfaction of a product received by a user. For example, the generation unit can analyze user reviews and ratings to calculate the emotional satisfaction. The generation unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional satisfaction of a user. For example, the generation unit can analyze the user's facial expressions and voice to evaluate the emotional satisfaction. The generation unit can also build a system for improving the production process based on user feedback. For example, the generation unit can collect user opinions and impressions and improve the production process. In this way, the user's emotional satisfaction can be evaluated and the production process can be improved based on the feedback, thereby improving user satisfaction.

[0088] The generation unit can display reviews and ratings from other users in real time when the user is selecting a product, to help the user make a selection. The generation unit, for example, builds a system that displays reviews and ratings from other users in real time when the user is selecting a product. For example, the reviews and ratings of the selected product are displayed on a screen to help the user make a selection. The generation unit can also build a system for collecting reviews and ratings from other users. For example, it collects user comments and rating scores and registers them in a database. The generation unit can also recommend products that are optimal for the user based on the reviews and ratings of other users. For example, it prioritizes the recommendation of products that are highly rated by other users. This allows the user to make more accurate selections by referring to the reviews and ratings of other users.

[0089] The generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can prioritize the use of eco-friendly and recyclable materials when the generation AI generates new design proposals. For example, the generation unit can propose designs using recycled plastic and organic cotton. The generation unit can also build a system for using eco-friendly and recyclable materials. For example, the generation unit can collect information on eco-friendly and recyclable materials and register it in a database. The generation unit can also develop an algorithm for using eco-friendly and recyclable materials. For example, the generation unit can analyze the characteristics of materials and select the optimal materials. This makes it possible to generate environmentally friendly design proposals by using eco-friendly and recyclable materials.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The generation unit can estimate the user's emotions and generate design proposals based on the estimated emotions. For example, it can analyze the user's emotional reactions to designs previously selected and prioritize the generation of designs that elicit positive emotions. The generation unit can also analyze the user's emotions in real time and adjust the design proposals according to changes in emotions. Furthermore, the generation unit can accumulate the user's emotional data and analyze long-term emotional trends to optimize the design proposals. This makes it possible to provide personalized design proposals based on the user's emotions.

[0092] The generation unit can incorporate design elements from different cultures and regions when the generative AI generates new design proposals. For example, it can propose designs that combine traditional design elements from Asia and Europe. The generation unit can also build a system to collect design elements from different cultures and regions. For example, it can collect information on traditional crafts and art from each region and register it in a database. The generation unit can also develop algorithms to propose designs from a global perspective. For example, it can analyze design elements from different cultures and regions and propose optimal designs. This allows it to propose designs from a global perspective by incorporating design elements from different cultures and regions.

[0093] The generation unit can use the emotion estimation function to analyze the emotions of a user when selecting a product in real time, and promote product selection that elicits positive emotions. For example, the generation unit can analyze the user's facial expressions and voice and recommend products that elicit positive emotions. The generation unit can also use the emotion estimation function to develop an algorithm for analyzing the user's emotions. For example, the generation unit can calculate an emotion score based on the user's facial expressions and voice. The generation unit can also analyze the user's emotions and identify products that elicit positive emotions. For example, the generation unit can recommend products that elicit positive emotions based on the user's emotion score. This makes it possible to select products that take the user's emotions into consideration.

[0094] The generation unit prioritizes the use of eco-friendly and recyclable materials when the generative AI generates new design proposals. For example, it can propose designs using recycled plastic and organic cotton. The generation unit can also build a system for using eco-friendly and recyclable materials. For example, it can collect information on eco-friendly and recyclable materials and register it in a database. The generation unit can also develop an algorithm for using eco-friendly and recyclable materials. For example, it can analyze the characteristics of materials and select the optimal materials. This makes it possible to generate environmentally friendly design proposals by using eco-friendly and recyclable materials.

[0095] The generation unit can use the emotion estimation function to evaluate the emotional impact of the generated design proposals on the user and preferentially generate designs that elicit positive emotions. For example, the generation unit can analyze the user's emotional response to the design proposals and preferentially generate designs that elicit positive emotions. The generation unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional impact of the design proposals. For example, the generation unit can analyze the user's facial expressions and voice to evaluate the emotional impact. The generation unit can also use the emotion estimation function to identify designs that elicit positive emotions. For example, the generation unit can identify designs that elicit positive emotions based on the user's emotion score. This allows the generation unit to preferentially generate design proposals that elicit positive emotions in the user, thereby improving user satisfaction.

[0096] The generation unit can build a system in which users provide real-time feedback on design proposals presented by the generation AI and improve the design proposals based on that feedback. For example, users input their opinions and thoughts about the design proposals in real time, and the design proposals are improved based on that feedback. The generation unit can also build a system for collecting user feedback. For example, it can collect user comments and evaluation scores and register them in a database. The generation unit can also develop an algorithm for improving design proposals based on user feedback. For example, it can analyze user opinions and thoughts and improve the design proposals. In this way, user satisfaction can be improved by improving the design proposals based on user feedback.

[0097] The generation unit can use the emotion estimation function to evaluate the emotional satisfaction of the product received by the user and improve the production process based on the feedback. For example, the generation unit can analyze user reviews and ratings to calculate the emotional satisfaction. The generation unit can also use the emotion estimation function to develop an algorithm for evaluating the emotional satisfaction of the user. For example, the generation unit can analyze the user's facial expressions and voice to evaluate the emotional satisfaction. The generation unit can also build a system for improving the production process based on user feedback. For example, the generation unit can collect user opinions and impressions and improve the production process. In this way, the user's emotional satisfaction can be evaluated and the production process can be improved based on the feedback, thereby increasing user satisfaction.

[0098] The generation unit can work with local artisans and artists to incorporate unique design elements into the creation of upcycled products. For example, it can propose designs that incorporate local traditional crafts and art. The generation unit can also build a system for collaborating with local artisans and artists. For example, it can collect information on local artisans and artists and register it in a database. The generation unit can also develop an algorithm for collaborating with local artisans and artists to propose designs. For example, it can analyze local traditional crafts and art and propose optimal designs. This makes it possible to provide upcycled products that incorporate unique design elements by collaborating with local artisans and artists.

[0099] When the generation AI generates new design proposals, the generation unit can refer to data on past design trends and fashion shows to reflect the latest trends. For example, data from fashion shows over the past few years can be analyzed to reflect the latest trends. The generation unit can also build a system for collecting data on design trends and fashion shows. For example, it can collect videos of fashion shows and photos of designs and register them in a database. The generation unit can also develop an algorithm for generating design proposals that reflect the latest trends. For example, it can analyze past design trends and generate design proposals that reflect the latest trends. This improves user satisfaction by generating design proposals that reflect the latest trends.

[0100] When the generation AI generates new design proposals, the generation unit can input information about the user's lifestyle and intended use into the generation AI to generate more personalized design proposals. For example, it generates design proposals that take into account the user's lifestyle and intended use. The generation unit can also build a system for collecting information about the user's lifestyle and intended use. For example, it can collect information about lifestyle and intended use through user questionnaires and interviews. The generation unit can also develop an algorithm for generating design proposals that suit the user's lifestyle and intended use. For example, it generates optimal design proposals based on the user's lifestyle and intended use. This makes it possible to generate personalized design proposals that suit the user's lifestyle and intended use.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The surplus inventory acquisition department acquires surplus inventory from brands. For example, they purchase surplus clothing and accessories. The surplus inventory acquisition department can also receive surplus inventory directly from brands. Step 2: The data conversion unit registers the product visual and material information acquired by the excess inventory acquisition unit in a database. For example, it takes a photo of the product and inputs information such as material, size, and color. The data conversion unit can also manually input detailed product information. Step 3: The generation unit generates new design proposals based on the information registered by the data conversion unit. For example, the generation AI may use a text generation AI (e.g., LLM) to generate new design proposals. The generation AI may also use a multimodal generation AI to analyze the product's visual and material information to generate new design proposals. The generation AI may also generate design proposals taking into account the product's characteristics and color combinations.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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. an excess inventory acquisition unit that acquires excess inventory from brands; a data conversion unit that registers the visual and material information of the product acquired by the surplus inventory acquisition unit in a database; a generation unit that generates a new design proposal based on the information registered by the data conversion unit. A system characterized by:

2. The data conversion unit Adding metadata including product history and brand background will enable the generative AI to generate more contextual designs.

2. The system of claim 1.

3. The surplus inventory acquisition unit The purchasing and data collection of excess inventory will be applied not only to clothing and accessories but also to furniture and home appliances, achieving upcycling across a wide range of product categories.

2. The system of claim 1.

4. The generation unit Build a system that learns a user's past selection history and preferences and recommends the most suitable products to that user.

2. The system of claim 1.

5. The data conversion unit Using the emotion estimation function, the emotional value of the product is evaluated, and products with high emotional value are prioritized for data collection.

2. The system of claim 1.

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