system

Abstract

The system according to this embodiment aims to allow users to create their desired nail designs and print them in real time. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a printing unit. The reception unit receives an image of the design desired by the user. The generation unit analyzes the image received by the reception unit and generates a 3D model. The printing unit prints the design based on the 3D model generated by the generation unit.

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 steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to create their desired nail design by themselves and print it in real time, and the degree of freedom of design is limited.

[0005] The system according to the embodiment aims to enable a user to create their desired nail design by themselves and print it in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a printing unit. The reception unit receives an image of the design desired by the user. The generation unit analyzes the image received by the reception unit and generates a 3D model. The printing unit prints the design based on the 3D model generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to create their desired nail designs and print them in real time. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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 nail art system according to an embodiment of the present invention is a system in which a user inputs an image of their desired design, an AI analyzes the image to generate a 3D model, and a nail printer prints the design based on that 3D model. This nail art system allows users to input an image of their desired design, and by analyzing that image to generate a 3D model, it can realize the user's desired design without being limited by existing designs. It is easy to operate and can be used by individuals as well as in stores such as convenience stores and gyms. Since it can be molded in about 1 minute per finger, it is possible to significantly reduce the time required compared to the 2-3 hours that traditional nail salons take. For example, the user inputs an image of their desired design. At this time, the user only needs to input the starting point and destination. For example, the user inputs "I want to go from my home to the station." This information is input into the generating AI. Next, the generating AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generating AI calculates the optimal route based on map data and generates a video along that route. For example, if the user inputs a route from home to the station, a video along that route will be generated. The generated video starts navigation according to the orientation of the user's smartphone. For example, if a user points their smartphone north, the video will also start navigating in the north direction. This allows the user to receive navigation aligned with their own direction of travel. Furthermore, the video screen moves in sync with the user's walking speed. For example, if the user walks slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it appealing to everyone. Users can receive intuitive navigation without complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if a user is walking with their smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This enables the nail art system to print the user's desired design in real time.

[0029] The nail art system according to this embodiment comprises a reception unit, a generation unit, and a printing unit. The reception unit receives an image of the design desired by the user. The image of the design desired by the user includes, but is not limited to, graphic designs and textile designs. The reception unit provides, for example, an interface for uploading an image of the design desired by the user. The reception unit can also provide a tool for selecting an image of the design desired by the user. For example, the reception unit provides an interface that allows the user to upload a design image by drag and drop. The generation unit uses a generation AI to analyze the image input by the reception unit and generate a 3D model. The generation unit analyzes the image using, for example, an image recognition algorithm or a feature extraction technique. The generation unit can also generate a 3D model using techniques such as mesh generation or voxelization. For example, the generation unit extracts features from an image and generates a 3D model based on them. The generation unit can perform image analysis and 3D model generation using a generation AI. The generation AI can use, for example, techniques such as deep learning or GAN (Generative Opposite Network). The printing unit prints designs based on 3D models generated by the generation unit. The printing unit prints designs using technologies such as inkjet printing or laser printing. The printing unit can also print designs onto the surface of nails based on the generated 3D model. For example, the printing unit prints designs based on the scale and resolution of the 3D model. The printing unit can accurately print designs based on the generated 3D model. As a result, the nail art system according to this embodiment can print the user's desired design in real time.

[0030] The generation unit can analyze images using generative AI and generate 3D models. For example, the generation unit analyzes images using generative AI. For example, the generation unit extracts image features using deep learning and generates a 3D model. The generation unit can also analyze images and generate 3D models using GAN (Generative Opposite Network). For example, the generation unit extracts image features and generates a 3D model based on them. The generation unit can perform image analysis and 3D model generation using generative AI. The generative AI can use technologies such as deep learning and GAN (Generative Opposite Network). As a result, the accuracy of image analysis and 3D model generation is improved by using generative AI. Some or all of the above-described processes in the generation unit may be performed using generative AI, or they may not be performed using generative AI. For example, the generation unit can perform image analysis and 3D model generation using a generative AI model that takes an image as input and outputs a 3D model.

[0031] The printing unit can print designs based on the generated 3D model. For example, the printing unit prints designs based on the generated 3D model. For example, the printing unit uses inkjet printing technology to print the 3D model design onto the surface of the nail. The printing unit can also print 3D model designs using laser printing technology. For example, the printing unit prints designs based on the scale and resolution of the 3D model. The printing unit can accurately print designs based on the generated 3D model. This allows for accurate printing of designs based on the generated 3D model. Some or all of the above processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can print designs using an AI model that takes the generated 3D model as input and prints the design.

[0032] The reception desk can analyze the user's past design history and propose the most suitable design. For example, the reception desk can use data mining techniques to analyze the user's past design history. For example, the reception desk can analyze the trends of designs the user has chosen in the past and propose similar designs. The reception desk can also propose new designs based on the color schemes the user has preferred in the past. Furthermore, the reception desk can eliminate designs the user has avoided in the past and propose designs that the user will like. This makes it possible to propose the most suitable design based on the user's past design history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past design history data into a generating AI and have the generating AI propose the most suitable design.

[0033] The reception desk can filter designs based on the user's current fashion and events when receiving design requests. For example, the reception desk can refer to a fashion database and an event calendar to filter designs based on the user's current fashion and events. For instance, if the user is attending a specific event, the reception desk can suggest a design suitable for that event. The reception desk can also suggest designs that match the user's current fashion style. Furthermore, the reception desk can suggest appropriate designs based on the season and trends. This enables the reception desk to suggest designs that are suitable for the user's current fashion and events. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's fashion data and event information into a generating AI and have the generating AI perform the filtering.

[0034] The reception desk can prioritize suggesting highly relevant designs based on the user's geographical location information when a design is submitted. For example, the reception desk can use GPS data to obtain the user's geographical location information. For instance, the reception desk can obtain the user's current location from GPS data and suggest highly relevant designs based on that location information. The reception desk can also obtain the user's geographical location information using location services. For example, if the user is in a specific region, the reception desk can suggest designs based on the culture and trends of that region. Furthermore, if the user is traveling, the reception desk can suggest designs that are themed around local products or scenery of the travel destination. For example, if the user is in a specific city, the reception desk can suggest designs that match the fashion trends of that city. This enables the suggestion of highly relevant designs based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI suggest highly relevant designs.

[0035] The reception desk can suggest relevant designs based on the user's social media activity when a design request is received. For example, the reception desk can use data mining techniques to analyze the user's social media activity. For instance, it can suggest relevant designs based on images and posts the user has shared on social media. It can also analyze the trends of accounts the user follows and suggest relevant designs. Furthermore, it can suggest preferred designs based on posts the user has "liked." This enables the suggestion of relevant designs based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant designs.

[0036] The generation unit can adjust the level of detail of the 3D model based on the complexity of the design during generation. For example, the generation unit uses image analysis techniques to evaluate the complexity of the design. For example, the generation unit analyzes the number of elements and level of detail of the design and adjusts the level of detail of the 3D model based on that. The generation unit can also adjust the level of detail of the 3D model by adjusting the mesh resolution and the number of polygons. For example, in the case of a complex design, the generation unit generates a 3D model that reproduces even the smallest details in great detail. In the case of a simple design, the generation unit can also generate a 3D model that emphasizes the basic shape. Furthermore, in the case of a design of moderate complexity, the generation unit can generate a 3D model with a balanced level of detail. This makes it possible to adjust the level of detail of the 3D model according to the complexity of the design. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for evaluating the complexity of the design into a generation AI and have the generation AI perform the adjustment of the level of detail of the 3D model.

[0037] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit can use clustering techniques to classify design categories. For instance, the generation unit can perform clustering based on design features and classify categories. The generation unit can also apply different generation algorithms depending on the design category. For example, in the case of a flower design, the generation unit can apply an algorithm that reproduces the shape and color of the petals. In the case of an animal design, the generation unit can also apply an algorithm that emphasizes the characteristics of the animal. Furthermore, in the case of a geometric design, the generation unit can apply an algorithm that accurately reproduces geometric patterns. This makes it possible to apply generation algorithms according to the design category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for classifying design categories into a generation AI and have the generation AI execute the application of the generation algorithm.

[0038] The generation unit can determine the priority of 3D models based on the design submission date during generation. For example, the generation unit can use timestamps to evaluate the design submission date. For example, the generation unit can record the design submission date and time with a timestamp and determine the priority of 3D models based on that. The generation unit can also determine the priority of 3D models based on the submission order. For example, the generation unit can prioritize generating designs that were submitted early. The generation unit can also prioritize generating designs with approaching deadlines. Furthermore, the generation unit can generate 3D models in an appropriate order according to the submission date. This makes it possible to prioritize 3D models according to the design submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input design submission date data into a generation AI and have the generation AI perform the determination of 3D model priority.

[0039] The generation unit can adjust the order of 3D models based on the relevance of the designs during generation. For example, the generation unit can use data mining techniques to evaluate the relevance of the designs. For example, the generation unit can prioritize generating highly relevant designs based on the user's past design history. The generation unit can also prioritize generating highly relevant designs based on current trends. Furthermore, the generation unit can prioritize generating highly relevant designs based on the user's preferences. This makes it possible to adjust the order of 3D models according to the relevance of the designs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for evaluating the relevance of designs into a generation AI and have the generation AI perform the adjustment of the order of the 3D models.

[0040] The printing unit can adjust the level of detail in the print based on the complexity of the design during printing. For example, the printing unit uses image analysis technology to evaluate the complexity of the design. For example, the printing unit analyzes the number of elements and the level of detail in the design and adjusts the level of detail in the print based on that. The printing unit can also adjust the level of detail in the print by adjusting the resolution and the amount of ink used. For example, in the case of a complex design, the printing unit will print with high detail down to the smallest detail. In the case of a simple design, the printing unit can also print with an emphasis on the basic shape. Furthermore, in the case of a design of moderate complexity, the printing unit can print with a balanced level of detail. This makes it possible to adjust the level of detail in the print according to the complexity of the design. Some or all of the above processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input data for evaluating the complexity of the design into a generating AI and have the generating AI perform the adjustment of the level of detail in the print.

[0041] The printing unit can apply different printing algorithms depending on the design category during printing. For example, the printing unit can use clustering techniques to classify design categories. For instance, the printing unit can perform clustering based on the design features and classify the categories. The printing unit can also apply different printing algorithms depending on the design category. For example, in the case of a flower design, the printing unit can apply an algorithm that reproduces the shape and color of the petals. In the case of an animal design, the printing unit can also apply an algorithm that emphasizes the characteristics of the animal. Furthermore, in the case of a geometric design, the printing unit can apply an algorithm that accurately reproduces the geometric pattern. This makes it possible to apply printing algorithms according to the design category. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input data for classifying design categories into a generating AI and have the generating AI execute the application of printing algorithms.

[0042] The printing unit can determine the printing priority based on the design submission date. For example, the printing unit can use timestamps to evaluate the design submission date. For instance, the printing unit can record the design submission date and time with a timestamp and determine the printing priority based on that. The printing unit can also determine the printing priority based on the submission order. For example, the printing unit can prioritize printing designs submitted early. The printing unit can also prioritize printing designs with approaching deadlines. Furthermore, the printing unit can print in an appropriate order according to the submission date. This makes it possible to determine the printing priority according to the design submission date. Some or all of the above processing in the printing unit may be performed using AI, for example, or not using AI. For example, the printing unit can input design submission date data into a generating AI and have the generating AI perform the determination of printing priority.

[0043] The printing unit can adjust the printing order based on the relevance of designs during printing. For example, the printing unit can use data mining techniques to evaluate the relevance of designs. For example, the printing unit can prioritize printing highly relevant designs based on the user's past design history. The printing unit can also prioritize printing highly relevant designs based on current trends. Furthermore, the printing unit can prioritize printing highly relevant designs based on the user's preferences. This makes it possible to adjust the printing order according to the relevance of designs. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input data for evaluating the relevance of designs into a generating AI and have the generating AI perform the adjustment of the printing order.

[0044] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0045] The reception desk can analyze the user's past purchase history and suggest the most suitable design. For example, the reception desk can analyze the trends of nail designs the user has purchased in the past and suggest similar designs. It can also suggest new designs based on the user's past preferred color palette. Furthermore, the reception desk can eliminate designs the user has avoided in the past and suggest designs they might like. This enables the system to suggest the most suitable design based on the user's past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past purchase history data into a generating AI and have the AI ​​generate and suggest the most suitable design.

[0046] The generation unit can adjust the 3D model generation method based on the user's preferred style. For example, the generation unit can obtain the user's preferred style in advance from a questionnaire or selection options and generate a 3D model based on that. Furthermore, the generation unit can apply different generation algorithms depending on the style chosen by the user. For instance, if the user prefers a classic style, the generation unit will generate a 3D model that heavily utilizes classic design elements. If the user prefers a modern style, the generation unit can also generate a 3D model that heavily utilizes modern design elements. This allows for adjustment of the 3D model generation method to suit the user's preferences.

[0047] The printing unit can adjust the print color based on the user's skin tone. For example, the printing unit can capture the user's skin tone with a camera and adjust the print color based on that. Furthermore, the printing unit can fine-tune the color of the user's chosen design to match their skin tone. For instance, if the user has light skin, the printing unit will slightly darken the design's colors. Conversely, if the user has dark skin, the printing unit can slightly lighten the design's colors. This allows for optimal print color adjustment according to the user's skin tone.

[0048] The generation unit can adjust the level of detail of the 3D model based on the complexity of the design during generation. For example, the generation unit analyzes the number of elements and level of detail of the design and adjusts the level of detail of the 3D model accordingly. It can also adjust the level of detail of the 3D model by adjusting the mesh resolution and polygon count. For example, in the case of a complex design, it can generate a 3D model that reproduces even the smallest details in great detail. Conversely, in the case of a simple design, it can generate a 3D model that emphasizes the basic shape. This allows for adjustment of the level of detail of the 3D model according to the complexity of the design.

[0049] The printing unit can apply different printing algorithms depending on the design category during printing. For example, the printing unit performs clustering based on the design's characteristics to classify it into categories. Furthermore, the printing unit can apply different printing algorithms depending on the design category. For instance, in the case of a floral design, an algorithm that reproduces the shape and color of the petals is applied. Similarly, in the case of an animal design, an algorithm that emphasizes the animal's characteristics can be applied. This allows for the application of printing algorithms tailored to the design category.

[0050] The reception desk can prioritize suggesting highly relevant designs based on the user's geographical location when receiving design requests. For example, the reception desk can obtain the user's current location from GPS data and suggest highly relevant designs based on that location information. Furthermore, if the user is in a specific region, the reception desk can suggest designs based on the culture and trends of that region. If the user is traveling, it can also suggest designs featuring local specialties or scenery from their travel destination. This enables the suggestion of highly relevant designs based on the user's geographical location.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The reception desk receives an image of the design the user desires. Users can use an interface to upload images such as graphic designs and textile designs. For example, they can upload images by dragging and dropping. Step 2: The generation unit uses a generation AI to analyze the image input by the reception unit and generate a 3D model. The generation unit analyzes the image using image recognition algorithms and feature extraction techniques, and generates a 3D model using techniques such as mesh generation and voxelization. The generation AI uses techniques such as deep learning and GAN (Generative Adversarial Network). Step 3: The printing unit prints the design based on the 3D model generated by the generation unit. The printing unit uses technologies such as inkjet printing and laser printing to print the design accurately onto the nail surface.

[0053] (Example of form 2) The nail art system according to an embodiment of the present invention is a system in which a user inputs an image of their desired design, an AI analyzes the image to generate a 3D model, and a nail printer prints the design based on that 3D model. This nail art system allows users to input an image of their desired design, and by analyzing that image to generate a 3D model, it can realize the user's desired design without being limited by existing designs. It is easy to operate and can be used by individuals as well as in stores such as convenience stores and gyms. Since it can be molded in about 1 minute per finger, it is possible to significantly reduce the time required compared to the 2-3 hours that traditional nail salons take. For example, the user inputs an image of their desired design. At this time, the user only needs to input the starting point and destination. For example, the user inputs "I want to go from my home to the station." This information is input into the generating AI. Next, the generating AI analyzes the input information and creates a video showing how to get from the current location to the destination. The generating AI calculates the optimal route based on map data and generates a video along that route. For example, if the user inputs a route from home to the station, a video along that route will be generated. The generated video starts navigation according to the orientation of the user's smartphone. For example, if a user points their smartphone north, the video will also start navigating in the north direction. This allows the user to receive navigation aligned with their own direction of travel. Furthermore, the video screen moves in sync with the user's walking speed. For example, if the user walks slowly, the video will also progress slowly. This allows the user to receive navigation at their own pace. This mechanism results in a simple structure that is easy for children and the elderly to use, making it appealing to everyone. Users can receive intuitive navigation without complex operations. Also, since the viewpoint of the smartphone is the axis of all directions, there is no chance of getting lost, and walking safety is ensured because the smartphone is held horizontally. For example, if a user is walking with their smartphone held horizontally, the video will also be displayed horizontally, allowing the user to walk safely. This enables the nail art system to print the user's desired design in real time.

[0054] The nail art system according to this embodiment comprises a reception unit, a generation unit, and a printing unit. The reception unit receives an image of the design desired by the user. The image of the design desired by the user includes, but is not limited to, graphic designs and textile designs. The reception unit provides, for example, an interface for uploading an image of the design desired by the user. The reception unit can also provide a tool for selecting an image of the design desired by the user. For example, the reception unit provides an interface that allows the user to upload a design image by drag and drop. The generation unit uses a generation AI to analyze the image input by the reception unit and generate a 3D model. The generation unit analyzes the image using, for example, an image recognition algorithm or a feature extraction technique. The generation unit can also generate a 3D model using techniques such as mesh generation or voxelization. For example, the generation unit extracts features from an image and generates a 3D model based on them. The generation unit can perform image analysis and 3D model generation using a generation AI. The generation AI can use, for example, techniques such as deep learning or GAN (Generative Opposite Network). The printing unit prints designs based on 3D models generated by the generation unit. The printing unit prints designs using technologies such as inkjet printing or laser printing. The printing unit can also print designs onto the surface of nails based on the generated 3D model. For example, the printing unit prints designs based on the scale and resolution of the 3D model. The printing unit can accurately print designs based on the generated 3D model. As a result, the nail art system according to this embodiment can print the user's desired design in real time.

[0055] The generation unit can analyze images using generative AI and generate 3D models. For example, the generation unit analyzes images using generative AI. For example, the generation unit extracts image features using deep learning and generates a 3D model. The generation unit can also analyze images and generate 3D models using GAN (Generative Opposite Network). For example, the generation unit extracts image features and generates a 3D model based on them. The generation unit can perform image analysis and 3D model generation using generative AI. The generative AI can use technologies such as deep learning and GAN (Generative Opposite Network). As a result, the accuracy of image analysis and 3D model generation is improved by using generative AI. Some or all of the above-described processes in the generation unit may be performed using generative AI, or they may not be performed using generative AI. For example, the generation unit can perform image analysis and 3D model generation using a generative AI model that takes an image as input and outputs a 3D model.

[0056] The printing unit can print designs based on the generated 3D model. For example, the printing unit prints designs based on the generated 3D model. For example, the printing unit uses inkjet printing technology to print the 3D model design onto the surface of the nail. The printing unit can also print 3D model designs using laser printing technology. For example, the printing unit prints designs based on the scale and resolution of the 3D model. The printing unit can accurately print designs based on the generated 3D model. This allows for accurate printing of designs based on the generated 3D model. Some or all of the above processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can print designs using an AI model that takes the generated 3D model as input and prints the design.

[0057] The reception desk can estimate the user's emotions and propose designs based on those emotions. For example, the reception desk can use facial recognition technology to estimate the user's emotions. For instance, the reception desk can capture the user's facial expression with a camera and estimate the emotion using a facial recognition algorithm. The reception desk can also estimate the user's emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the reception desk can propose designs based on the estimated user emotions. For example, if the user is relaxed, the reception desk can propose designs with calming colors. If the user is excited, the reception desk can also propose designs with vibrant colors. Furthermore, if the user is stressed, the reception desk can propose simple and visually calming designs. This makes it possible to propose designs that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0058] The reception desk can analyze the user's past design history and propose the most suitable design. For example, the reception desk can use data mining techniques to analyze the user's past design history. For example, the reception desk can analyze the trends of designs the user has chosen in the past and propose similar designs. The reception desk can also propose new designs based on the color schemes the user has preferred in the past. Furthermore, the reception desk can eliminate designs the user has avoided in the past and propose designs that the user will like. This makes it possible to propose the most suitable design based on the user's past design history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past design history data into a generating AI and have the generating AI propose the most suitable design.

[0059] The reception desk can filter designs based on the user's current fashion and events when receiving design requests. For example, the reception desk can refer to a fashion database and an event calendar to filter designs based on the user's current fashion and events. For instance, if the user is attending a specific event, the reception desk can suggest a design suitable for that event. The reception desk can also suggest designs that match the user's current fashion style. Furthermore, the reception desk can suggest appropriate designs based on the season and trends. This enables the reception desk to suggest designs that are suitable for the user's current fashion and events. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's fashion data and event information into a generating AI and have the generating AI perform the filtering.

[0060] The reception desk can estimate the user's emotions and prioritize designs based on those emotions. For example, the reception desk can use facial recognition technology to estimate the user's emotions. For instance, the reception desk can capture the user's facial expression with a camera and estimate the emotion using a facial recognition algorithm. The reception desk can also estimate the user's emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the user's voice to estimate the emotion. Furthermore, the reception desk can prioritize designs based on the estimated user emotions. For example, if the user is relaxed, the reception desk will prioritize displaying calm designs. If the user is excited, the reception desk can also prioritize displaying vibrant designs. Furthermore, if the user is stressed, the reception desk can also prioritize displaying simple designs. This makes it possible to prioritize designs according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes at the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input user image data captured by a camera into a generating AI and have the generating AI perform an estimation of the user's emotions.

[0061] The reception desk can prioritize suggesting highly relevant designs based on the user's geographical location information when a design is submitted. For example, the reception desk can use GPS data to obtain the user's geographical location information. For instance, the reception desk can obtain the user's current location from GPS data and suggest highly relevant designs based on that location information. The reception desk can also obtain the user's geographical location information using location services. For example, if the user is in a specific region, the reception desk can suggest designs based on the culture and trends of that region. Furthermore, if the user is traveling, the reception desk can suggest designs that are themed around local products or scenery of the travel destination. For example, if the user is in a specific city, the reception desk can suggest designs that match the fashion trends of that city. This enables the suggestion of highly relevant designs based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI suggest highly relevant designs.

[0062] The reception desk can suggest relevant designs based on the user's social media activity when a design request is received. For example, the reception desk can use data mining techniques to analyze the user's social media activity. For instance, it can suggest relevant designs based on images and posts the user has shared on social media. It can also analyze the trends of accounts the user follows and suggest relevant designs. Furthermore, it can suggest preferred designs based on posts the user has "liked." This enables the suggestion of relevant designs based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant designs.

[0063] The generation unit can estimate the user's emotions and adjust the 3D model generation method based on those emotions. For example, the generation unit can use facial recognition technology to estimate the user's emotions. For instance, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using a facial recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the generation unit adjusts the 3D model generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a 3D model that makes extensive use of soft curves. If the user is excited, the generation unit can also generate a 3D model that makes extensive use of sharp lines. Furthermore, if the user is stressed, the generation unit can generate a simple and visually calming 3D model. This makes it possible to adjust the 3D model generation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0064] The generation unit can adjust the level of detail of the 3D model based on the complexity of the design during generation. For example, the generation unit uses image analysis techniques to evaluate the complexity of the design. For example, the generation unit analyzes the number of elements and level of detail of the design and adjusts the level of detail of the 3D model based on that. The generation unit can also adjust the level of detail of the 3D model by adjusting the mesh resolution and the number of polygons. For example, in the case of a complex design, the generation unit generates a 3D model that reproduces even the smallest details in great detail. In the case of a simple design, the generation unit can also generate a 3D model that emphasizes the basic shape. Furthermore, in the case of a design of moderate complexity, the generation unit can generate a 3D model with a balanced level of detail. This makes it possible to adjust the level of detail of the 3D model according to the complexity of the design. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for evaluating the complexity of the design into a generation AI and have the generation AI perform the adjustment of the level of detail of the 3D model.

[0065] The generation unit can apply different generation algorithms depending on the design category during generation. For example, the generation unit can use clustering techniques to classify design categories. For instance, the generation unit can perform clustering based on design features and classify categories. The generation unit can also apply different generation algorithms depending on the design category. For example, in the case of a flower design, the generation unit can apply an algorithm that reproduces the shape and color of the petals. In the case of an animal design, the generation unit can also apply an algorithm that emphasizes the characteristics of the animal. Furthermore, in the case of a geometric design, the generation unit can apply an algorithm that accurately reproduces geometric patterns. This makes it possible to apply generation algorithms according to the design category. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for classifying design categories into a generation AI and have the generation AI execute the application of the generation algorithm.

[0066] The generation unit can estimate the user's emotions and adjust the 3D model generation speed based on those emotions. For example, the generation unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the generation unit adjusts the 3D model generation speed based on the estimated user emotions. For example, if the user is in a hurry, the generation unit speeds up the generation to quickly generate the 3D model. If the user is relaxed, the generation unit can generate the 3D model at a normal speed. Furthermore, if the user is stressed, the generation unit can adjust the generation speed to generate the 3D model at an optimal pace. This makes it possible to adjust the 3D model generation speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generative AI may be a text-generating AI (e.g., LLM) or a multimodal generative AI, but is not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0067] The generation unit can determine the priority of 3D models based on the design submission date during generation. For example, the generation unit can use timestamps to evaluate the design submission date. For example, the generation unit can record the design submission date and time with a timestamp and determine the priority of 3D models based on that. The generation unit can also determine the priority of 3D models based on the submission order. For example, the generation unit can prioritize generating designs that were submitted early. The generation unit can also prioritize generating designs with approaching deadlines. Furthermore, the generation unit can generate 3D models in an appropriate order according to the submission date. This makes it possible to prioritize 3D models according to the design submission date. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input design submission date data into a generation AI and have the generation AI perform the determination of 3D model priority.

[0068] The generation unit can adjust the order of 3D models based on the relevance of the designs during generation. For example, the generation unit can use data mining techniques to evaluate the relevance of the designs. For example, the generation unit can prioritize generating highly relevant designs based on the user's past design history. The generation unit can also prioritize generating highly relevant designs based on current trends. Furthermore, the generation unit can prioritize generating highly relevant designs based on the user's preferences. This makes it possible to adjust the order of 3D models according to the relevance of the designs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data for evaluating the relevance of designs into a generation AI and have the generation AI perform the adjustment of the order of the 3D models.

[0069] The printing unit can estimate the user's emotions and adjust the printing method based on those emotions. For example, the printing unit can use facial recognition technology to estimate the user's emotions. For instance, the printing unit can capture the user's facial expressions with a camera and estimate the emotions using a facial recognition algorithm. The printing unit can also estimate the user's emotions using voice analysis technology. For example, the printing unit can analyze the tone and speed of the user's voice to estimate the emotions. Furthermore, the printing unit adjusts the printing method based on the estimated user emotions. For example, if the user is relaxed, the printing unit will print at a slow pace. If the user is in a hurry, the printing unit can print quickly. In addition, if the user is stressed, the printing unit can select a simple and visually calming printing method. This makes it possible to adjust the printing method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0070] The printing unit can adjust the level of detail in the print based on the complexity of the design during printing. For example, the printing unit uses image analysis technology to evaluate the complexity of the design. For example, the printing unit analyzes the number of elements and the level of detail in the design and adjusts the level of detail in the print based on that. The printing unit can also adjust the level of detail in the print by adjusting the resolution and the amount of ink used. For example, in the case of a complex design, the printing unit will print with high detail down to the smallest detail. In the case of a simple design, the printing unit can also print with an emphasis on the basic shape. Furthermore, in the case of a design of moderate complexity, the printing unit can print with a balanced level of detail. This makes it possible to adjust the level of detail in the print according to the complexity of the design. Some or all of the above processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input data for evaluating the complexity of the design into a generating AI and have the generating AI perform the adjustment of the level of detail in the print.

[0071] The printing unit can apply different printing algorithms depending on the design category during printing. For example, the printing unit can use clustering techniques to classify design categories. For instance, the printing unit can perform clustering based on the design features and classify the categories. The printing unit can also apply different printing algorithms depending on the design category. For example, in the case of a flower design, the printing unit can apply an algorithm that reproduces the shape and color of the petals. In the case of an animal design, the printing unit can also apply an algorithm that emphasizes the characteristics of the animal. Furthermore, in the case of a geometric design, the printing unit can apply an algorithm that accurately reproduces the geometric pattern. This makes it possible to apply printing algorithms according to the design category. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input data for classifying design categories into a generating AI and have the generating AI execute the application of printing algorithms.

[0072] The printing unit can estimate the user's emotions and adjust the printing speed based on those emotions. For example, the printing unit can use facial recognition technology to estimate the user's emotions. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. The printing unit can also estimate the user's emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the printing unit adjusts the printing speed based on the estimated user emotions. For example, if the user is in a hurry, the printing unit will increase the printing speed for faster printing. If the user is relaxed, the printing unit can print at a normal speed. Additionally, if the user is stressed, the printing unit can adjust the printing speed to print at an optimal pace. This allows for adjustment of the printing speed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input user image data captured by a camera into a generating AI and have the generating AI perform the estimation of the user's emotions.

[0073] The printing unit can determine the printing priority based on the design submission date. For example, the printing unit can use timestamps to evaluate the design submission date. For instance, the printing unit can record the design submission date and time with a timestamp and determine the printing priority based on that. The printing unit can also determine the printing priority based on the submission order. For example, the printing unit can prioritize printing designs submitted early. The printing unit can also prioritize printing designs with approaching deadlines. Furthermore, the printing unit can print in an appropriate order according to the submission date. This makes it possible to determine the printing priority according to the design submission date. Some or all of the above processing in the printing unit may be performed using AI, for example, or not using AI. For example, the printing unit can input design submission date data into a generating AI and have the generating AI perform the determination of printing priority.

[0074] The printing unit can adjust the printing order based on the relevance of designs during printing. For example, the printing unit can use data mining techniques to evaluate the relevance of designs. For example, the printing unit can prioritize printing highly relevant designs based on the user's past design history. The printing unit can also prioritize printing highly relevant designs based on current trends. Furthermore, the printing unit can prioritize printing highly relevant designs based on the user's preferences. This makes it possible to adjust the printing order according to the relevance of designs. Some or all of the above processing in the printing unit may be performed using AI, for example, or without AI. For example, the printing unit can input data for evaluating the relevance of designs into a generating AI and have the generating AI perform the adjustment of the printing order. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, generation unit, and printing 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 reception device 38 of the smart device 14 and provides an interface for the user to input an image of the desired design. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the image using generation AI to generate a 3D model. The printing unit is implemented by the output device 40 of the smart device 14 and prints the design based on the generated 3D model. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, generation unit, and printing unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input an image of the desired design. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the image using generation AI to generate a 3D model. The printing unit is implemented, for example, by the speaker 240 of the smart glasses 214 and prints the design based on the generated 3D model. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, generation unit, and printing unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input an image of the desired design. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the image using generation AI and generates a 3D model. The printing unit is implemented, for example, by the display 343 of the headset terminal 314 and prints the design based on the generated 3D model. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, generation unit, and printing unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for inputting an image of the design desired by the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the image using generation AI and generates a 3D model. The printing unit is implemented by, for example, the controlled object 443 of the robot 414, which prints the design based on the generated 3D model.

[0075] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0076] The reception desk can analyze the user's past purchase history and suggest the most suitable design. For example, the reception desk can analyze the trends of nail designs the user has purchased in the past and suggest similar designs. It can also suggest new designs based on the user's past preferred color palette. Furthermore, the reception desk can eliminate designs the user has avoided in the past and suggest designs they might like. This enables the system to suggest the most suitable design based on the user's past purchase history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past purchase history data into a generating AI and have the AI ​​generate and suggest the most suitable design.

[0077] The generation unit can adjust the 3D model generation method based on the user's preferred style. For example, the generation unit can obtain the user's preferred style in advance from a questionnaire or selection options and generate a 3D model based on that. Furthermore, the generation unit can apply different generation algorithms depending on the style chosen by the user. For instance, if the user prefers a classic style, the generation unit will generate a 3D model that heavily utilizes classic design elements. If the user prefers a modern style, the generation unit can also generate a 3D model that heavily utilizes modern design elements. This allows for adjustment of the 3D model generation method to suit the user's preferences.

[0078] The printing unit can adjust the print color based on the user's skin tone. For example, the printing unit can capture the user's skin tone with a camera and adjust the print color based on that. Furthermore, the printing unit can fine-tune the color of the user's chosen design to match their skin tone. For instance, if the user has light skin, the printing unit will slightly darken the design's colors. Conversely, if the user has dark skin, the printing unit can slightly lighten the design's colors. This allows for optimal print color adjustment according to the user's skin tone.

[0079] The reception desk can estimate the user's emotions and propose designs based on those emotions. For example, the reception desk can capture the user's facial expression with a camera and estimate their emotions using an expression recognition algorithm. The reception desk can also estimate the user's emotions using voice analysis technology. For example, the reception desk can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the reception desk can propose designs based on the estimated user emotions. For example, if the user is relaxed, it can propose designs with calm colors. If the user is excited, it can propose designs with vibrant colors. This makes it possible to propose designs that match the user's emotions.

[0080] The generation unit can estimate the user's emotions and adjust the 3D model generation method based on those emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an expression recognition algorithm. The generation unit can also estimate the user's emotions using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the generation unit adjusts the 3D model generation method based on the estimated user emotions. For example, if the user is relaxed, it can generate a 3D model that makes extensive use of soft curves. Conversely, if the user is excited, it can generate a 3D model that makes extensive use of sharp lines. This makes it possible to adjust the 3D model generation method according to the user's emotions.

[0081] The printing unit can estimate the user's emotions and adjust the printing method based on those emotions. For example, the printing unit can capture the user's facial expressions with a camera and estimate their emotions using an expression recognition algorithm. The printing unit can also estimate user emotions using voice analysis technology. For example, the printing unit can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the printing unit adjusts the printing method based on the estimated user emotions. For example, if the user is relaxed, it will print at a slow pace. If the user is in a hurry, it can print quickly. This allows for adjustment of the printing method according to the user's emotions.

[0082] The reception desk can estimate the user's emotions and prioritize designs based on those emotions. For example, the reception desk can capture the user's facial expressions with a camera and estimate their emotions using a facial recognition algorithm. The reception desk can also estimate user emotions using voice analysis technology. For example, it can analyze the tone and speed of the user's voice to estimate their emotions. Furthermore, the reception desk prioritizes designs based on the estimated user emotions. For example, if the user is relaxed, it will prioritize displaying calming designs. Conversely, if the user is excited, it can prioritize displaying vibrant designs. This makes it possible to prioritize designs according to the user's emotions.

[0083] The generation unit can adjust the level of detail of the 3D model based on the complexity of the design during generation. For example, the generation unit analyzes the number of elements and level of detail of the design and adjusts the level of detail of the 3D model accordingly. It can also adjust the level of detail of the 3D model by adjusting the mesh resolution and polygon count. For example, in the case of a complex design, it can generate a 3D model that reproduces even the smallest details in great detail. Conversely, in the case of a simple design, it can generate a 3D model that emphasizes the basic shape. This allows for adjustment of the level of detail of the 3D model according to the complexity of the design.

[0084] The printing unit can apply different printing algorithms depending on the design category during printing. For example, the printing unit performs clustering based on the design's characteristics to classify it into categories. Furthermore, the printing unit can apply different printing algorithms depending on the design category. For instance, in the case of a floral design, an algorithm that reproduces the shape and color of the petals is applied. Similarly, in the case of an animal design, an algorithm that emphasizes the animal's characteristics can be applied. This allows for the application of printing algorithms tailored to the design category.

[0085] The reception desk can prioritize suggesting highly relevant designs based on the user's geographical location when receiving design requests. For example, the reception desk can obtain the user's current location from GPS data and suggest highly relevant designs based on that location information. Furthermore, if the user is in a specific region, the reception desk can suggest designs based on the culture and trends of that region. If the user is traveling, it can also suggest designs featuring local specialties or scenery from their travel destination. This enables the suggestion of highly relevant designs based on the user's geographical location.

[0086] The following briefly describes the processing flow for example form 2.

[0087] Step 1: The reception desk receives an image of the design the user desires. Users can use an interface to upload images such as graphic designs and textile designs. For example, they can upload images by dragging and dropping. Step 2: The generation unit uses a generation AI to analyze the image input by the reception unit and generate a 3D model. The generation unit analyzes the image using image recognition algorithms and feature extraction techniques, and generates a 3D model using techniques such as mesh generation and voxelization. The generation AI uses techniques such as deep learning and GAN (Generative Adversarial Network). Step 3: The printing unit prints the design based on the 3D model generated by the generation unit. The printing unit uses technologies such as inkjet printing and laser printing to print the design accurately onto the nail surface.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0092] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0108] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] [Explanation of symbols]

[0160] 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 area where users input images of the design they want, A generation unit analyzes the image input by the reception unit and generates a 3D model, The system includes a printing unit that prints a design based on the 3D model generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generative AI analyzes images and generates 3D models. The system according to feature 1.

3. The aforementioned printing unit is Print the design based on the generated 3D model. The system according to feature 1.

4. The aforementioned reception unit is It estimates the user's emotions and proposes designs based on those emotions. The system according to feature 1.

5. The aforementioned reception unit is We analyze the user's past design history and suggest appropriate designs. The system according to feature 1.

6. The aforementioned reception unit is When submitting designs, filters are applied based on the user's current fashion or events. The system according to feature 1.

7. The aforementioned reception unit is We estimate user emotions and determine design priorities based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When receiving design submissions, the system prioritizes suggesting designs that are highly relevant based on the user's geographical location. The system according to feature 1.

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