system

Abstract

The system according to this embodiment aims to enable users to generate designs from images, text, music, etc., without requiring specialized knowledge, convert them into 3D data, and automatically convert them into patterns and specifications. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a 3D generation unit, a regeneration unit, and a conversion unit. The reception unit receives image or text or music input from the user. The generation unit analyzes the information received by the reception unit and generates a 2D design proposal. The 3D generation unit generates 3D data based on the 2D design proposal generated by the generation unit. The regeneration unit regenerates the 3D data based on the 3D data generated by the 3D generation unit, reflecting the textile or decorative item selected by the user. The conversion unit automatically converts the 3D data regenerated by the regeneration unit into a pattern and specifications.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of a user generating a design from an image, text, music, etc., converting it into 3D data, and further converting it into a pattern and a specification is complicated and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to enable a user to generate a design from an image, text, music, etc., convert it into 3D data, and automatically convert it into a pattern and a specification without specialized knowledge.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a reception unit, a generation unit, a 3D generation unit, a regeneration unit, and a conversion unit. The reception unit receives input from the user, such as images, text, or music. The generation unit analyzes the information received by the reception unit and generates a 2D design proposal. The 3D generation unit generates 3D data based on the 2D design proposal generated by the generation unit. The regeneration unit regenerates the 3D data based on the 3D data generated by the 3D generation unit, reflecting the textile or decorative item selected by the user. The conversion unit automatically converts the 3D data regenerated by the regeneration unit into patterns and specifications. [Effects of the Invention]

[0007] The system according to this embodiment allows users to generate designs from images, text, music, etc., without requiring specialized knowledge, convert them into 3D data, and automatically convert them into patterns and specifications. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The clothing design system according to an embodiment of the present invention is an AI tool that allows anyone to design clothing. When a user inputs images, text, music, etc., the AI ​​analyzes the input and generates a 2D design proposal. Next, the AI ​​generates 3D data based on the generated 2D design proposal. Furthermore, the user selects digital textiles and embellishments (buttons, zippers, etc.), and the AI ​​regenerates the 3D data based on these selections. Finally, the system automatically converts the generated 3D data into patterns and specifications. This entire process makes it possible for anyone to easily design, manufacture, and sell clothing. For example, if a user inputs "I want to make a floral dress," the AI ​​analyzes the information and generates a 2D design proposal. Next, the AI ​​generates 3D data based on the generated 2D design proposal. For example, the 2D design proposal is represented three-dimensionally, and the shape and details of the dress are generated as 3D data. Furthermore, the user selects digital textiles and embellishments. For example, if a user selects "I want to use red silk fabric and gold buttons," the AI ​​regenerates the 3D data based on these selections. Finally, the generated 3D data is automatically converted into patterns and specifications. For example, it generates patterns for each part of a dress and specifications including sewing instructions. This entire process makes it possible for anyone to easily design, manufacture, and sell clothing. In short, the clothing design system makes it possible for anyone to easily design, manufacture, and sell clothing.

[0029] The clothing design system according to this embodiment comprises a reception unit, a generation unit, a 3D generation unit, a regeneration unit, and a conversion unit. The reception unit receives image or text and music input from the user. For example, the reception unit can accept images, text, and music in formats such as JPEG, PNG, MP3, and WAV. The generation unit analyzes the information received by the reception unit and generates 2D design proposals. For example, the generation unit uses image recognition algorithms, natural language processing technology, and music analysis technology to analyze images, text, and music and generate 2D design proposals. The 3D generation unit generates 3D data based on the 2D design proposals generated by the generation unit. For example, the 3D generation unit generates 3D data in formats such as STL, OBJ, and FBX. The regeneration unit regenerates 3D data based on the 3D data generated by the 3D generation unit, reflecting textiles and ornaments selected by the user. For example, the regeneration unit regenerates 3D data using methods for applying textiles and ornaments and a regeneration algorithm. The conversion unit automatically converts the 3D data regenerated by the regeneration unit into patterns and specifications. For example, the conversion unit generates patterns in formats such as PDF and DXF, and specifications including manufacturing procedures, material lists, and dimension drawings. As a result, the clothing design system according to this embodiment allows anyone to easily design, manufacture, and sell clothing.

[0030] The clothing design system includes an retrieval unit that obtains textiles and decorative items selected by the user from a database. For example, the retrieval unit searches the database for fabric types and decorative item types, and retrieves the textiles and decorative items selected by the user. This allows for efficient retrieval of the textiles and decorative items selected by the user.

[0031] The clothing design system includes a provisioning unit that provides generated design proposals and 3D data to the user. For example, the provisioning unit provides the generated design proposals and 3D data to the user through methods such as an online platform, download links, or email. This allows the user to review the generated design proposals and 3D data.

[0032] The generation unit can analyze images, text, or music to generate 2D design proposals. For example, it uses image recognition algorithms, natural language processing techniques, and music analysis techniques to analyze images, text, and music and generate 2D design proposals. This allows for the generation of 2D design proposals from diverse input information.

[0033] The 3D generation unit can generate 3D data based on 2D design proposals. The 3D generation unit generates 3D data in formats such as STL, OBJ, and FBX. This allows for a three-dimensional representation of 2D design proposals.

[0034] The regeneration unit can regenerate 3D data that reflects the textiles and decorative items selected by the user. For example, the regeneration unit regenerates the 3D data using methods for applying the textiles and decorative items, and a regeneration algorithm. This allows for the regeneration of 3D data based on the user's selections.

[0035] The conversion unit can automatically convert the regenerated 3D data into patterns and specifications. For example, the conversion unit generates patterns in formats such as PDF and DXF, and specifications including manufacturing procedures, material lists, and dimension drawings. This allows for the automatic generation of patterns and specifications from 3D data.

[0036] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display images and text that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest images and text that the user will use at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0037] The input system can filter input based on the user's current projects and areas of interest. For example, it might prioritize displaying images and text related to the user's current projects. It can also suggest relevant music and text based on the user's areas of interest. Furthermore, it can filter input based on areas the user has shown interest in in the past. This allows for filtering of input based on the user's projects and areas of interest.

[0038] The input system can prioritize accepting highly relevant input content by considering the user's geographical location. For example, if the user is in a specific region, the input system can prioritize displaying images and text related to that region. If the user is traveling, it can suggest music and text related to their travel destination. Furthermore, if the user is attending a specific event, it can prioritize displaying input content related to that event. This allows the system to prioritize accepting highly relevant input content based on the user's geographical location.

[0039] The input system can analyze the user's social media activity and accept relevant input content during the input process. For example, it can automatically display images and text shared by the user on social media as suggestions. It can also suggest relevant music and text based on the user's social media interests. Furthermore, it can filter input content based on the accounts the user follows on social media. This allows the system to accept relevant input content based on the user's social media activity.

[0040] The generation unit can adjust the level of detail of the generated design based on the importance of the input information. For example, it can generate a detailed design based on highly important information. It can also generate a simplified design based on less important information. Furthermore, it can generate a balanced design based on moderately important information. This allows the level of detail of the generated design to be adjusted according to the importance of the input information.

[0041] The generation unit can apply different generation algorithms depending on the category of the input information during generation. For example, it can apply a natural language processing algorithm to generate a design based on text information. It can also apply an image analysis algorithm to generate a design based on image information. Furthermore, it can apply a music analysis algorithm to generate a design based on music information. This allows the optimal generation algorithm to be applied according to the category of the input information.

[0042] The generation unit can determine the generation priority based on the submission timing of the input information during the generation process. For example, the generation unit can prioritize processing information submitted earlier and generate the design. It can also postpone processing information submitted later and generate the design. Furthermore, the generation unit can process information submitted at a moderate stage in a balanced manner and generate the design. This allows the generation priority to be determined based on the submission timing.

[0043] The generation unit can adjust the generation order based on the relevance of the input information during generation. For example, the generation unit can prioritize processing highly relevant information and generate a design. It can also postpone processing less relevant information and generate a design. Furthermore, it can balance the processing of moderately relevant information and generate a design. This allows the generation order to be adjusted based on the relevance of the input information.

[0044] The 3D generation unit can adjust the level of detail generated based on the importance of the 2D design proposals during 3D data generation. For example, the 3D generation unit can generate detailed 3D data based on high-importance 2D design proposals. It can also generate simplified 3D data based on low-importance 2D design proposals. Furthermore, it can generate balanced 3D data based on 2D design proposals of moderate importance. This allows the level of detail of the 3D data to be adjusted according to the importance of the 2D design proposals.

[0045] The 3D generation unit can apply different generation algorithms depending on the category of the 2D design proposal when generating 3D data. For example, the 3D generation unit can generate 3D data by applying a natural language processing algorithm based on text information. It can also generate 3D data by applying an image analysis algorithm based on image information. Furthermore, it can generate 3D data by applying a music analysis algorithm based on music information. This allows for the application of the optimal generation algorithm depending on the category of the 2D design proposal.

[0046] The 3D generation unit can determine the generation priority based on the submission timing of 2D design proposals when generating 3D data. For example, the 3D generation unit can prioritize processing 2D design proposals submitted earlier and generate 3D data from them. It can also postpone processing 2D design proposals submitted later and generate 3D data from them. Furthermore, the 3D generation unit can process 2D design proposals submitted at intermediate times in a balanced manner and generate 3D data from them. This allows the generation priority of 3D data to be determined based on the submission timing.

[0047] The 3D generation unit can adjust the generation order based on the relevance of 2D design proposals when generating 3D data. For example, the 3D generation unit can prioritize processing highly relevant 2D design proposals and generate 3D data from them. It can also postpone processing less relevant 2D design proposals and generate 3D data from them. Furthermore, the 3D generation unit can process 2D design proposals of moderate relevance in a balanced manner and generate 3D data from them. In this way, the generation order of 3D data can be adjusted based on the relevance of 2D design proposals.

[0048] The regeneration unit can adjust the level of detail in the regeneration based on the importance of the textiles and decorative items selected by the user. For example, the regeneration unit can regenerate detailed 3D data based on textiles and decorative items of high importance. It can also regenerate simplified 3D data based on textiles and decorative items of low importance. Furthermore, it can regenerate balanced 3D data based on textiles and decorative items of medium importance. This allows the level of detail in the regeneration to be adjusted according to the importance of the selected textiles and decorative items.

[0049] The regeneration unit can apply different regeneration algorithms depending on the textile and decorative item categories selected by the user during the regeneration process. For example, the regeneration unit can regenerate 3D data with a smooth texture based on silk textiles. It can also regenerate 3D data with a robust texture based on leather textiles. Furthermore, the regeneration unit can regenerate 3D data with detailed features based on decorative items such as buttons and zippers. This allows the application of the optimal regeneration algorithm according to the selected textile and decorative item categories.

[0050] The regeneration unit can prioritize the reflection of highly relevant textiles and accessories, taking into account the user's geographical location during the regeneration process. For example, if the user is in a specific region, the regeneration unit will prioritize the reflection of textiles and accessories available in that region. Furthermore, if the user is traveling, the regeneration unit can suggest textiles and accessories available at their travel destination. Additionally, if the user is participating in a specific event, the regeneration unit can prioritize the reflection of textiles and accessories related to that event. This allows for the prioritization of highly relevant textiles and accessories based on the user's geographical location.

[0051] The regeneration unit can analyze the user's social media activity during regeneration and reflect relevant textiles and accessories. For example, the regeneration unit can automatically reflect textiles and accessories that the user has shared on social media. Furthermore, the regeneration unit can suggest relevant textiles and accessories based on the user's interests on social media. In addition, the regeneration unit can reflect textiles and accessories based on the accounts the user follows on social media. This allows the regeneration unit to reflect relevant textiles and accessories based on the user's social media activity.

[0052] The conversion unit can adjust the level of detail in the conversion based on the importance of the regenerated 3D data. For example, the conversion unit can generate detailed patterns and specifications based on high-importance 3D data. It can also generate simplified patterns and specifications based on low-importance 3D data. Furthermore, it can generate balanced patterns and specifications based on medium-importance 3D data. This allows the level of detail in the conversion to be adjusted according to the importance of the regenerated 3D data.

[0053] The conversion unit can apply different conversion algorithms depending on the category of the regenerated 3D data during conversion. For example, based on the 3D data of a dress, the conversion unit can apply a conversion algorithm specifically for dresses. Similarly, based on the 3D data of a jacket, the conversion unit can apply a conversion algorithm specifically for jackets. Furthermore, based on the 3D data of pants, the conversion unit can apply a conversion algorithm specifically for pants. This allows the application of the optimal conversion algorithm depending on the category of the regenerated 3D data.

[0054] The conversion unit can determine the conversion priority based on the submission timing of the regenerated 3D data during the conversion process. For example, the conversion unit can prioritize processing 3D data with earlier submission dates and generate patterns and specifications. It can also postpone processing 3D data with later submission dates and generate patterns and specifications for those. Furthermore, the conversion unit can process 3D data with intermediate submission dates in a balanced manner and generate patterns and specifications for those. This allows the conversion priority of patterns and specifications to be determined based on the submission dates.

[0055] The conversion unit can adjust the order of conversion based on the relevance of the regenerated 3D data during the conversion process. For example, the conversion unit can prioritize processing highly relevant 3D data to generate patterns and specifications. It can also postpone processing less relevant 3D data to generate patterns and specifications. Furthermore, the conversion unit can balance the processing of moderately relevant 3D data to generate patterns and specifications. This allows the conversion order of patterns and specifications to be adjusted based on the relevance of the regenerated 3D data.

[0056] The acquisition unit can analyze the user's past selection history during acquisition and propose the optimal acquisition method. For example, the acquisition unit can automatically display textiles and decorative items that the user has frequently selected in the past as candidates. Furthermore, the acquisition unit can prioritize suggesting acquisition methods (online, offline, etc.) that the user has used in the past. In addition, the acquisition unit can predict and suggest textiles and decorative items that the user will use at specific times based on their past selection history. This allows the system to propose the optimal acquisition method based on the user's past selection history.

[0057] The acquisition unit can prioritize the acquisition of highly relevant textiles and decorative items by considering the user's geographical location information during the acquisition process. For example, if the user is in a specific region, the acquisition unit will prioritize the acquisition of textiles and decorative items available in that region. Furthermore, if the user is traveling, the acquisition unit can suggest textiles and decorative items available at their travel destination. Additionally, if the user is participating in a specific event, the acquisition unit can prioritize the acquisition of textiles and decorative items related to that event. This allows for the priority acquisition of highly relevant textiles and decorative items based on the user's geographical location information.

[0058] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can prioritize suggesting delivery methods that the user has frequently used in the past (online, offline, etc.). Furthermore, the service provider can select a delivery method optimized for the device the user has previously used (smartphone, tablet, etc.). In addition, the service provider can predict and suggest the optimal delivery method for a specific time period based on the user's past usage history. This allows for the selection of the optimal delivery method based on the user's past usage history.

[0059] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can provide a delivery method optimized for the larger screen. Furthermore, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible delivery method. This allows the delivery unit to select the optimal delivery method based on the user's device information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze a user's past design history and suggest similar design options. For example, it can automatically display related design options based on the style and color scheme of designs the user has created in the past. It can also consider textiles and decorative items the user has selected in the past and suggest new design options that match them. Furthermore, it can prioritize displaying design options that the user has previously given high ratings to, providing design options that match the user's preferences. In this way, it can suggest the most suitable design options based on the user's past design history.

[0062] The conversion unit can apply different conversion algorithms depending on the category of the regenerated 3D data. For example, based on the 3D data of a dress, a conversion algorithm specifically for dresses can be applied. Similarly, based on the 3D data of a jacket, a conversion algorithm specifically for jackets can be applied. Furthermore, based on the 3D data of pants, a conversion algorithm specifically for pants can be applied. This allows the application of the optimal conversion algorithm according to the category of the regenerated 3D data.

[0063] The reception desk can prioritize receiving input content that is highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, images and text related to that region will be displayed preferentially. Also, if the user is traveling, music and text related to their travel destination can be suggested. Furthermore, if the user is participating in a specific event, input content related to that event can be displayed preferentially. In this way, the system can prioritize receiving input content that is highly relevant based on the user's geographical location.

[0064] The generation unit can apply different generation algorithms depending on the category of the input information during generation. For example, it can apply a natural language processing algorithm to generate a design based on text information. It can also apply an image analysis algorithm to generate a design based on image information. Furthermore, it can apply a music analysis algorithm to generate a design based on music information. This allows the optimal generation algorithm to be applied according to the category of the input information.

[0065] The regeneration unit can apply different regeneration algorithms depending on the textile and decorative item categories selected by the user during the regeneration process. For example, it can regenerate 3D data with a smooth texture based on silk textiles. It can also regenerate 3D data with a robust texture based on leather textiles. Furthermore, it can regenerate 3D data with detailed features based on decorative items such as buttons and zippers. This allows for the application of the optimal regeneration algorithm according to the selected textile and decorative item categories.

[0066] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize suggesting delivery methods that the user has frequently used in the past (online, offline, etc.). Furthermore, the service provider can select a delivery method optimized for the device the user has previously used (smartphone, tablet, etc.). In addition, the service provider can predict and suggest the optimal delivery method for a specific time period based on the user's past usage history. This allows for the selection of the optimal delivery method based on the user's past usage history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception unit accepts image, text, or music input from the user. For example, the reception unit can accept images, text, and music in formats such as JPEG, PNG, MP3, and WAV. Step 2: The generation unit analyzes the information received by the reception unit and generates 2D design proposals. For example, the generation unit uses image recognition algorithms, natural language processing technology, and music analysis technology to analyze images, text, and music and generate 2D design proposals. Step 3: The 3D generation unit generates 3D data based on the 2D design proposal generated by the generation unit. For example, the 3D generation unit generates 3D data in formats such as STL, OBJ, and FBX. Step 4: The regeneration unit regenerates the 3D data based on the 3D data generated by the 3D generation unit, reflecting the textiles and decorations selected by the user. For example, the regeneration unit regenerates the 3D data using the application method of the textiles and decorations and the regeneration algorithm. Step 5: The conversion unit automatically converts the 3D data regenerated by the regeneration unit into patterns and specifications. For example, the conversion unit generates patterns in formats such as PDF and DXF, and specifications including manufacturing procedures, material lists, and dimension drawings.

[0069] (Example of form 2) The clothing design system according to an embodiment of the present invention is an AI tool that allows anyone to design clothing. When a user inputs images, text, music, etc., the AI ​​analyzes the input and generates a 2D design proposal. Next, the AI ​​generates 3D data based on the generated 2D design proposal. Furthermore, the user selects digital textiles and embellishments (buttons, zippers, etc.), and the AI ​​regenerates the 3D data based on these selections. Finally, the system automatically converts the generated 3D data into patterns and specifications. This entire process makes it possible for anyone to easily design, manufacture, and sell clothing. For example, if a user inputs "I want to make a floral dress," the AI ​​analyzes the information and generates a 2D design proposal. Next, the AI ​​generates 3D data based on the generated 2D design proposal. For example, the 2D design proposal is represented three-dimensionally, and the shape and details of the dress are generated as 3D data. Furthermore, the user selects digital textiles and embellishments. For example, if a user selects "I want to use red silk fabric and gold buttons," the AI ​​regenerates the 3D data based on these selections. Finally, the generated 3D data is automatically converted into patterns and specifications. For example, it generates patterns for each part of a dress and specifications including sewing instructions. This entire process makes it possible for anyone to easily design, manufacture, and sell clothing. In short, the clothing design system makes it possible for anyone to easily design, manufacture, and sell clothing.

[0070] The clothing design system according to this embodiment comprises a reception unit, a generation unit, a 3D generation unit, a regeneration unit, and a conversion unit. The reception unit receives image or text and music input from the user. For example, the reception unit can accept images, text, and music in formats such as JPEG, PNG, MP3, and WAV. The generation unit analyzes the information received by the reception unit and generates 2D design proposals. For example, the generation unit uses image recognition algorithms, natural language processing technology, and music analysis technology to analyze images, text, and music and generate 2D design proposals. The 3D generation unit generates 3D data based on the 2D design proposals generated by the generation unit. For example, the 3D generation unit generates 3D data in formats such as STL, OBJ, and FBX. The regeneration unit regenerates 3D data based on the 3D data generated by the 3D generation unit, reflecting textiles and ornaments selected by the user. For example, the regeneration unit regenerates 3D data using methods for applying textiles and ornaments and a regeneration algorithm. The conversion unit automatically converts the 3D data regenerated by the regeneration unit into patterns and specifications. For example, the conversion unit generates patterns in formats such as PDF and DXF, and specifications including manufacturing procedures, material lists, and dimension drawings. As a result, the clothing design system according to this embodiment allows anyone to easily design, manufacture, and sell clothing.

[0071] The clothing design system includes an retrieval unit that obtains textiles and decorative items selected by the user from a database. For example, the retrieval unit searches the database for fabric types and decorative item types, and retrieves the textiles and decorative items selected by the user. This allows for efficient retrieval of the textiles and decorative items selected by the user.

[0072] The clothing design system includes a provisioning unit that provides generated design proposals and 3D data to the user. For example, the provisioning unit provides the generated design proposals and 3D data to the user through methods such as an online platform, download links, or email. This allows the user to review the generated design proposals and 3D data.

[0073] The generation unit can analyze images, text, or music to generate 2D design proposals. For example, it uses image recognition algorithms, natural language processing techniques, and music analysis techniques to analyze images, text, and music and generate 2D design proposals. This allows for the generation of 2D design proposals from diverse input information.

[0074] The 3D generation unit can generate 3D data based on 2D design proposals. The 3D generation unit generates 3D data in formats such as STL, OBJ, and FBX. This allows for a three-dimensional representation of 2D design proposals.

[0075] The regeneration unit can regenerate 3D data that reflects the textiles and decorative items selected by the user. For example, the regeneration unit regenerates the 3D data using methods for applying the textiles and decorative items, and a regeneration algorithm. This allows for the regeneration of 3D data based on the user's selections.

[0076] The conversion unit can automatically convert the regenerated 3D data into patterns and specifications. For example, the conversion unit generates patterns in formats such as PDF and DXF, and specifications including manufacturing procedures, material lists, and dimension drawings. This allows for the automatic generation of patterns and specifications from 3D data.

[0077] The reception desk can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is tense, the reception desk can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, it can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, it can provide a simple and highly visible interface to facilitate the input process. In this way, the system can provide an interface that responds to the user's emotions.

[0078] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display images and text that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest images and text that the user will use at specific times based on their past input history. This allows the system to suggest the optimal input method based on the user's past input history.

[0079] The input system can filter input based on the user's current projects and areas of interest. For example, it might prioritize displaying images and text related to the user's current projects. It can also suggest relevant music and text based on the user's areas of interest. Furthermore, it can filter input based on areas the user has shown interest in in the past. This allows for filtering of input based on the user's projects and areas of interest.

[0080] The reception system can estimate the user's emotions and prioritize input content based on those emotions. For example, if the user is stressed, the reception system can prioritize displaying simple input content. If the user is relaxed, it can provide more detailed input options. Furthermore, if the user is in a hurry, it can prioritize displaying content that can be entered quickly. This allows the system to prioritize input content according to the user's emotions.

[0081] The input system can prioritize accepting highly relevant input content by considering the user's geographical location. For example, if the user is in a specific region, the input system can prioritize displaying images and text related to that region. If the user is traveling, it can suggest music and text related to their travel destination. Furthermore, if the user is attending a specific event, it can prioritize displaying input content related to that event. This allows the system to prioritize accepting highly relevant input content based on the user's geographical location.

[0082] The input system can analyze the user's social media activity and accept relevant input content during the input process. For example, it can automatically display images and text shared by the user on social media as suggestions. It can also suggest relevant music and text based on the user's social media interests. Furthermore, it can filter input content based on the accounts the user follows on social media. This allows the system to accept relevant input content based on the user's social media activity.

[0083] The generation unit can estimate the user's emotions and adjust the expression method of the 2D design proposals generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate designs that use soft colors and many curves. If the user is excited, it can generate designs with vibrant colors and bold designs. Furthermore, if the user is calm, it can generate simple and sophisticated designs. In this way, 2D design proposals can be generated that are appropriate for the user's emotions.

[0084] The generation unit can adjust the level of detail of the generated design based on the importance of the input information. For example, it can generate a detailed design based on highly important information. It can also generate a simplified design based on less important information. Furthermore, it can generate a balanced design based on moderately important information. This allows the level of detail of the generated design to be adjusted according to the importance of the input information.

[0085] The generation unit can apply different generation algorithms depending on the category of the input information during generation. For example, it can apply a natural language processing algorithm to generate a design based on text information. It can also apply an image analysis algorithm to generate a design based on image information. Furthermore, it can apply a music analysis algorithm to generate a design based on music information. This allows the optimal generation algorithm to be applied according to the category of the input information.

[0086] The generation unit can estimate the user's emotions and adjust the length of the 2D design proposals it generates based on those emotions. For example, if the user is in a hurry, the generation unit can generate short, concise design proposals. If the user is relaxed, it can generate longer design proposals that include detailed explanations. Furthermore, if the user is excited, it can generate design proposals with visually stimulating effects. This allows for the generation of 2D design proposals of appropriate length based on the user's emotions.

[0087] The generation unit can determine the generation priority based on the submission timing of the input information during the generation process. For example, the generation unit can prioritize processing information submitted earlier and generate the design. It can also postpone processing information submitted later and generate the design. Furthermore, the generation unit can process information submitted at a moderate stage in a balanced manner and generate the design. This allows the generation priority to be determined based on the submission timing.

[0088] The generation unit can adjust the generation order based on the relevance of the input information during generation. For example, the generation unit can prioritize processing highly relevant information and generate a design. It can also postpone processing less relevant information and generate a design. Furthermore, it can balance the processing of moderately relevant information and generate a design. This allows the generation order to be adjusted based on the relevance of the input information.

[0089] The 3D generation unit can estimate the user's emotions and adjust the 3D data generation method based on the estimated emotions. For example, if the user is relaxed, the 3D generation unit can generate 3D data that makes extensive use of soft curves. If the user is excited, the 3D generation unit can generate 3D data with vivid colors and bold shapes. Furthermore, if the user is calm, the 3D generation unit can generate simple and refined 3D data. This allows for the generation of 3D data that matches the user's emotions.

[0090] The 3D generation unit can adjust the level of detail generated based on the importance of the 2D design proposals during 3D data generation. For example, the 3D generation unit can generate detailed 3D data based on high-importance 2D design proposals. It can also generate simplified 3D data based on low-importance 2D design proposals. Furthermore, it can generate balanced 3D data based on 2D design proposals of moderate importance. This allows the level of detail of the 3D data to be adjusted according to the importance of the 2D design proposals.

[0091] The 3D generation unit can apply different generation algorithms depending on the category of the 2D design proposal when generating 3D data. For example, the 3D generation unit can generate 3D data by applying a natural language processing algorithm based on text information. It can also generate 3D data by applying an image analysis algorithm based on image information. Furthermore, it can generate 3D data by applying a music analysis algorithm based on music information. This allows for the application of the optimal generation algorithm depending on the category of the 2D design proposal.

[0092] The 3D generation unit can estimate the user's emotions and adjust the generation order of 3D data based on those emotions. For example, if the user is in a hurry, the 3D generation unit will generate 3D data starting with the most important parts. If the user is relaxed, the 3D generation unit can generate the entire model in a balanced manner. Furthermore, if the user is excited, the 3D generation unit can prioritize generating visually stimulating parts. This allows for the generation of 3D data in an order that corresponds to the user's emotions.

[0093] The 3D generation unit can determine the generation priority based on the submission timing of 2D design proposals when generating 3D data. For example, the 3D generation unit can prioritize processing 2D design proposals submitted earlier and generate 3D data from them. It can also postpone processing 2D design proposals submitted later and generate 3D data from them. Furthermore, the 3D generation unit can process 2D design proposals submitted at intermediate times in a balanced manner and generate 3D data from them. This allows the generation priority of 3D data to be determined based on the submission timing.

[0094] The 3D generation unit can adjust the generation order based on the relevance of 2D design proposals when generating 3D data. For example, the 3D generation unit can prioritize processing highly relevant 2D design proposals and generate 3D data from them. It can also postpone processing less relevant 2D design proposals and generate 3D data from them. Furthermore, the 3D generation unit can process 2D design proposals of moderate relevance in a balanced manner and generate 3D data from them. In this way, the generation order of 3D data can be adjusted based on the relevance of 2D design proposals.

[0095] The regeneration unit can estimate the user's emotions and adjust the representation of the regenerated 3D data based on those emotions. For example, if the user is relaxed, the regeneration unit can regenerate 3D data with soft colors and many curves. If the user is excited, the regeneration unit can regenerate 3D data with vivid colors and bold shapes. Furthermore, if the user is calm, the regeneration unit can regenerate simple and refined 3D data. This allows for the regeneration of 3D data that responds to the user's emotions.

[0096] The regeneration unit can adjust the level of detail in the regeneration based on the importance of the textiles and decorative items selected by the user. For example, the regeneration unit can regenerate detailed 3D data based on textiles and decorative items of high importance. It can also regenerate simplified 3D data based on textiles and decorative items of low importance. Furthermore, it can regenerate balanced 3D data based on textiles and decorative items of medium importance. This allows the level of detail in the regeneration to be adjusted according to the importance of the selected textiles and decorative items.

[0097] The regeneration unit can apply different regeneration algorithms depending on the textile and decorative item categories selected by the user during the regeneration process. For example, the regeneration unit can regenerate 3D data with a smooth texture based on silk textiles. It can also regenerate 3D data with a robust texture based on leather textiles. Furthermore, the regeneration unit can regenerate 3D data with detailed features based on decorative items such as buttons and zippers. This allows the application of the optimal regeneration algorithm according to the selected textile and decorative item categories.

[0098] The regeneration unit can estimate the user's emotions and determine the priority of the 3D data to be regenerated based on those emotions. For example, if the user is in a hurry, the regeneration unit will regenerate the 3D data starting with the most important parts. If the user is relaxed, the regeneration unit can regenerate the entire image in a balanced way. Furthermore, if the user is excited, the regeneration unit can prioritize the regeneration of visually stimulating parts. This allows for the regeneration of 3D data with priorities that correspond to the user's emotions.

[0099] The regeneration unit can prioritize the reflection of highly relevant textiles and accessories, taking into account the user's geographical location during the regeneration process. For example, if the user is in a specific region, the regeneration unit will prioritize the reflection of textiles and accessories available in that region. Furthermore, if the user is traveling, the regeneration unit can suggest textiles and accessories available at their travel destination. Additionally, if the user is participating in a specific event, the regeneration unit can prioritize the reflection of textiles and accessories related to that event. This allows for the prioritization of highly relevant textiles and accessories based on the user's geographical location.

[0100] The regeneration unit can analyze the user's social media activity during regeneration and reflect relevant textiles and accessories. For example, the regeneration unit can automatically reflect textiles and accessories that the user has shared on social media. Furthermore, the regeneration unit can suggest relevant textiles and accessories based on the user's interests on social media. In addition, the regeneration unit can reflect textiles and accessories based on the accounts the user follows on social media. This allows the regeneration unit to reflect relevant textiles and accessories based on the user's social media activity.

[0101] The conversion unit can estimate the user's emotions and adjust the conversion method of patterns and specifications based on the estimated user emotions. For example, if the user is relaxed, the conversion unit can generate specifications that include detailed explanations. If the user is in a hurry, the conversion unit can generate concise and to-the-point specifications. Furthermore, if the user is excited, the conversion unit can generate specifications with visually stimulating effects. This allows for the generation of patterns and specifications that are tailored to the user's emotions.

[0102] The conversion unit can adjust the level of detail in the conversion based on the importance of the regenerated 3D data. For example, the conversion unit can generate detailed patterns and specifications based on high-importance 3D data. It can also generate simplified patterns and specifications based on low-importance 3D data. Furthermore, it can generate balanced patterns and specifications based on medium-importance 3D data. This allows the level of detail in the conversion to be adjusted according to the importance of the regenerated 3D data.

[0103] The conversion unit can apply different conversion algorithms depending on the category of the regenerated 3D data during conversion. For example, based on the 3D data of a dress, the conversion unit can apply a conversion algorithm specifically for dresses. Similarly, based on the 3D data of a jacket, the conversion unit can apply a conversion algorithm specifically for jackets. Furthermore, based on the 3D data of pants, the conversion unit can apply a conversion algorithm specifically for pants. This allows the application of the optimal conversion algorithm depending on the category of the regenerated 3D data.

[0104] The conversion unit can estimate the user's emotions and determine the priority of patterns and specifications based on those emotions. For example, if the user is in a hurry, the conversion unit will generate patterns and specifications starting with the most important parts. If the user is relaxed, the conversion unit can generate the entire design in a balanced manner. Furthermore, if the user is excited, the conversion unit can prioritize generating visually stimulating parts. This allows for the generation of patterns and specifications with priorities that correspond to the user's emotions.

[0105] The conversion unit can determine the conversion priority based on the submission timing of the regenerated 3D data during the conversion process. For example, the conversion unit can prioritize processing 3D data with earlier submission dates and generate patterns and specifications. It can also postpone processing 3D data with later submission dates and generate patterns and specifications for those. Furthermore, the conversion unit can process 3D data with intermediate submission dates in a balanced manner and generate patterns and specifications for those. This allows the conversion priority of patterns and specifications to be determined based on the submission dates.

[0106] The conversion unit can adjust the order of conversion based on the relevance of the regenerated 3D data during the conversion process. For example, the conversion unit can prioritize processing highly relevant 3D data to generate patterns and specifications. It can also postpone processing less relevant 3D data to generate patterns and specifications. Furthermore, the conversion unit can balance the processing of moderately relevant 3D data to generate patterns and specifications. This allows the conversion order of patterns and specifications to be adjusted based on the relevance of the regenerated 3D data.

[0107] The acquisition unit can estimate the user's emotions and adjust how textiles and decorative items are acquired based on those estimated emotions. For example, if the user is relaxed, the acquisition unit will prioritize acquiring textiles with a soft texture. If the user is excited, the acquisition unit can prioritize acquiring textiles with bright colors. Furthermore, if the user is calm, the acquisition unit can prioritize acquiring simple and sophisticated decorative items. This allows the acquisition method of textiles and decorative items to be adjusted according to the user's emotions.

[0108] The acquisition unit can analyze the user's past selection history during acquisition and propose the optimal acquisition method. For example, the acquisition unit can automatically display textiles and decorative items that the user has frequently selected in the past as candidates. Furthermore, the acquisition unit can prioritize suggesting acquisition methods (online, offline, etc.) that the user has used in the past. In addition, the acquisition unit can predict and suggest textiles and decorative items that the user will use at specific times based on their past selection history. This allows the system to propose the optimal acquisition method based on the user's past selection history.

[0109] The acquisition unit can estimate the user's emotions and determine the priority of textiles and decorative items to acquire based on those estimated emotions. For example, if the user is in a hurry, the acquisition unit will prioritize acquiring important textiles and decorative items. If the user is relaxed, the acquisition unit can acquire a balanced selection of items. Furthermore, if the user is excited, the acquisition unit can prioritize acquiring visually stimulating textiles and decorative items. This allows for the acquisition of textiles and decorative items with priorities that correspond to the user's emotions.

[0110] The acquisition unit can prioritize the acquisition of highly relevant textiles and decorative items by considering the user's geographical location information during the acquisition process. For example, if the user is in a specific region, the acquisition unit will prioritize the acquisition of textiles and decorative items available in that region. Furthermore, if the user is traveling, the acquisition unit can suggest textiles and decorative items available at their travel destination. Additionally, if the user is participating in a specific event, the acquisition unit can prioritize the acquisition of textiles and decorative items related to that event. This allows for the priority acquisition of highly relevant textiles and decorative items based on the user's geographical location information.

[0111] The service provider can estimate the user's emotions and adjust the way design proposals and 3D data are provided based on those estimated emotions. For example, if the user is relaxed, the service provider can provide design proposals and 3D data that include detailed explanations. If the user is in a hurry, the service provider can provide concise and to-the-point design proposals and 3D data. Furthermore, if the user is excited, the service provider can provide design proposals and 3D data with visually stimulating effects. This allows the service provider to adjust the way design proposals and 3D data are provided according to the user's emotions.

[0112] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider can prioritize suggesting delivery methods that the user has frequently used in the past (online, offline, etc.). Furthermore, the service provider can select a delivery method optimized for the device the user has previously used (smartphone, tablet, etc.). In addition, the service provider can predict and suggest the optimal delivery method for a specific time period based on the user's past usage history. This allows for the selection of the optimal delivery method based on the user's past usage history.

[0113] The service provider can estimate the user's emotions and adjust the order in which design proposals and 3D data are provided based on those estimated emotions. For example, if the user is in a hurry, the service provider will prioritize providing important design proposals and 3D data. If the user is relaxed, the service provider can provide everything in a balanced manner. Furthermore, if the user is excited, the service provider can prioritize providing visually stimulating design proposals and 3D data. This allows the service provider to deliver design proposals and 3D data in an order that matches the user's emotions.

[0114] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can provide a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can provide a delivery method optimized for the larger screen. Furthermore, if the user is using a smartwatch, the delivery unit can provide a concise and highly visible delivery method. This allows the delivery unit to select the optimal delivery method based on the user's device information. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, 3D generation unit, regeneration unit, conversion unit, acquisition unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input of images, text, and music from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to generate a 2D design proposal. The 3D generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates 3D data based on the generated 2D design proposal. The regeneration unit is implemented by the specific processing unit 290 of the data processing unit 12 and regenerates the 3D data reflecting the textiles and ornaments selected by the user. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically converts the regenerated 3D data into patterns and specifications. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12, which acquires the textiles and decorative items selected by the user from the database. The provision unit is implemented by the control unit 46A of the smart device 14, which provides the generated design proposals and 3D data to the user. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, 3D generation unit, regeneration unit, conversion unit, acquisition unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input of images, text, and music from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to generate a 2D design proposal. The 3D generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates 3D data based on the generated 2D design proposal. The regeneration unit is implemented by the specific processing unit 290 of the data processing unit 12 and regenerates the 3D data reflecting the textiles and ornaments selected by the user. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically converts the regenerated 3D data into patterns and specifications. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12, which acquires textiles and decorative items selected by the user from the database. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides the generated design proposals and 3D data to the user. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, 3D generation unit, regeneration unit, conversion unit, acquisition unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input of images, text, and music from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to generate a 2D design proposal. The 3D generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates 3D data based on the generated 2D design proposal. The regeneration unit is implemented by the specific processing unit 290 of the data processing unit 12 and regenerates 3D data reflecting the textiles and ornaments selected by the user. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically converts the regenerated 3D data into patterns and specifications. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12, which acquires the textiles and decorative items selected by the user from the database. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides the generated design proposals and 3D data to the user. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, 3D generation unit, regeneration unit, conversion unit, acquisition unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input of images, text, and music from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to generate a 2D design proposal. The 3D generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates 3D data based on the generated 2D design proposal. The regeneration unit is implemented by the specific processing unit 290 of the data processing unit 12 and regenerates the 3D data reflecting the textiles and decorations selected by the user. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically converts the regenerated 3D data into patterns and specifications. The acquisition unit is implemented by the specific processing unit 290 of the data processing device 12, which acquires textiles and decorative items selected by the user from the database. The provision unit is implemented by the control unit 46A of the robot 414, which provides the generated design proposals and 3D data to the user.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The reception desk can analyze a user's past design history and suggest similar design options. For example, it can automatically display related design options based on the style and color scheme of designs the user has created in the past. It can also consider textiles and decorative items the user has selected in the past and suggest new design options that match them. Furthermore, it can prioritize displaying design options that the user has previously given high ratings to, providing design options that match the user's preferences. In this way, it can suggest the most suitable design options based on the user's past design history.

[0117] The generation unit can estimate the user's emotions and adjust the color scheme of the generated 2D design proposals based on those emotions. For example, if the user is relaxed, it can generate a design that uses many soft pastel colors. If the user is excited, it can generate a design with vibrant colors and high contrast. Furthermore, if the user is calm, it can generate a simple, monochrome design. This allows for the generation of 2D design proposals with color schemes that match the user's emotions.

[0118] The regeneration unit can estimate the user's emotions and adjust the detail of the regenerated 3D data based on those emotions. For example, if the user is relaxed, it can regenerate 3D data with lots of fine detail. If the user is excited, it can regenerate 3D data with bold and eye-catching details. Furthermore, if the user is calm, it can regenerate 3D data with simple and refined details. This allows for the regeneration of 3D data with details that match the user's emotions.

[0119] The conversion unit can apply different conversion algorithms depending on the category of the regenerated 3D data. For example, based on the 3D data of a dress, a conversion algorithm specifically for dresses can be applied. Similarly, based on the 3D data of a jacket, a conversion algorithm specifically for jackets can be applied. Furthermore, based on the 3D data of pants, a conversion algorithm specifically for pants can be applied. This allows the application of the optimal conversion algorithm according to the category of the regenerated 3D data.

[0120] The service provider can estimate the user's emotions and adjust the way design proposals and 3D data are provided based on those estimated emotions. For example, if the user is relaxed, the service provider can provide design proposals and 3D data that include detailed explanations. If the user is in a hurry, the service provider can provide concise and to-the-point design proposals and 3D data. Furthermore, if the user is excited, the service provider can provide design proposals and 3D data with visually stimulating effects. This allows the service provider to adjust the way design proposals and 3D data are provided according to the user's emotions.

[0121] The reception desk can prioritize receiving input content that is highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, images and text related to that region will be displayed preferentially. Also, if the user is traveling, music and text related to their travel destination can be suggested. Furthermore, if the user is participating in a specific event, input content related to that event can be displayed preferentially. In this way, the system can prioritize receiving input content that is highly relevant based on the user's geographical location.

[0122] The generation unit can apply different generation algorithms depending on the category of the input information during generation. For example, it can apply a natural language processing algorithm to generate a design based on text information. It can also apply an image analysis algorithm to generate a design based on image information. Furthermore, it can apply a music analysis algorithm to generate a design based on music information. This allows the optimal generation algorithm to be applied according to the category of the input information.

[0123] The regeneration unit can apply different regeneration algorithms depending on the textile and decorative item categories selected by the user during the regeneration process. For example, it can regenerate 3D data with a smooth texture based on silk textiles. It can also regenerate 3D data with a robust texture based on leather textiles. Furthermore, it can regenerate 3D data with detailed features based on decorative items such as buttons and zippers. This allows for the application of the optimal regeneration algorithm according to the selected textile and decorative item categories.

[0124] The conversion unit can estimate the user's emotions and adjust the conversion method of patterns and specifications based on the estimated user emotions. For example, if the user is relaxed, it can generate specifications that include detailed explanations. If the user is in a hurry, the conversion unit can generate concise and to-the-point specifications. Furthermore, if the user is excited, the conversion unit can generate specifications with visually stimulating effects. This allows for the generation of patterns and specifications that are tailored to the user's emotions.

[0125] The service provider can select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize suggesting delivery methods that the user has frequently used in the past (online, offline, etc.). Furthermore, the service provider can select a delivery method optimized for the device the user has previously used (smartphone, tablet, etc.). In addition, the service provider can predict and suggest the optimal delivery method for a specific time period based on the user's past usage history. This allows for the selection of the optimal delivery method based on the user's past usage history.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The reception unit accepts image, text, or music input from the user. For example, the reception unit can accept images, text, and music in formats such as JPEG, PNG, MP3, and WAV. Step 2: The generation unit analyzes the information received by the reception unit and generates 2D design proposals. For example, the generation unit uses image recognition algorithms, natural language processing technology, and music analysis technology to analyze images, text, and music and generate 2D design proposals. Step 3: The 3D generation unit generates 3D data based on the 2D design proposal generated by the generation unit. For example, the 3D generation unit generates 3D data in formats such as STL, OBJ, and FBX. Step 4: The regeneration unit regenerates the 3D data based on the 3D data generated by the 3D generation unit, reflecting the textiles and decorations selected by the user. For example, the regeneration unit regenerates the 3D data using the application method of the textiles and decorations and the regeneration algorithm. Step 5: The conversion unit automatically converts the 3D data regenerated by the regeneration unit into patterns and specifications. For example, the conversion unit generates patterns in formats such as PDF and DXF, and specifications including manufacturing procedures, material lists, and dimension drawings.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception unit that accepts image, text, or music input from the user, A generation unit analyzes the information received by the reception unit and generates a 2D design proposal, A 3D generation unit generates 3D data based on the 2D design proposal generated by the generation unit, A regeneration unit regenerates 3D data based on the 3D generation unit, reflecting the textile or decorative item selected by the user. The system includes a conversion unit that automatically converts the 3D data regenerated by the regeneration unit into a pattern and specifications. A system characterized by the following features.

2. It includes an acquisition unit that retrieves textiles and decorative items selected by the user from a database. The system according to feature 1.

3. It includes a provisioning unit that provides users with generated design proposals and 3D data. The system according to feature 1.

4. The generating unit is It analyzes images, text, or music to generate 2D design proposals. The system according to feature 1.

5. The 3D generation unit is Generate 3D data based on 2D design proposals. The system according to feature 1.

6. The regeneration unit, Regenerate 3D data to reflect the textiles and decorations selected by the user. The system according to feature 1.

7. The conversion unit is Automatically converts the regenerated 3D data into patterns and specifications. The system according to feature 1.

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

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

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